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Comparing privacy on Aztec, Canton, Starkware, Tempo, and zkSync
Privacy has become a baseline requirement for L1s and L2s who care about bringing real-world users onchain. Users don't want their activity broadcast to competitors or the general public, but applications operating at scale also need some form of auditability, whether for regulators, compliance requirements, or tax reporting. Selective disclosure resolves that tension: privacy by default, with the ability to prove specific facts when required. What separates these networks is not whether they offer that switch, but who gets to hold it.
Aztec, Canton, Starknet, Tempo, and zkSync all offer some form of privacy with selective disclosure, but under the hood they make fundamentally different architectural decisions about who can see your data and who can turn your privacy off. Those decisions determine whether your privacy stays under your own control or sits behind a switch that someone else operates.
Three questions reveal where these networks actually diverge:
The answers determine whether your privacy off-switch is held by a policy, by an operator's good behavior, or by you alone through a cryptographic proof. As you'll see in this post, there are legitimate reasons to use each one with different tradeoffs. Aztec is the only network, however, where that switch stays in the user's hands, answering all three questions without putting a permissioned set of operators or a standing viewing key in control of your privacy. That gives developers the flexibility to build apps that comply with applicable laws while still keeping full privacy under the user's control.
This article will compare the privacy approaches of Aztec, Canton, Starknet, Tempo, and zkSync to give developers insight into the privacy tradeoffs of each network.
Here’s how each network handles the selective disclosure privacy off-switch, and who has control over your privacy:
Each of these networks offers privacy with selective disclosure, but each rests on a different network design with its own tradeoffs. We have ordered them by who holds your privacy off-switch, starting with designs where a third party controls access to your data and ending with designs where that control stays with you. At the top, the switch sits behind a policy promise and an honest operator, and further down it is replaced by proofs that the user generates and controls.
Canton keeps data private by controlling viewing permissions for the various actors on its network. A transaction splits into per-participant views, so each party receives only the sub-transactions that name it, and the parts it is not entitled to never reach it. The sequencer and mediator move those views without reading them, which is real privacy against those roles.
However, the data is still read in plaintext by the participant nodes that host the relevant parties, and in the common regulated-asset pattern where the issuer is a signatory on its own token, the issuer's node sees every transfer. The harder gap is verification, because no third party can reconstruct the global ledger, so correctness rests on the confirming nodes staying honest and their keys staying safe. In practice the off-switch sits with those nodes rather than with you, since you cannot see when your data is read and cannot stop it.
Tempo is designed for payments and uses validity proofs to verify that each zone is executing correctly, while still giving the zone operator full plaintext visibility into every transaction within that zone. Privacy comes from Tempo Zones, which are parallel execution environments connected to the Tempo mainnet.
By design, the zone operator has visibility into all transactions within the zone, while users see only their own and the public sees only a proof that the zone is valid. Token issuers set compliance controls, allowlists, blocklists, and freezes, enforced across zones. The mainnet checks each zone's validity, so execution is verified, while the operator still reads every transaction in plaintext and holds the off-switch over what is revealed. Your privacy is from the public, not from the operator.
zkSync Prividium adds the verifiability piece that Canton lacks. Every batch produces a validity proof settled to Ethereum, so a compromised operator cannot forge state or mint tokens from nothing without also forging a proof, which it cannot do. The tradeoff is that the operator processes every transaction in plaintext and decides who sees what, which means the off-switch stays with the operator and your privacy is from the outside world rather than from the operator itself.
This tradeoff has legitimate uses in high-trust institutional environments. If Bank of America, JPMorgan, and Wells Fargo are transacting on a shared network, a zone where BofA's infrastructure processes BofA-originated transactions satisfies internal control requirements while still delivering genuine ZK privacy from the other banks and the rest of the world. Where this model breaks down is in lower-trust environments where giving an operator full plaintext access and the switch that comes with it holds back product design possibilities.
Starknet's STRK20 breaks from relying on an operator for privacy. It shields ERC-20 balances and transfers in a privacy pool, and every private transaction carries a zero-knowledge proof generated client-side, so no operator sees your plaintext in order to build it.
Disclosure is where STRK20 diverges from Aztec. To join the Starknet Privacy Pool, you register an encrypted viewing key onchain, and it sits there for the life of your participation. On a regulatory request, a designated auditing entity can decrypt that key and trace your complete transaction history, forwards and backwards. StarkWare calls this ‘not a backdoor’ but a carefully scoped access mechanism, and the safeguard is a policy promise that the auditor decrypts only when required. The privacy is cryptographic, but the off-switch is a standing key that someone else holds and can flip whether or not you are watching.
On Aztec your private state lives as encrypted private data that only you can decrypt. The contract developer can choose what state is public and what is private, and whether your encrypted private data is emitted onchain as a private log or shared off-chain instead.
Your transactions get proven client-side on your own device, so no sequencer or operator sees your unencrypted private data. Those proofs settle to Ethereum, which gives the same integrity anchor marketed by Prividium, with every transaction verified and no forged state, but without a single operator who reads your data. The base protocol decentralizes sequencing, proving, and governance, so there is no operator to choose and trust in the first place.
Disclosure is your choice too: you decide who learns your private data, and whether they learn it in encrypted or decrypted form. To grant discovery without readability, you share an app-specific tagging secret that lets an auditor find your data in encrypted form without being able to decrypt and read it. This is enough to prove things calculated from that data, such as a tax basis or a profit and loss figure. Granting permission to actually read the data works differently. There's no per-contract read key you can hand out, because decryption uses your master viewing key, which would unlock all your data across every contract. So instead of sharing a key, you share the data itself, plus a proof that your plaintext is what encrypts to the on-chain ciphertext.
Aztec has true selective disclosure in that you can selectively share it, and nothing else you don’t need to. This is app specific, meaning that private data discoverability access on one app does not grant access on another. Most importantly, the off-switch stays in your hands, and you never need to trust the network to handle access to any of your private data and activity.
This is not just conceptual: here is a working proof-of-concept of this model on Aztec. PrivPNL takes you from private DEX trades through a tagging-key disclosure to a browser-generated ZK proof of your PnL. The auditor verifies a proof while the prover only has to reveal the amount they owe, and your portfolio stays private.
Canton keeps the switch with the participant nodes that read your data in plaintext, so disclosure rests on those nodes staying honest rather than on anything you control. Tempo similarly gives the off-switch to a zone-based node operator, but allows you to verify the correctness of transactions using validity proofs. Prividium hardens that promise with a proof settled to Ethereum, a real improvement, but the operator still reads every transaction and still decides who sees what. This can work well for large institutions, but small to medium sized enterprises are left with the same privacy as their current banks unless they run their own Prividium nodes. STRK20 moves the switch into a standing viewing key and asks you to trust that a designated auditor reaches for it only when needed. In each of these models the real question is not whether your privacy can be switched off, but who gets to do the switching, and whether you would even know it happened.
Aztec takes the operator and the standing key out of the question entirely. You keep the data, you generate the proof, and you disclose the result, one fact at a time and only when you choose to. The off-switch never leaves your hands, and no operator, auditor, or node can reach it on your behalf. This is one of the benefits of a network that offers fully programmable, privacy-preserving smart contracts that put you in control.
Selective disclosure is how privacy survives contact with a regulator, and the model you pick decides who can open your history when you are not looking. On Aztec, that answer is no one but you.
Dive into the technical details: Try a live demo of selective disclosure on Aztec and read the technical article on how it was built.
Integrate with Aztec: Reach out if you are interested in integrating privacy into your project.
Aztec is built on one idea: smart contracts on Ethereum where developers choose what is public and what is private. That doesn't just mean shielded transactions. It means who acts (identity), what they transact (state), and how they execute (computation) can all be private. Aztec makes end-to-end privacy possible; even the contracts themselves can be private.
But privacy also has to be practical. Aztec integrates private and public execution in the same contract, so apps can seamlessly weave together private and public state. Every account is a smart contract, letting users grant granular, revocable access for selective disclosure, which is useful for compliance, tax reporting, or agent permissions.
Finally, Aztec is a decentralized L2, with 3,500+ sequencers participating in the network today. That permissionlessness is what makes Aztec a credible foundation for a global privacy layer.
In this article we’re going to explore the Aztec stack and how we make programmable privacy a reality. We’ll answer questions like ‘what can you do on Aztec?’, ‘how does it all work?’, and ‘what are the core layers of the system that makes it all possible?’
The four layers of the Aztec stack, all live today on the Alpha network:
Aztec smart contracts are written in Noir, a programming language with Rust-like syntax optimized for writing private programs. Noir is an open-source project developed by Aztec and is now the industry-leading language for writing private apps using zero-knowledge proofs. Let’s dive into what Noir is, and why we use it as the building block for writing smart contracts.
Writing zk programs is extremely difficult without a background in cryptography. When developing Noir, our first goal was to create a highly optimized and easy-to-write zk Domain Specific Language (zkDSL) where developers don’t need to know any of the underlying mathematics. As a result, Noir handles all the cryptographic complexities, and will automatically convert your code into fancy zk circuits.
Noir compiles down to the Abstract Circuit Intermediate Representation (ACIR), an adaptable intermediary language that makes it easy to plug in a variety of popular proving backends. These proving backends, such as Aztec’s Barretenberg proving system, take the compiled zero-knowledge circuits and generate proofs attesting to the validity of the program’s execution, all without revealing any private inputs. From authorization systems that keep a password on the user's device, to complex onchain state channels with recursive proofs to verify offchain state; Noir and a proving backend handle everything from compilation to cryptography for you.
In addition to simplifying the developer experience, we also wanted to make it intuitive to specify which elements you want to keep private and which you want to make public. With Noir, privacy is baked in as a default, so all variables and functions are automatically kept completely private, and executed on the user’s device.
If you want to make any part of your code public, you can do so by simply adding a pub attribute.
Noir on its own is great for writing programs that need to execute stateless functions, such as proving that you reside in a specific country based on passport data, or that you hold a certain number of tokens without revealing how many. Projects are already starting to build with Noir outside of Aztec on Base, Scroll, Starknet, and other chains. However, if you are looking to write privacy-preserving apps that store private state data onchain, it’s helpful to utilize a smart contract library that deliberately handles those complexities for you. That’s exactly what we’ve built at Aztec and what we’ll explore in the next section.
Aztec smart contracts leverage Noir to create apps with onchain private and public state. This section covers how smart contracts work on Aztec and how you deploy and interact with your contracts.
An Aztec smart contract is a set of public and private functions written in Noir, deployed on the Aztec Network. They are written using the Aztec.nr framework which handles all the cryptography under the hood for managing private and public state and interacting with other contracts on the network.
To build useful applications, developers need to be able to incorporate both private and public components into their contracts. For example, an onchain voting contract might want to keep the information of the voter private and prevent someone from voting twice, but publicly display how many votes have been cast, and the outcome.
Because contracts are written in Noir, this functionality is as easy as adding a ‘private’ annotation above your function. The following function will be executed privately on the user device, allowing a user to cast a vote without revealing their address:
The output of this private, local execution will be sent to the network, where the correctness of execution is verified and public function calls are executed. The following function is designed to be executed publicly, adding a vote to the total tally without revealing where it came from.
To seamlessly weave together private and public components that can easily interact with onchain state, Aztec smart contracts utilize the Aztec.nr framework to execute private functions on the user’s device and bundle the proofs for these transactions together with the public functions that will be executed by the Aztec Virtual Machine (AVM).
This framework adds the functionality needed to build onchain, privacy-preserving apps, including defining contracts, accessing private or public context, and interacting with the Aztec Network. Similar to how a vanilla Noir program would compile down into zk circuits, Aztec smart contracts are compiled into zk circuits for private functions or AVM bytecode for public functions, and stored in the Aztec contract tree.
Aztec accounts are also written as smart contracts, implementing what is known as account abstraction. Account abstraction allows application developers to create programmable accounts to dramatically improve user experience, including social recovery, sponsored transactions, and multi-factor authentication. It also makes compliance practical, allowing for granular access controls on accounts.
The Aztec Network is the only decentralized L2 on Ethereum. There are currently over 3,500 sequencers running the alpha network. View the live block explorer for the alpha network.
In order for a network to fully protect users and their data, it must guarantee three levels of privacy:
When you write Aztec smart contracts, everything listed above is taken care of for you. As discussed in the previous two sections, you can easily opt into privacy protections at a granular level by weaving together public and private functions.
To understand how all of these components fit together, let’s examine how a transaction is executed on the Aztec Network.
When a user interacts with your application, private functions are executed client-side on the user’s device: a phone, a personal computer, etc. This happens in a private execution environment (PXE) that is able to create highly-efficient zk proofs even on browsers and mobile devices. Any private state updates are added to the state of the network in an append-only database (UTXO tree). Because the proof is generated client-side, no information can be leaked about the inputs, outputs, accounts, or even the functions that were executed.
On the other hand, public transactions are bundled together with the private client-side proofs and sent off to the Aztec Network, powered by a decentralized network of independent community-run sequencers. Sequencers check the validity of private execution proofs, execute any public functions and update the public state, propose blocks, and publish state updates (“diffs”) to Ethereum L1. Sequencers also coordinate with a decentralized network of provers who compute the final proof for every Aztec “epoch” - defined as a contiguous sequence of 32 L2 blocks - and publish it to Ethereum. Any public functions executed by sequencers update an account-based database similar to Ethereum.
Sequencer and prover roles are fully permissionless. Anyone can spin up the required hardware and join the sequencer set or run provers and start bidding to produce proofs of Aztec epochs.
Aztec delivers on a simple but powerful idea: smart contracts on Ethereum where you choose what's public and what's private across identity, data, and compute. The four layers we've walked through are what make that possible.
Noir gives developers a Rust-like language for writing zero-knowledge programs without a cryptography background, with privacy as the default. Aztec smart contracts build on Noir through the Aztec.nr framework, letting you weave private and public functions into a single contract and use account abstraction to unlock granular access controls for compliance, tax reporting, and selective disclosure. The Aztec Network is the only decentralized L2 on Ethereum, with over 3,500 sequencers powering end-to-end programmable privacy across data, identity, and compute. And it all settles to Ethereum, inheriting L1's economic security.
The result is the first practical platform for privacy on Ethereum, and a credible candidate to become the global settlement layer of the future.
Now it's your turn to build. Head to docs.aztec.network to ship your first privacy-preserving app.
Follow Aztec on X for updates on the current state of the network.
Last week, PSE published an insightful and comprehensive user-research piece on private transfers on Ethereum. They interviewed 38 teams in the space and asked what's broken, what's missing, what builders wish they had. The list reads like a wishlist of features every privacy app on L1 is currently trying to engineer towards. It's the kind of rigorous, builder-grounded research the privacy ecosystem has needed.
We read the list. It's the list we've been building against for years.
Aztec solves all of these problems. Every requested feature already lives on Aztec. The proving system, the private contract language, the decentralized network, the privacy wallet architecture, the key model, the snark-friendliness: all of Aztec was built against this list before it was a list.
What follows is a walkthrough. For each of PSE's top technical findings, here's the feature builders are asking for, and how it works on Aztec today.
Ethereum Problem: Proof generation is too slow on user devices, especially mobile. Elliptic-curve pairing operations are a specific bottleneck. Server-side proving is a censorship and privacy leak vector. Sub-second proving was the stated threshold.
Aztec solution: Proving on Aztec runs locally in the PXE (Private eXecution Environment, pronounced "pixie"), so no data ever leaves the user's device. Chonk, our client-side zk proving system, is ruthlessly optimised for fast recursive proving on low-memory devices like phones, native and in-browser. Years of optimization have already gone in, and we're still finding more. It’s best in class and we haven’t even merged-in GPU acceleration yet!
The slow pairing checks that PSE's interviewees called out as a bottleneck aren’t a problem with Aztec; pairings are simply batched together and deferred away from the user's device, handled by the more powerful network instead, without leaking any information. With such a powerful local prover, there’s little need to outsource proving to an untrusted party.
Ethereum Problem: Verifying a ZK proof on Ethereum is prohibitively expensive. A Groth16 proof for a private transfer costs several hundred thousand L1 gas. A Halo2 (KZG Plonk) proof can cost approximately one million gas
Aztec solution: Aztec amortises L1 verification gas across all transactions in the rollup. At current network throughput, that cost is split across roughly 2,000 users per proof. Later this year, it’s slated to be split across ~20,000. Rollup costs are also partially subsidised by Aztec block rewards.
Net result: hundreds of L1 gas per user instead of millions. Plus cheap L2 gas. The user pays pennies for an Aztec transaction.
Ethereum Problem: Wrapping and unwrapping tokens leaks privacy and breaks composability. Smart contracts can't easily interact with encrypted balances. Private state is isolated; contract state is normally shared.
Aztec solution: Private state is not isolated on Aztec. The private state of one contract can be composed with that of another. This can unlock new privacy-preserving DeFi patterns directly on Aztec.
A single private transaction can call a stack of private functions across multiple contracts, with private inputs, private state transitions, and privacy over which functions were even executed and how many. Observers see that a transaction landed. They do not see what happened inside it. Stew on that for a second: a call stack of nested private functions across contracts written by different developers, each causing state transitions, all completely private.
Aztec also runs public functions, similar to Ethereum, inside the same smart contract, so you can build existing DeFi primitives on Aztec.
For Ethereum DeFi specifically, Aztec has a tidy L1-to-L2 messaging layer. Private balances can be unshielded to interact with L1 protocols and shielded back, without leaking who did the interaction and without leaky public gas payments. And for private DeFi primitives that need genuinely shared private state (state nobody knows the value of, but which anyone can mutate), people have built Aztec contracts that compose conventional Aztec private state with co-snark or FHE sidecars.
Private and public state are peers inside a single Aztec smart contract. Builders mix and match.
Ethereum Problem: Entry and exit points are the dominant privacy leak, not the protocol itself. Depositing and quickly withdrawing makes identity analysis trivial.
Aztec solution: The main fix is to stop crossing the boundary so often. (Or even if you do cross the boundary, Aztec has leakage protections).
Imagine if thousands of private smart contracts lived on the same network and could call each other without leaking which contracts were called, which arguments were passed, or what was returned. Imagine they all shared one global note tree and one global nullifier tree. That's Aztec. Once funds are inside, users don't need to keep crossing the private/public boundary to do useful things: Aztec is its own rich environment for composable, private execution of smart contracts.
Even when a private function does need to call a public function – be it an L1 DeFi contract, or a native public function within Aztec – the developer controls the information they reveal; not the protocol. The call can even be "incognito" to hide msg_sender. A single environment for many private apps to thrive also means re-usable tooling for builders.
Ethereum Problem: Privacy features (per-dapp addresses, private transfers) aren't natively integrated into major wallets. Reliance on dapp-specific UIs damages UX.
Aztec solution: Ethereum wallets weren't built for any of this, and they don't need to be: the chain underneath them has no private state to protect. Aztec wallets are an entirely new category of software.
Aztec wallets are able to manage all these new privacy-centric concepts:
Aztec wallets are in active development, and this is an area where we expect many teams to build different wallets that are customized to various user needs. An early wallet is already baked into the protocol for developers to start using today.
Ethereum Problem: Encrypted tokens and many privacy protocols depend on external networks for encryption, decryption, or relaying. Threshold-decryption committees and TEE hardware vendors are added trust assumptions on top of the chain itself.
Aztec solution: Aztec's private and public execution environments are not reliant on external networks. Aztec is its own decentralised network: ~4,000 validators stake on it, block proposers are randomly selected, a random committee attests, and a decentralised set of provers proves the rollup's execution. Validity is ultimately backed by cryptographic proofs settled on Ethereum.
External networks (co-snark networks, TEEs, MPC or FHE sidecars) become an opt-in choice for the specific case of private shared state. The trust tradeoffs there are something the contract developer signs up for explicitly, not a tax every user pays on every transaction by default.
Ethereum Problem: Keccak is inefficient to prove inside ZK circuits. There is no native support for a ZK-friendly hash like Poseidon.
Aztec solution: Poseidon2 is enshrined across the entire Aztec protocol, for rapid proving of every tx. Every Aztec state tree, the proving system, the innards of the protocol; everywhere. Reading and writing state inside a circuit is as cheap as it gets.
Keccak, SHA, and Blake hashes are still available through optimised Noir libraries when contracts need them for L1 interoperability. The default is ZK-friendly; the L1-friendly hashes are there when you reach for them.
Ethereum Problem: Syncing private state (scanning for incoming notes and events) is a client-side bottleneck. Users wait for scans to complete before seeing their balance. Tachyon-style oblivious sync was cited as a path forward.
Aztec solution: Brute-force syncing of private state is rarely needed. Most real-world use cases involve a sender and recipient who can establish a shared secret offchain first.
From that shared secret, both parties can derive a sequence of random-looking “tags”. Each encrypted note log is prepended with the next tag in the sequence. The recipient already knows the next tag, so they know exactly what to query. Note discovery happens in seconds, not minutes. The scheme slots cleanly into PIR or mixnet approaches for extra privacy on the query itself, and smart contracts that don't trust senders to use the correct tag can just constrain it inside the circuit.
That’s not to say that Aztec requires interactivity between all senders and recipients. For genuinely non-interactive use cases (recipient can't talk to the sender before the transfer), Aztec enables devs to customize both their log emission and their note-discovery logic however they like. (Aztec also has ways to speed up the brute-force scanning approach from "scan the whole chain" to "scan a tiny subset of non-interactive handshake txs"
Ethereum Problem: Shielded pools are fragmented across dapps and chains, reducing the effective privacy set for all users. Each new privacy protocol must bootstrap its own.
Aztec solution: There is one global note tree and one global nullifier tree on Aztec, shared by every smart contract on the network. Every private app contributes to and draws from the same privacy set. No per-app bootstrap. No walled gardens.
Private payments, private swaps, lending, payroll, treasury, identity attestations: all of them land in the same global commitment set, by construction.
Ethereum Problem: Ethereum developer tooling lacks support for private transfers and private state. Standards for private tokens, compliance, and wallet interactions are missing. Many privacy teams are small, with short runway and expensive audits.
Aztec solution: Aztec ships the full toolchain for private contracts: Noir for writing private logic, the Aztec smart contract framework with macros that hide the protocol mess so devs can focus on app logic, the PXE for keys / state / syncing / proof generation, a JS SDK, a local node for testing, a CLI, and a real, live, decentralised L2.
The mental overhead of building a privacy protocol on Aztec collapses to "just write the app logic." Here is an example of a complete private transfer function on Aztec:
#[authorize_once("from", "authwit_nonce")]
#[external("private")]
fn transfer_in_private(from: AztecAddress, to: AztecAddress, amount: u128, authwit_nonce: Field) {
self.storage.balances.at(from).sub(amount).deliver(MessageDelivery.ONCHAIN_CONSTRAINED);
self.storage.balances.at(to).add(amount).deliver(MessageDelivery.ONCHAIN_CONSTRAINED);
}
Look at how simple that is.
A two-line function body.
Two lines.
Aztec takes care of the rest.
Behind those #[...] macros, the framework handles: caller authorisation, note syncing, fetching notes from the user's private db, Merkle membership proofs against the global note tree, safe nullifier creation (without leaking master secrets to the circuit), randomness for new notes, encrypted ciphertext generation, log tagging for fast recipient discovery, and public-input population. The PXE handles key management, private state, and proof generation. The smart contract itself contains its own message-processing logic for log discovery, decryption, and storage on the recipient side.
If you want whitelists, blacklists, association sets, custom tx authorisation, viewing-key hierarchies, temporary view access, selective disclosure to specific counterparties, just import a Noir library. Want something more adventurous than private payments? Same toolchain.
PSE's findings are not ten unrelated bugs. They're the same problem refracted ten ways: privacy retrofitted onto a chain that was not designed for it yields bad tradeoffs.
Aztec was designed against this list before it was a list. One global note tree and one global nullifier tree. Private and public state inside the same contract. Compose calls between private contracts without leaking anything. Fast client-side proving on phones via Chonk. Snark-friendliness everywhere. Rollup-amortised L1 gas costs, fractions of a cent per user. Native account abstraction with private fee paymasters. No painfully slow private state syncing: a tagging-based note discovery scheme that runs in seconds. An entirely new category of wallet that treats privacy as a first-class concern. Simple, high-level smart contract syntax that collapses a basic private token transfer function into two lines.
There were 10 privacy features Ethereum devs wanted, all of them live on Aztec. The infrastructure is in place. Build the thing.
Aztec is the blockchain that solved the privacy problem. Start at docs.aztec.network or read the architecture deep-dive on The Best of Both Worlds: How Aztec Blends Private and Public State.
Alpha is live: a fully feature-complete, privacy-first network. The infrastructure is in place, privacy is native to the protocol, and developers can now build truly private applications.
Nine years ago, we set out to redesign blockchain for privacy. The goal: create a system institutions can adopt while giving users true control of their digital lives. Privacy band-aids are coming to Ethereum (someday), but it’s clear we need privacy now, and there’s an arms race underway to build it. Privacy is complex, it’s not a feature you can bolt-on as an afterthought. It demands a ground-up approach, deep tech stack integration, and complete decentralization.
In November 2025, the Aztec Ignition Chain went live as the first decentralized L2 on Ethereum, it’s the coordination layer that the execution layer sits on top of. The network is not operated by the Aztec Labs or the Aztec Foundation, it’s run by the community, making it the true backbone of Aztec.
With the infrastructure in place and a unanimous community vote, the network enters Alpha.
Alpha is the first Layer 2 with a full execution environment for private smart contracts. All accounts, transactions, and the execution itself can be completely private. Developers can now choose what they want public and what they want to keep private while building with the three privacy pillars we have in place across data, identity, and compute.
These privacy pillars, which can be used individually or combined, break down into three core layers:
Alpha is feature complete–meaning this is the only full-stack solution for adding privacy to your business or application. You build, and Aztec handles the cryptography under the hood.
It’s Composable. Private-preserving contracts are not isolated; they can talk to each other and seamlessly blend both private and public state across contracts. Privacy can be preserved across contract calls for full callstack privacy.
No backdoor access. Aztec is the only decentralized L2, and is launching as a fully decentralized rollup with a Layer 1 escape hatch.
It’s Compliant. Companies are missing out on the benefits of blockchains because transparent chains expose user data, while private networks protect it, but still offer fully customizable controls. Now they can build compliant apps that move value around the world instantly.
Developers can explore our privacy primitives across data, identity, and compute and start building with them using the documentation here. Note that this is an early version of the network with known vulnerabilities, see this post for details. While this is the first iteration of the network, there will be several upgrades that secure and harden the network on our path to Beta. If you’d like to learn more about how you can integrate privacy into your project, reach out here.
To hear directly from our Cofounders, join our live from Cannes Q&A on Tuesday, March 31st at 9:30 am ET. Follow us on X to get the latest updates from the Aztec Network.
On Wednesday 17 March 2026 our team discovered a new vulnerability in the Aztec Network. Following the analysis, the vulnerability has been confirmed as a critical vulnerability in accordance with our vulnerability matrix.
The vulnerability affects the proving system as a whole, and is not mitigated via public re-execution by the committee of validators. Exploitation can lead to severe disruption of the protocol and theft of user funds.
In accordance with our policy, fixes for the network will be packaged and distributed with the “v5” release of the network, currently planned for July 2026.
The actual bug and corresponding patch will not be publicly disclosed until “v5.”
Aztec applications and portals bridging assets from Layer 1s should warn users about the security guarantees of Alpha, in particular, reminding users not to put in funds they are not willing to lose. Portals or applications may add additional security measures or training wheels specific to their application or use case.
We will shortly establish a bug tracker to show the number and severity of bugs known to us in v4. The tracker will be updated as audits and security researchers discover issues. Each new alpha release will get its own tracker. This will allow developers and users to judge for themselves how they are willing to use the network, and we will use the tracker as a primary determinant for whether the network is ready for a "Beta" label.
We have identified a vulnerability in barretenberg allowing inclusion of incorrect proofs in the Aztec Network mempool, and ask all nodes to upgrade to versions v.4.1.2 or later.
We’d like to thank Consensys Diligence & TU Vienna for a recent discovery of a separate vulnerability in barretenberg categorized as medium for the network and critical for Noir:
We have published a fixed version of barretenberg.
We’d also like to thank Plainshift AI for discovery, reproduction, and reporting of one more vulnerability in the Aztec Network and their ongoing work to help secure the network.
Privacy has become a baseline requirement for L1s and L2s who care about bringing real-world users onchain. Users don't want their activity broadcast to competitors or the general public, but applications operating at scale also need some form of auditability, whether for regulators, compliance requirements, or tax reporting. Selective disclosure resolves that tension: privacy by default, with the ability to prove specific facts when required. What separates these networks is not whether they offer that switch, but who gets to hold it.
Aztec, Canton, Starknet, Tempo, and zkSync all offer some form of privacy with selective disclosure, but under the hood they make fundamentally different architectural decisions about who can see your data and who can turn your privacy off. Those decisions determine whether your privacy stays under your own control or sits behind a switch that someone else operates.
Three questions reveal where these networks actually diverge:
The answers determine whether your privacy off-switch is held by a policy, by an operator's good behavior, or by you alone through a cryptographic proof. As you'll see in this post, there are legitimate reasons to use each one with different tradeoffs. Aztec is the only network, however, where that switch stays in the user's hands, answering all three questions without putting a permissioned set of operators or a standing viewing key in control of your privacy. That gives developers the flexibility to build apps that comply with applicable laws while still keeping full privacy under the user's control.
This article will compare the privacy approaches of Aztec, Canton, Starknet, Tempo, and zkSync to give developers insight into the privacy tradeoffs of each network.
Here’s how each network handles the selective disclosure privacy off-switch, and who has control over your privacy:
Each of these networks offers privacy with selective disclosure, but each rests on a different network design with its own tradeoffs. We have ordered them by who holds your privacy off-switch, starting with designs where a third party controls access to your data and ending with designs where that control stays with you. At the top, the switch sits behind a policy promise and an honest operator, and further down it is replaced by proofs that the user generates and controls.
Canton keeps data private by controlling viewing permissions for the various actors on its network. A transaction splits into per-participant views, so each party receives only the sub-transactions that name it, and the parts it is not entitled to never reach it. The sequencer and mediator move those views without reading them, which is real privacy against those roles.
However, the data is still read in plaintext by the participant nodes that host the relevant parties, and in the common regulated-asset pattern where the issuer is a signatory on its own token, the issuer's node sees every transfer. The harder gap is verification, because no third party can reconstruct the global ledger, so correctness rests on the confirming nodes staying honest and their keys staying safe. In practice the off-switch sits with those nodes rather than with you, since you cannot see when your data is read and cannot stop it.
Tempo is designed for payments and uses validity proofs to verify that each zone is executing correctly, while still giving the zone operator full plaintext visibility into every transaction within that zone. Privacy comes from Tempo Zones, which are parallel execution environments connected to the Tempo mainnet.
By design, the zone operator has visibility into all transactions within the zone, while users see only their own and the public sees only a proof that the zone is valid. Token issuers set compliance controls, allowlists, blocklists, and freezes, enforced across zones. The mainnet checks each zone's validity, so execution is verified, while the operator still reads every transaction in plaintext and holds the off-switch over what is revealed. Your privacy is from the public, not from the operator.
zkSync Prividium adds the verifiability piece that Canton lacks. Every batch produces a validity proof settled to Ethereum, so a compromised operator cannot forge state or mint tokens from nothing without also forging a proof, which it cannot do. The tradeoff is that the operator processes every transaction in plaintext and decides who sees what, which means the off-switch stays with the operator and your privacy is from the outside world rather than from the operator itself.
This tradeoff has legitimate uses in high-trust institutional environments. If Bank of America, JPMorgan, and Wells Fargo are transacting on a shared network, a zone where BofA's infrastructure processes BofA-originated transactions satisfies internal control requirements while still delivering genuine ZK privacy from the other banks and the rest of the world. Where this model breaks down is in lower-trust environments where giving an operator full plaintext access and the switch that comes with it holds back product design possibilities.
Starknet's STRK20 breaks from relying on an operator for privacy. It shields ERC-20 balances and transfers in a privacy pool, and every private transaction carries a zero-knowledge proof generated client-side, so no operator sees your plaintext in order to build it.
Disclosure is where STRK20 diverges from Aztec. To join the Starknet Privacy Pool, you register an encrypted viewing key onchain, and it sits there for the life of your participation. On a regulatory request, a designated auditing entity can decrypt that key and trace your complete transaction history, forwards and backwards. StarkWare calls this ‘not a backdoor’ but a carefully scoped access mechanism, and the safeguard is a policy promise that the auditor decrypts only when required. The privacy is cryptographic, but the off-switch is a standing key that someone else holds and can flip whether or not you are watching.
On Aztec your private state lives as encrypted private data that only you can decrypt. The contract developer can choose what state is public and what is private, and whether your encrypted private data is emitted onchain as a private log or shared off-chain instead.
Your transactions get proven client-side on your own device, so no sequencer or operator sees your unencrypted private data. Those proofs settle to Ethereum, which gives the same integrity anchor marketed by Prividium, with every transaction verified and no forged state, but without a single operator who reads your data. The base protocol decentralizes sequencing, proving, and governance, so there is no operator to choose and trust in the first place.
Disclosure is your choice too: you decide who learns your private data, and whether they learn it in encrypted or decrypted form. To grant discovery without readability, you share an app-specific tagging secret that lets an auditor find your data in encrypted form without being able to decrypt and read it. This is enough to prove things calculated from that data, such as a tax basis or a profit and loss figure. Granting permission to actually read the data works differently. There's no per-contract read key you can hand out, because decryption uses your master viewing key, which would unlock all your data across every contract. So instead of sharing a key, you share the data itself, plus a proof that your plaintext is what encrypts to the on-chain ciphertext.
Aztec has true selective disclosure in that you can selectively share it, and nothing else you don’t need to. This is app specific, meaning that private data discoverability access on one app does not grant access on another. Most importantly, the off-switch stays in your hands, and you never need to trust the network to handle access to any of your private data and activity.
This is not just conceptual: here is a working proof-of-concept of this model on Aztec. PrivPNL takes you from private DEX trades through a tagging-key disclosure to a browser-generated ZK proof of your PnL. The auditor verifies a proof while the prover only has to reveal the amount they owe, and your portfolio stays private.
Canton keeps the switch with the participant nodes that read your data in plaintext, so disclosure rests on those nodes staying honest rather than on anything you control. Tempo similarly gives the off-switch to a zone-based node operator, but allows you to verify the correctness of transactions using validity proofs. Prividium hardens that promise with a proof settled to Ethereum, a real improvement, but the operator still reads every transaction and still decides who sees what. This can work well for large institutions, but small to medium sized enterprises are left with the same privacy as their current banks unless they run their own Prividium nodes. STRK20 moves the switch into a standing viewing key and asks you to trust that a designated auditor reaches for it only when needed. In each of these models the real question is not whether your privacy can be switched off, but who gets to do the switching, and whether you would even know it happened.
Aztec takes the operator and the standing key out of the question entirely. You keep the data, you generate the proof, and you disclose the result, one fact at a time and only when you choose to. The off-switch never leaves your hands, and no operator, auditor, or node can reach it on your behalf. This is one of the benefits of a network that offers fully programmable, privacy-preserving smart contracts that put you in control.
Selective disclosure is how privacy survives contact with a regulator, and the model you pick decides who can open your history when you are not looking. On Aztec, that answer is no one but you.
Dive into the technical details: Try a live demo of selective disclosure on Aztec and read the technical article on how it was built.
Integrate with Aztec: Reach out if you are interested in integrating privacy into your project.
Alpha is live: a fully feature-complete, privacy-first network. The infrastructure is in place, privacy is native to the protocol, and developers can now build truly private applications.
Nine years ago, we set out to redesign blockchain for privacy. The goal: create a system institutions can adopt while giving users true control of their digital lives. Privacy band-aids are coming to Ethereum (someday), but it’s clear we need privacy now, and there’s an arms race underway to build it. Privacy is complex, it’s not a feature you can bolt-on as an afterthought. It demands a ground-up approach, deep tech stack integration, and complete decentralization.
In November 2025, the Aztec Ignition Chain went live as the first decentralized L2 on Ethereum, it’s the coordination layer that the execution layer sits on top of. The network is not operated by the Aztec Labs or the Aztec Foundation, it’s run by the community, making it the true backbone of Aztec.
With the infrastructure in place and a unanimous community vote, the network enters Alpha.
Alpha is the first Layer 2 with a full execution environment for private smart contracts. All accounts, transactions, and the execution itself can be completely private. Developers can now choose what they want public and what they want to keep private while building with the three privacy pillars we have in place across data, identity, and compute.
These privacy pillars, which can be used individually or combined, break down into three core layers:
Alpha is feature complete–meaning this is the only full-stack solution for adding privacy to your business or application. You build, and Aztec handles the cryptography under the hood.
It’s Composable. Private-preserving contracts are not isolated; they can talk to each other and seamlessly blend both private and public state across contracts. Privacy can be preserved across contract calls for full callstack privacy.
No backdoor access. Aztec is the only decentralized L2, and is launching as a fully decentralized rollup with a Layer 1 escape hatch.
It’s Compliant. Companies are missing out on the benefits of blockchains because transparent chains expose user data, while private networks protect it, but still offer fully customizable controls. Now they can build compliant apps that move value around the world instantly.
Developers can explore our privacy primitives across data, identity, and compute and start building with them using the documentation here. Note that this is an early version of the network with known vulnerabilities, see this post for details. While this is the first iteration of the network, there will be several upgrades that secure and harden the network on our path to Beta. If you’d like to learn more about how you can integrate privacy into your project, reach out here.
To hear directly from our Cofounders, join our live from Cannes Q&A on Tuesday, March 31st at 9:30 am ET. Follow us on X to get the latest updates from the Aztec Network.
The Ignition Chain launched late last year, as the first fully decentralized L2 on Ethereum– a huge milestone for decentralized networks. The team has reinvented what true programmable privacy means, building the execution model from the ground up— combining the programmability of Ethereum with the privacy of Zcash in a single execution environment.
Since then, the network has been running with zero downtime with 3,500+ sequencers and 50+ provers across five continents. With the infrastructure now in place, the network is fully in the hands of the community, and the culmination of the past 8 years of work is now converging.
Major upgrades have landed across four tracks: the execution layer, the proving system, the programming language, Noir, and the decentralization stack. Together, these milestones deliver on Aztec’s original promise, a system where developers can write fully programmable smart contracts with customizable privacy.
The infrastructure is in place. The code is ready. And we’re ready to ship.
The execution layer delivers on Aztec's core promise: fully programmable, privacy-preserving smart contracts on Ethereum.
A complete dual state model is now in place–with both private and public state. Private functions execute client-side in the Private Execution Environment (PXE), running directly in the user's browser and generating zero-knowledge proofs locally, so that private data never leaves the original device. Public functions execute on the Aztec Virtual Machine (AVM) on the network side.
Aztec.js is now live, giving developers a full SDK for managing accounts and interacting with contracts. Native account abstraction has been implemented, meaning every account is a smart contract with customizable authentication rules. Note discovery has been solved through a tagging mechanism, allowing recipients to efficiently query for relevant notes without downloading and decrypting everything on the network.
Contract standards are underway, with the Wonderland team delivering AIP-20 for tokens and AIP-721 for NFTs, along with escrow contracts and logic libraries, providing the production-ready building blocks for the Alpha Network.
The proving system is what makes Aztec's privacy guarantees real, and it has deep roots.
In 2019, Aztec's cofounder Zac Williamson and Chief Scientist Ariel Gabizon introduced PLONK, which became one of the most widely used proving systems in zero-knowledge cryptography. Since then, Aztec's cryptographic backend, Barretenberg, has evolved through multiple generations, each facilitating faster, lighter, and more efficient proving than the last. The latest innovation, CHONK (Client-side Highly Optimized ploNK), is purpose-built for proving on phones and browsers and is what powers proof generation for the Alpha Network.
CHONK is a major leap forward for the user experience, dramatically reducing the memory and time required to generate proofs on consumer devices. It leverages best-in-class circuit primitives, a HyperNova-style folding scheme for efficiently processing chains of private function calls, and Goblin, a hyper-efficient purpose-built recursion acceleration scheme. The result is that private transactions can be proven on the devices people actually use, not just powerful servers.
This matters because privacy on Aztec means proofs are generated on the user's own device, keeping private data private. If proving is too slow or too resource-intensive, privacy becomes impractical. CHONK makes it practical.
Decentralization is what makes Aztec's privacy guarantees credible. Without it, a central operator could censor transactions, introduce backdoors, or compromise user privacy at will.
Aztec addressed this by hardcoding decentralized sequencing, proving, and governance directly into the base protocol. The Ignition Chain has proven the stability of this consensus layer, maintaining zero downtime with over 3,500 sequencers and 50+ provers running across five continents. Aztec Labs and the Aztec Foundation run no sequencers and do not participate in governance.
Noir 1.0 is nearing completion, bringing a stable, production-grade language within reach. Aztec's own protocol circuits have been entirely rewritten in Noir, meaning the language is already battle-tested at the deepest layer of the stack.
Internal and external audits of the compiler and toolchain are progressing in parallel, and security tooling including fuzzers and bytecode parsers is nearly finished. A stable, audited language means application teams can build on Alpha with confidence that the foundation beneath them won't shift.
The code for Alpha Network, a functionally complete and raw version of the network, is ready.
The Alpha Network brings fully programmable, privacy-preserving smart contracts to Ethereum for the first time. It's the culmination of years of parallel work across the four tracks in the Aztec Roadmap. Together, they enable efficient client-side proofs that power customizable smart contracts, letting users choose exactly what stays private and what goes public.
No other project in the space is close to shipping this.
The code is written. The network is running. All the pieces are in place. The governance proposal is now live on the forum and open for discussion. Read through it, ask questions, poke holes, and help shape the path forward.
Once the community is aligned, the proposal moves to a vote. This is how a decentralized network upgrades. Not by a team pushing a button, but by the people running it.
Programmable privacy will unlock a renaissance in onchain adoption. Real-world applications are coming and institutions are paying attention. Alpha represents the culmination of eight years of intense work to deliver privacy on Ethereum.
Now it needs to be battle-tested in the wild.
View the updated product roadmap here and join us on Thursday, March 5th, at 3 pm UTC on X to hear more about the most recent updates to our product roadmap.
Privacy has emerged as a major driver for the crypto industry in 2025. We’ve seen the explosion of Zcash, the Ethereum Foundation’s refocusing of PSE, and the launch of Aztec’s testnet with over 24,000 validators powering the network. Many apps have also emerged to bring private transactions to Ethereum and Solana in various ways, and exciting technologies like ZKPassport that privately bring identity on-chain using Noir have become some of the most talked about developments for ushering in the next big movements to the space.
Underpinning all of these developments is the emerging consensus that without privacy, blockchains will struggle to gain real-world adoption.
Without privacy, institutions can’t bring assets on-chain in a compliant way or conduct complex swaps and trades without revealing their strategies. Without privacy, DeFi remains dominated and controlled by advanced traders who can see all upcoming transactions and manipulate the market. Without privacy, regular people will not want to move their lives on-chain for the entire world to see every detail about their every move.
While there's been lots of talk about privacy, few can define it. In this piece we’ll outline the three pillars of privacy and gives you a framework for evaluating the privacy claims of any project.
True privacy rests on three essential pillars: transaction privacy, identity privacy, and computational privacy. It is only when we have all three pillars that we see the emergence of a private world computer.
Transaction privacy means that both inputs and outputs are not viewable by anyone other than the intended participants. Inputs include any asset, value, message, or function calldata that is being sent. Outputs include any state changes or transaction effects, or any transaction metadata caused by the transaction. Transaction privacy is often primarily achieved using a UTXO model (like Zcash or Aztec’s private state tree). If a project has only the option for this pillar, it can be said to be confidential, but not private.
Identity privacy means that the identities of those involved are not viewable by anyone other than the intended participants. This includes addresses or accounts and any information about the identity of the participants, such as tx.origin, msg.sender, or linking one’s private account to public accounts. Identity privacy can be achieved in several ways, including client-side proof generation that keeps all user info on the users’ devices. If a project has only the option for this pillar, it can be said to be anonymous, but not private.
Computation privacy means that any activity that happens is not viewable by anyone other than the intended participants. This includes the contract code itself, function execution, contract address, and full callstack privacy. Additionally, any metadata generated by the transaction is able to be appropriately obfuscated (such as transaction effects, events are appropriately padded, inclusion block number are in appropriate sets). Callstack privacy includes which contracts you call, what functions in those contracts you’ve called, what the results of those functions were, any subsequent functions that will be called after, and what the inputs to the function were. A project must have the option for this pillar to do anything privately other than basic transactions.
Bitcoin ushered in a new paradigm of digital money. As a permissionless, peer-to-peer currency and store of value, it changed the way value could be sent around the world and who could participate. Ethereum expanded this vision to bring us the world computer, a decentralized, general-purpose blockchain with programmable smart contracts.
Given the limitations of running a transparent blockchain that exposes all user activity, accounts, and assets, it was clear that adding the option to preserve privacy would unlock many benefits (and more closely resemble real cash). But this was a very challenging problem. Zcash was one of the first to extend Bitcoin’s functionality with optional privacy, unlocking a new privacy-preserving UTXO model for transacting privately. As we’ll see below, many of the current privacy-focused projects are working on similar kinds of private digital money for Ethereum or other chains.
Now, Aztec is bringing us the final missing piece: a private world computer.
A private world computer is fully decentralized, programmable, and permissionless like Ethereum and has optional privacy at every level. In other words, Aztec is extending all the functionality of Ethereum with optional transaction, identity, and computational privacy. This is the only approach that enables fully compliant, decentralized applications to be built that preserve user privacy, a new design space that we see as ushering in the next Renaissance for the space.
Private digital money emerges when you have the first two privacy pillars covered - transactions and identity - but you don’t have the third - computation. Almost all projects today that claim some level of privacy are working on private digital money. This includes everything from privacy pools on Ethereum and L2s to newly emerging payment L1s like Tempo and Arc that are developing various degrees of transaction privacy
When it comes to digital money, privacy exists on a spectrum. If your identity is hidden but your transactions are visible, that's what we call anonymous. If your transactions are hidden but your identity is known, that's confidential. And when both your identity and transactions are protected, that's true privacy. Projects are working on many different approaches to implement this, from PSE to Payy using Noir, the zkDSL built to make it intuitive to build zk applications using familiar Rust-like syntax.
Private digital money is designed to make payments private, but any interaction with more complex smart contracts than a straightforward payment transaction is fully exposed.
What if we also want to build decentralized private apps using smart contracts (usually multiple that talk to each other)? For this, you need all three privacy pillars: transaction, identity, and compute.
If you have these three pillars covered and you have decentralization, you have built a private world computer. Without decentralization, you are vulnerable to censorship, privileged backdoors and inevitable centralized control that can compromise privacy guarantees.
What exactly is a private world computer? A private world computer extends all the functionality of Ethereum with optional privacy at every level, so developers can easily control which aspects they want public or private and users can selectively disclose information. With Aztec, developers can build apps with optional transaction, identity, and compute privacy on a fully decentralized network. Below, we’ll break down the main components of a private world computer.
A private world computer is powered by private smart contracts. Private smart contracts have fully optional privacy and also enable seamless public and private function interaction.
Private smart contracts simply extend the functionality of regular smart contracts with added privacy.
As a developer, you can easily designate which functions you want to keep private and which you want to make public. For example, a voting app might allow users to privately cast votes and publicly display the result. Private smart contracts can also interact privately with other smart contracts, without needing to make it public which contracts have interacted.
Transaction: Aztec supports the optionality for fully private inputs, including messages, state, and function calldata. Private state is updated via a private UTXO state tree.
Identity: Using client-side proofs and function execution, Aztec can optionally keep all user info private, including tx.origin and msg.sender for transactions.
Computation: The contract code itself, function execution, and call stack can all be kept private. This includes which contracts you call, what functions in those contracts you’ve called, what the results of those functions were, and what the inputs to the function were.
A decentralized network must be made up of a permissionless network of operators who run the network and decide on upgrades. Aztec is run by a decentralized network of node operators who propose and attest to transactions. Rollup proofs on Aztec are also run by a decentralized prover network that can permissionlessly submit proofs and participate in block rewards. Finally, the Aztec network is governed by the sequencers, who propose, signal, vote, and execute network upgrades.
A private world computer enables the creation of DeFi applications where accounts, transactions, order books, and swaps remain private. Users can protect their trading strategies and positions from public view, preventing front-running and maintaining competitive advantages. Additionally, users can bridge privately into cross-chain DeFi applications, allowing them to participate in DeFi across multiple blockchains while keeping their identity private despite being on an existing transparent blockchain.
This technology makes it possible to bring institutional trading activity on-chain while maintaining the privacy that traditional finance requires. Institutions can privately trade with other institutions globally, without having to touch public markets, enjoying the benefits of blockchain technology such as fast settlement and reduced counterparty risk, without exposing their trading intentions or volumes to the broader market.
Organizations can bring client accounts and assets on-chain while maintaining full compliance. This infrastructure protects on-chain asset trading and settlement strategies, ensuring that sophisticated financial operations remain private. A private world computer also supports private stablecoin issuance and redemption, allowing financial institutions to manage digital currency operations without revealing sensitive business information.
Users have granular control over their privacy settings, allowing them to fine-tune privacy levels for their on-chain identity according to their specific needs. The system enables selective disclosure of on-chain activity, meaning users can choose to reveal certain transactions or holdings to regulators, auditors, or business partners while keeping other information private, meeting compliance requirements.
The shift from transparent blockchains to privacy-preserving infrastructure is the foundation for bringing the next billion users on-chain. Whether you're a developer building the future of private DeFi, an institution exploring compliant on-chain solutions, or simply someone who believes privacy is a fundamental right, now is the time to get involved.
Follow Aztec on X to stay updated on the latest developments in private smart contracts and decentralized privacy technology. Ready to contribute to the network? Run a node and help power the private world computer.
The next Renaissance is here, and it’s being powered by the private world computer.
When Aztec mainnet launches, it will be the first fully private and decentralized L2 on Ethereum. Getting here was a long road: when Aztec started eight years ago, the initial plan was to build an onchain financial service called CreditMint for issuing corporate debt to mid-market enterprises – obviously a distant use case from how we understand Aztec today. When co-founders Zac Williamson, Joe Andrews, Tom Pocock, and Arnaud Schenk, got started, the world of zero-knowledge proving systems and applications weren’t even in their infancy: there was no PLONK, no Noir, no programmable privacy, and it wasn’t clear that demand for onchain privacy was even strong enough to necessitate a new blockchain network. The founders’ initial explorations through CreditMint led to what we know as Aztec today.
While putting corporate debt onchain might seem unglamorous (or just limited compared with how we now understand Aztec’s capabilities), it was useful, wildly popular, and necessary for the founding team to realized that no serious institution wanted to touch the blockchain without the same privacy assurances that they were accustomed to in the corporate world. Traditional finance is built around trusted intermediaries and middlemen, which of course introduces friction and bottlenecks progress – but offers more privacy assurances than what you see on public blockchains like Ethereum.
This takeaway led to a bigger understanding: the number of people (not just the number of institutions) who wanted to use the blockchain was limited by a lack of programmable privacy. Aztec was born out of the recognition that everyone – not only corporations – could use permissionless, onchain systems for private transactions, and this could become the default for all online payments. In the words of the CEO, Zac Williamson:
“If you had programmable digital money that had privacy guarantees around it, you could use that to create extremely fast permissionless payment channels for payments on the internet.”
Equipped with this understanding, Zac and Joe began to specialize. Zac, whose background is in particle physics, went deep on cryptography research and began exploring protocols that could be used to enable onchain privacy. Meanwhile, Joe worked on how to get user adoption for privacy tech, while Arnaud focused on getting the initial CreditMint platform live and recruiting early members of the team. In 2018, Aztec published a proof-of-concept transaction demonstrating the creation and transfer of private assets on Ethereum – using an early cryptographic protocol that predated modern proving schemes like PLONK. It was a limited example, with just DAI as the test-case (and it could only facilitate private assets, not private identities), but it garnered a lot of early interest from members of the Ethereum community.
The 2018 version of the Aztec Protocol had three key limitations: it wasn’t programmable, it only supported private data (rather than private data and user-level privacy), and it was expensive, from both a computation and gas perspective. The underlying proving scheme was, in the words of Zac, a “Frankenstein cryptography protocol using older primitives than zk-SNARKs.” These limitations motivated the development of PLONK in 2019, a SNARK-based proving system that is computationally inexpensive, and only requires one universal trusted setup.
A single universal trusted setup is desirable because it allows developers to utilize a common reference string for all of the programs they might want to instantiate in a circuit; the alternative is a much more cumbersome process of conducting a trusted setup ceremony for each cryptographic circuit. In other words, PLONK enabled programmable privacy for future versions of Aztec.
PLONK was a big breakthrough, not just for Aztec, but for the wider blockchain community. Today, PLONK has been implemented and extended by teams like zkSync, Polygon, Mina, and more. There is even an entire category of proving systems called PLONKish that all derive from the original 2019 paper. For Aztec specifically, PLONK was also instrumental in paving the way for zk.money and Aztec Connect, a private payment network and private DeFi rollup, which launched in 2021 and 2022 respectively.
The product needs of Aztec motivated the development of a modern-day proving system. PLONK proofs are computationally cheap to generate, leading not only to lower transaction costs and programmability for developers, but big steps forward for privacy and decentralization. PLONK made it simpler to generate client-side proofs on inexpensive hardware. In the words of Joe, “PLONK [was] developed to keep the middleman away.”
Between 2021 and 2023, the Aztec team operated zk.money and Aztec Connect. The products were not only vital in illustrating that there was a demand for onchain privacy solutions, but in demonstrating that it was possible to build performant and private networks leveraging PLONK. Joe remarked that they “wanted to test that we could build a viable payments network, where the user experience was on par with a public transaction. Privacy needed to be in the background.”
Aztec’s early products indicated that there was significant demand for private onchain payments and DeFi – at peak, the rollups had over $20 million in TVL. Both products fit into the vision Zac had to “make the blockchain real.” In his team’s eyes, blockchains are held back from mainstream adoption because you can’t bring consequential, real-world assets onchain without privacy.
Despite the demand for these networks, the team made the decision to sunset both zk.money and Aztec Connect after recognizing that they could not fully decentralize the networks without massive architectural changes. Zac and Joe don’t believe in “Progressive Decentralization” – the network needs to have no centralized operators from day one. And it wasn’t just the sequencer of these early Aztec products that were centralized – the team also recognized that it would have been impossible for other developers to write programs on Aztec that could compose with each other, because all programs operated on shared state. In 2023, zk.money and Aztec Connect were officially shut down.
In tandem, the team also began developing Noir (an original brainchild of Kevaundray Wedderbaum). Noir is a Rust-like programming language for writing zero-knowledge circuits that makes privacy technology accessible to mainstream developers. While Noir began as a way to make it easier for developers to write private programs without needing to know cryptography, the team soon realized that the demand for privacy didn’t just apply to applications on the Aztec stack, and that Noir could be a general-purpose DSL for any kind of application that needs to leverage privacy. In the same way that bringing consequential assets and activity onchain “makes the blockchain real,” bringing zero-knowledge technology to any application – onchain or offchain – makes privacy real. The team continued working on Noir, and it has developed into its own product stack today.
Aztec from 2017 to 2024 can be seen as a methodical journey toward building a fully private, programmable, and decentralized blockchain network. The earliest attempt at Aztec as a protocol introduced asset-level privacy, without addressing user-level privacy, or significant programmability. PLONK paved the way for user-level privacy and programmability, which yielded zk.money and Aztec Connect. Noir extended programmability even further, making it easy for developers to build applications in zero-knowledge. But zk.money and Aztec Connect were incomplete without a viable path to decentralization. So, the team decided to build a new network from scratch. Extending on their learnings from past networks, the foundations and findings from continuous R&D efforts of PLONK, and the growing developer community around Noir, they set the stage for Aztec mainnet.
The fact of the matter is that creating a network that is fully private and decentralized is hard. To have privacy, all data must be shielded cheaply inside of a SNARK. If you want to really embrace the idea of “making the blockchain real” then you should also be able to leverage outside authentication and identity solutions, like Apple ID – and you need to be able to put those technologies inside of a SNARK as well. The number of statements that need to be represented as provable circuits is massive. Then, all of these capabilities need to run inside of a network that is decentralized. The combination of mathematical, technological, and networking problems makes this very difficult to achieve
The technical architecture of Aztec reflects the learnings of the Aztec team. Zac describes Aztec mainnet as a “Russian nesting doll” of products that all add up to a private and decentralized network. Aztec today consists of:
At the network level, there will be many participants in the decentralization efforts of Aztec: provers, sequencers, and node operators. Joe views the infrastructure-level decentralization as a crucial first stage of Aztec’s mainnet launch.
As Aztec goes live, the vision extends beyond private transactions to enabling entirely new categories of applications. The team envisions use cases ranging from consumer lending based on private credit scores to games leveraging information asymmetry, to social applications that preserve user privacy. The next phase will focus on building a robust ecosystem of developers and the next generation of applications on Ethereum using Noir, the universal language of privacy.
Aztec mainnet marks the emergence of applications that weren't possible before – applications that combine the transparency and programmability of blockchain with the privacy necessary for real-world adoption.
Many thanks to Remi Gai, Hannes Huitula, Giacomo Corrias, Avishay Yanai, Santiago Palladino, ais, ji xueqian, Brecht Devos, Maciej Kalka, Chris Bender, Alex, Lukas Helminger, Dominik Schmid, 0xCrayon, Zac Williamson for inputs, discussions, and reviews.
Contents
Prerequisites:
Buzzwords are dangerous. They amuse and fascinate as cutting-edge, innovative, mesmerizing markers of new ideas and emerging mindsets. Even better if they are abbreviations, insider shorthand we can use to make ourselves look smarter and more progressive:
Using buzzwords can obfuscate the real scope and technical possibilities of technology. Furthermore, buzzwords might act as a gatekeeper making simple things look complex, or on the contrary, making complex things look simple (according to the Dunning-Kruger effect).
In this article, we will briefly review several suggested privacy-related abbreviations, their strong points, and their constraints. And after that, we’ll think about whether someone will benefit from combining them or not. We’ll look at different configurations and combinations.
Disclaimer: It’s not fair to compare the technologies we’re discussing since it won’t be an apples-to-apples comparison. The goal is to briefly describe each of them, highlighting their strong and weak points. Understanding this, we will be able to make some suggestions about combining these technologies in a meaningful way.
POV: a new dev enters the space.
Client-side ZKP is a specific category of zero-knowledge proofs (started in 1989). The exploration of general ZKPs in great depth is out-of-scope for this piece. If you're curious to learn about it, check this article.
Essentially, zero-knowledge protocol allows one party (prover) to prove to another party (verifier) that some given statement is true, while avoiding conveying any information beyond the mere fact of that statement's truth.
Client-side ZKPs enable generation of the proof on a user's device for the sake of privacy. A user makes some arbitrary computations and generates proof that whatever they computed was computed correctly. Then, this proof can be verified and utilized by external parties.
One of the most widely known use cases of the client-side ZKPs is a privacy preserving L2 on Ethereum where, thanks to client-side data processing, some functions and values in a smart-contract can be executed privately, while the rest are executed publicly. In this case, the client-side ZKP is generated by the user executing the transaction, then verified by the network sequencer.
However, client-side proof generation is not limited to Ethereum L2s, nor to blockchain at all. Whenever there are two or more parties who want to compute something privately and then verify each other’s computation and utilize their results for some public protocols, client-side ZKPs will be a good fit.
Check this article for more details on how client-side ZKPs work.
The main concern today about on-chain privacy by means of client-side proof generation is the lack of a private shared state. Potentially, it can be mitigated with an MPC committee (which we will cover in later sections).
Speaking of limitations of client-side proving, one should consider:
What can we do with client-side ZKPs today:
Whom to follow for client-side ZKPs updates: Aztec Labs, Miden, Aleo.
Disclaimer: in this section, we discuss general-purpose MPC (i.e. allowing computations on arbitrary functions). There are also a bunch of specialized MPC protocols optimized for various use cases (i.e. designing customized functions) but those are out-of-scope for this article.
MPC enables a set of parties to interact and compute a joint function of their private inputs while revealing nothing but the output: f(input_1, input_2, …, input_n) → output.
For example, parties can be servers that hold a distributed database system and the function can be the database update. Or parties can be several people jointly managing a private key from an Ethereum account and the function can be a transaction signing mechanism.
One issue of concern with MPCs is that one or more parties participating in the protocol can be malicious. They can try to:
Hence in the context of MPC security, one wants to ensure that:
To think about MPC security in an exhaustive way, we should consider three perspectives:
Rather than requiring all parties in the computation to remain honest, MPC tolerates different levels of corruption depending on the underlying assumptions. Some models remain secure if less than 1/3 of parties are corrupt, some if less than 1/2 are corrupt, and some even have security guarantees even in the case that more than half of the parties are corrupt. For details, formal definition, and proof of MPC protocol security, check this paper.
There are three main corruption strategies:
Each of these assumptions will assume a different security model.
Two definitions of malicious behavior are:
When it comes to the definition of privacy, MPC guarantees that the computation process itself doesn’t reveal any information. However, it doesn’t guarantee that the output won’t reveal any information. For an extreme example, consider two people computing the average of their salaries. While it’s true that nothing but the average will be output, when each participant knows their own salary amount and the average of both salaries, they can derive the exact salary of the other person.
That is to say, while the core “value proposition” of MPC seems to be very attractive for a wide range of real world use cases, a whole bunch of nuances should be taken into account before it will actually provide a high enough security level. (It's important to clarify the problem statement and decide whether it is the right tool for this particular task.)
What can be done with MPC protocols today:
When we think about MPC performance, we should consider the following parameters: number of participating parties, witness size of each party, and function complexity.
When it comes to using MPC in blockchain context, it’s important to consider message complexity, computational complexity, and such properties as public verifiability and abort identifiability (i.e. if a malicious party causes the protocol to prematurely halt, then they can be detected). For message distribution, the protocol relies either on P2P channels between each two parties (requires a large bandwidth) or broadcasting. Another concern arises around the permissionless nature of blockchain since MPC protocols often operate over permissioned sets of nodes.
Taking into account all that, it’s clear that MPC is a very nuanced technology on its own. And it becomes even more nuanced when combined with other technologies. Adding MPC to a specific blockchain protocol often requires designing a custom MPC protocol that will fit. And that design process often requires a room full of MPC PhDs who can not only design but also prove its security.
Whom to follow for MPC updates: dWallet Labs, TACEO, Fireblocks, Cursive, PSE, Fairblock, Soda Labs, Silence Laboratories, Nillion.
TEE stands for Trusted Execution Environment. TEE is an area on the main processor of a device that is separated from the system's main operating system (OS). It ensures data is stored, processed, and protected in a separate environment. One of the most widely known units of TEE (and one we often mention when discussing blockchain) is Software Guard Extensions (SGX) made by Intel.
SGX can be considered a type of private execution. For example, if a smart contract is run inside SGX, it’s executed privately.
SGX creates a non-addressable memory region of code and data (separated from RAM), and encrypts both at a hardware level.
How SGX works:
It’s worth noting that there is a key pair: a secret key and a public key. The secret key is generated inside of the enclave and never leaves it. The public key is available to anyone: Users can encrypt a message using a public key so only the enclave can decrypt it.
An SGX feature often utilized in the blockchain context is attestations. Attestation is the process of demonstrating that a software executable has been properly instantiated on a platform. Remote Attestation allows a remote party to be confident that the intended software is securely running within an enclave on a fully patched, Intel SGX-enabled platform.
Core SGX concerns:
Speaking of SGX cost, the proof generation cost can be considered free of charge. Though if one wants to use remote attestations, the initial one-time cost (once per SGX prover) for it is in the order of 1M gas (to make sure the code in SGX is running in the expected way).
Onchain verification cost equals to verifying an ECDSA signature (~5k gas while for ZK signature verification will cost ~300k gas).
When it comes to execution time, there is effectively no overhead. For example, for proving a zk-rollup block, it will be around 100ms.
Where SGX is utilized in blockchain today:
Whom to follow for TEE updates: Secret Network, Flashbots, Andrew Miller, Oasis, Phala, Marlin, Automata, TEN.
FHE enables encrypted data processing (i.e. computation on encrypted data).
The idea of FHE was proposed in 1978 by Rivest, Adleman, and Dertouzos. “Fully” means that both addition and multiplication can be performed on encrypted data. Let m be some plain text and E(m) be an encrypted text (ciphertext). Then additive homomorphism is E(m_1 + m_2) = E(m_1) + E(m_2) and multiplicative homomorphism is E(m_1 * m_2) = E(m_1) * E(m_2).
Additive Homomorphic Encryption was used for a while, but Multiplicative Homomorphic Encryption was still an issue. In 2009, Craig Gentry came up with the idea to use ideal lattices to tackle this problem. That made it possible to do both addition and multiplication, although it also made growing noise an issue.
How FHE works:
Plain text is encoded into ciphertext. Ciphertext consists of encrypted data and some noise.
That means when computations are done on ciphertext, they are done not purely on data but on data together with added noise. With each performed operation, the noise increases. After several operations, it starts overflowing on the bits of actual data, which might lead to incorrect results.
A number of tricks were proposed later on to handle the noise and make the FHE work more reliably. One of the most well-known tricks was bootstrapping, a special operation that reset the noise to its nominal level. However, bootstrapping is slow and costly (both in terms of memory consumption and computational cost).
Researchers rolled out even more workarounds to make bootstrapping efficient and took FHE several more steps forward. Further details are out-of-scope for this article, but if you’re interested in FHE history, check out this talk by mathematician Zvika Brakerski.
Core FHE concerns:
Compared to computations on plain text, the best per-operation overhead available today is polylogarithmic [GHS12b] where if n is the input size, by polylogarithmic we mean O(log^k(n)), k is a constant. For communication overhead, it’s reasonable if doing batching and unbatching of a number of ciphertexts but not reasonable otherwise.
For evaluation keys, key size is huge (larger than ciphertexts that are large as well). The evaluation key size is around 160,000,000 bits. Furthermore, one needs to permanently compute on these keys. Whenever homomorphic evaluation is done, you’ll need to access the evaluation key, bring it into the CPU (a regular data bus in a regular processor will be unable to bring it), and make computations on it.
If you want to do something beyond addition and multiplication—a branch operation, for example—you have to break down this operation into a sequence of additions and multiplications. That’s pretty expensive. Imagine you have an encrypted database and an encrypted data chunk, and you want to insert this chunk into a specific position in the database. If you’re representing this operation as a circuit, the circuit will be as large as the whole database.
In the future, FHE performance is expected to be optimized both on the FHE side (new tricks discovered) and hardware side (acceleration and ASIC design). This promises to allow for more complex smart contract logics as well as more computation-intensive use cases such as AI/ML. A number of companies are working on designing and building FHE-specific FPGAs (e.g. Belfort).
“Misuse of FHE can lead to security faults.”
What can be done with FHE today:
Note: In all of these examples, we are talking about plain FHE, without any MPC or ZK superstructures handling the core FHE issues.
Whom to follow for FHE updates: Zama, Sunscreen, Zvika Brakerski, Inco, FHE Onchain.
As we can see from the technology overview, these technologies are not exactly interchangeable. That said, they can complement each other. Now let’s think. Which ones should be combined, and for what reason?
Disclaimer: Each of the technologies we are talking about is pretty complex on its own. The combinations of them we discuss below are, to a large extent, theoretical and hypothetical. However, there are a number of teams working on combining them at the time of writing (both research and implementation).
In this section, we mostly describe two papers as examples and don’t claim to be exhaustive.
One of the possible applications of ZK-MPC is a collaborative zk-snark. This would allow users to jointly generate a proof over the witnesses of multiple, mutually distrusting parties. The proof generation algorithm is run as an MPC among N provers where function f is the circuit representation of a zk-SNARK proof generator.
Collaborative zk-SNARKs also offer an efficient construction for a cryptographic primitive called a publicly auditable MPC (PA-MPC). This is an MPC that also produces a proof the public can use to verify that the computation was performed correctly with respect to commitments to the inputs.
ZK-MPC introduces the notion of MPC-friendly zk-SNARKs. That is to say, not just any MPC protocol or any zk-SNARK can feasibly be combined into ZK-MPC. This is because MPC protocols and zk-SNARK provers are each thousands of times slower than their underlying functionality, and their combination is likely to be millions of times slower.
For those familiar with elliptic curve cryptography, let’s think for a moment about why is ZK-MPC tricky:
If doing it naively, you could decompose an elliptic curve operation into operations over the curve’s base field; then there is an obvious way to perform them in an MPC. But curve additions require tens of field operations, and scalar products require thousands.
The core tricks suggested for use include:
Essentially, ZK-MPC in general and collaborative zk-SNARKs in particular are not just about combining ZK and MPC. Getting these two technologies to work in concert is complex and requires a huge chunk of research.
According to one of the papers on this topic, for collaborative zk-SNARKs, over a 3Gb/s link, security against a malicious minority of provers can be achieved with approximately the same runtime as a single prover. Security against N−1 malicious provers requires only a 2x slowdown. Both TACEO and Renegade (launched mainnet on 04.09.24) teams are currently working on implementing this paper.
Another application of ZK-MPC is delegated zk-SNARKs. This enables a prover (called a delegator) to outsource proof generation to a set of workers for the sake of efficiency and engaging less powerful machines. This means that if at least one worker does not collude with other workers, no private information will be revealed to any worker.
This approach introduces a custom MPC protocol. The issues with using existing protocols are:
One of the papers on this topic suggests using SPDZ as a starting point and modifying it. A naive approach would be to use the zk-SNARK to succinctly check that the MPC execution is correct by having the delegator verify the zk-SNARK produced by the workers. However, this wouldn’t be knowledge-sound because the adversary can attempt to malleate its shares of the delegator’s valid witness (w) to produce a proof of a related statement. Even if the resulting proof is invalid, it can leak information about w. However, we can use the succinct verification properties of the underlying components of the zk-SNARK, the PIOP (Polynomial Interactive Oracle Proof) and the PC (Polynomial Commitment) scheme.
Other modifications correspond to optimizations, such as optimizing the number of multiplications in, and the multiplicative depth of circuits for these operations; and introducing a consistency checker for the PIOP to enable the delegator to efficiently check that the polynomials computed during the MPC execution are consistent with those that an honest prover would have computed.
According to one of the papers on this topic, “... when compared to local proving, using our protocols to delegate proof generation from a recent smartphone (a) reduces end-to-end latency by up to 26x, (b) lowers the delegator’s active computation time by up to 1447x, and (c) enables proving up to 256x larger instances.”
For a privacy-preserving blockchain, ZK-MPC can be utilized for collaboratively proving the correctness of state transition, where each party participating in generating proof has only a part of the witness. Hence the proof can be generated while no single party is aware of what they are proving. For this purpose, there should be an on-chain committee that will generate collaborative zk-SNARKs. It’s worth noting that even though we are using the term “committee,” this is still a purely cryptographic solution.
Whom to follow for ZK-MPC updates: TACEO, Renegade.
There are a number of ways to combine FHE and MPC and each serves a different goal. For example, MPC-FHE can be employed to tackle the issue “Who holds the decryption key?” This is relevant for an FHE network or an FHE DEX.
One approach is to have several parties jointly generate a global single FHE key. Another approach is multi-key FHE: the parties take their existing individual (multiple) FHE key pairs and combine them in order to perform an MPC-like computation.
As a concrete example, for an FHE network, the state decryption key can be distributed to multiple parties, with each party receiving one piece. While decrypting the state, each party does a partial decryption. The partial decryptions are aggregated to yield the full decrypted value. The security of this approach holds under an assumption of 2/3 honest validators.
The next question is, “How should other network participants (e.g. network nodes) access the decrypted data?” It can’t be done using a regular oracle (i.e. each node in the oracle consensus network must obtain the same result given the same input) since that would break privacy.
One possible solution is a two-round consensus mechanism (though this relies on social consensus, not pure cryptography). The first round is the consensus on what should be decrypted. That is, the oracle waits until most validators send it the same request for decryption. Next, the round of decryption. Then, the validators update the chain state and append the block to the blockchain.
Whom to follow for MPC-FHE updates: Gauss Labs (utilized by Cursive team).
MPC-FHE has two issues that can potentially be mitigated with ZK:
Without introducing ZK, both issues listed above make one fragment of private computations unverifiable. (That doesn’t quite work for most blockchain use cases).
Where are we today with ZK-FHE?
According to Zama, proof of one correct bootstrapping operation can be generated in 21 minutes on a huge AWS machine (c6i.metal). And that’s pretty much it. Hopefully, in the upcoming years we will see more research on ZK-FHE.
Whom to follow for ZK-FHE updates: Zama, Pado Labs.
One issue with MPC-FHE we haven’t mentioned so far has to do with knowing for sure that an encrypted piece of information supplied by a specific party was encrypted by that same party. What if party A took a piece of information encrypted by party B and supplied it as its own input?
To handle this issue, each party can generate a ZKP that they know the plaintext they are sending in an encrypted way. Adding this ZK tweak with two ZK tweaks from the previous section (ZK-FHE), we will get verifiable privacy with ZK-MPC-FHE.
Whom to follow for ZK-MPC-FHE updates: Pado Labs, Greco.
TL;DR: In general, when it comes to using any new technology, it makes sense to run it inside TEE since the attack vector with TEE is orders of magnitude smaller than on a regular computer:
Using TEE as an execution environment (to construct ZK proofs and participate in MPC and FHE protocols) improves security at almost zero cost. In this case, secrets stay in TEE only within active computation and then they are discarded. However, using TEE for storing secrets is a bad idea. Trusting TEEs for a month is bad, trusting TEEs for 30 seconds is probably fine.
Another approach is to use TEE as a “training wheels,” for example, for multi-prover where computations are run both in a ZK circuit and TEE, and to be considered valid they should agree on the same result.
Whom to follow for TEE-{something} updates: Safeheron (TEE-MPC).
It might feel tempting to take all of the technologies we’ve mentioned and craft a zk-mpc-fhe-tee machine that will combine all their strengths:
However, the mere fact that we can combine technologies doesn’t mean we should combine them. We can combine ZK-MPC-FHE-TEE and then add quantum computers, restaking, and AI gummy bears on top. But for what reason?
Each of these technologies adds its own overhead to the initial computations. 10 years ago, the blockchain, ZK, and FHE communities were mostly interested in proof of concept. But today, when it comes to blockchain applications, we are mostly interested in performance. That is to say we are curious to know if we combine a row of fancy technologies, what product/application could we build on it?
Let’s structure everything we discussed in a table:
Hence, if we are thinking about a privacy stack that will be expressive enough that developers can build any Web3 dApps they imagine, from everything we’ve mentioned in the article, we either have MPC-ZK (MPC is utilized for shared state) or ZK-MPC-FHE. As for today, client-side zero-knowledge proof generation is a proven concept and we are currently at the production stage. The same relates to ZK-MPC; a number of teams are working on its practical implementation.
At the same time, ZK-MPC-FHE is still at the research and proof-of-concept stage because when it comes to imposing zero-knowledge, it’s know how to zk-prove one bootstrapping operation but not arbitrary computations (i.e. circuit of arbitrary size). Without ZK, we lose the verifiability property necessary for blockchain.
Sources:
Today we introduce the Aztec Foundation, a nonprofit organization to support the growth and development of open-source programmable privacy. The launch of the Aztec Foundation marks a significant milestone for the Aztec Network, bringing us closer to the launch of a fully decentralized, privacy-preserving network.
As steward of the Aztec Network, the Foundation will conduct fundamental research in freedom-enhancing cryptography. It will also provide support to builders developing innovative applications that protect user privacy, enable compliance, and maintain Noir, the universal language for zero-knowledge proofs.
The Foundation will empower the open-source community to put programmable privacy technology into the hands of builders and deliver on the promise to solve one of the biggest barriers to mass blockchain adoption – privacy.
The Foundation will contribute across protocol operations, technology, and commercial, ensuring the community is involved in key decisions. Its goal is to provide support in bootstrapping a healthy and active ecosystem for emerging projects, helping them become self-sustaining while supporting public good projects that drive adoption.
The Foundation will provide ancillary support to the protocol ecosystem through grants to teams and individuals building end-user-facing applications. The Foundation will also fund cryptography research and other special projects to support Aztec’s greater aim of empowering developers to build privacy-first applications.
The Aztec Foundation is co-founded by Zac Williamson, who will serve as President and Chairman of the Board, and Arnaud Schenk who will lead the Foundation as Executive Director and serve on the board. We’re honored to welcome Arnaud back to the ecosystem. Arnaud is an original co-founder of Aztec with deep expertise in early-stage startup ecosystems who will now help lead day-to-day operations and commercial efforts at the Foundation. Herbert Sterchi, an early board member of Ethereum Switzerland GmbH, will also join as a board member.
For more information about the Aztec Foundation, visit the website and follow @aztecFND on X. To continue following updates on Noir and Aztec, follow Noir and Aztec on X.
It’s no secret that we’re at a pivotal moment for Ethereum.
Ethereum has come a long way since the publication of its white paper in 2014 and has matured in ways that allow us to move closer toward mainstream adoption. Now, with ZK technology and programmable privacy, we’re in a stronger position and better equipped to scale Ethereum and create the next generation of applications. ETHDenver is where this begins.
We’re showing up to Denver in a big way to celebrate the epic builders of Ethereum and a new dawn of applications with the second NoirCon and an immersive night at Meow Wolf.
Following the success of NoirCon 0 in Bangkok, which featured talks from Vitalik and leading privacy researchers, we're bringing the community together again in Denver on Monday, February 24th, 2025 to focus on practical application building.
Since the start of this year, we've seen massive adoption for applications built with Noir, such as Anoncast. The World Team shared their experience, calling Noir "a best-in-class robust and ergonomic tool for ZK developers," and, a number of new Noir Research Grants (NRGs) have been issued to enable privacy to come to AI and identity.
At NoirCon 1, you can expect hands-on workshops from teams actively building with Noir, technical deep dives on AI and privacy applications, real-world case studies of privacy-first development, and interactive sessions with the broader ZK community. In addition, we’ll have a few fiery panels, including zkVMs versus zkDSLs, hosted by Alice Lui, host of House of ZK, and featuring panelists from RISC Zero, Succinct, and Aztec.
With partners such as EigenLayer, BuidlGuidl, Starknet, and House of ZK, we’re excited to create an event focused on helping developers build actual applications.
See event details and sign up for NoirCon 1 here.
To celebrate the new dawn of Ethereum, we’re rolling out the red carpet at Meow Wolf, an interactive venue with artwork from over 350 local and international artists.
We’re encouraging maximum creativity and celebrating you – the builders of the next wave of innovative applications on Ethereum. Dress up fancy, funky, or wild. This is your opportunity to let your creativity shine and revel in the Ethereum culture we all know and love.
Explore Meow Wolf’s immersive space while enjoying cocktails, hors d'oeuvres, and music from an incredible local house DJ. We will have a silent screening of 1995’s Hackers starring Angelina Jolie, along with custom swag from Chipped that you won’t want to miss.
Spots for this event are limited and will be first come, first served. We encourage you to get there early and lean into the theme. A good outfit or costume may even get you to the front of the line.
Sign up for the Ethereum Ball here, and NoirCon 1 here, and let’s build the next generation of applications on Ethereum together.
Stay up-to-date on Noir and Aztec by following Noir and Aztec on X.
