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Mar 25th, 2025
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Is ZK-MPC-FHE-TEE a real creature?

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.

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Written by
Lisa A.
Edited by

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

  1. Introduction: why we are here and why this article should exist
  2. Quick overview of each technology
    1. Client-side proving
    2. FHE
    3. MPC
    4. TEE
  3. Does it make sense to combine any of them and is it feasible?
    1. ZK-MPC
    2. MPC-FHE
    3. ZK-FHE
    4. ZK-MPC-FHE
    5. TEE-{everything}
  4. Conclusions: what to use and under what circumstances
    1. Comparison table
    2. What are the most reasonable approaches for on-chain privacy?

Prerequisites:

Introduction

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.

Quick overview of each technology

Client-side ZKPs

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: 

  • The memory constraint: inherited from WASM memory cap – 4Gb and in case of mobile proving each device has its own memory cap as well. 
  • The maximum circuit size (derived from WASM memory cap): currently 2^20 for Aztec’s client-side proof generation (i.e. to prove any Noir program with Barretenberg in WASM).

What can we do with client-side ZKPs today: 

  • According to HashCloak benchmarking, a client-side ZKP of an RSA signature in Noir is generated in 0.2s (using UltraHonk and a laptop with Intel(R) Core(TM) i7-13700H CPU and 32 GB of RAM).
  • According to Polygon Miden, a STARK ZKP for the Fibonacci calculator program for 2^20 cycles at 96-bit security level can be generated in 7 sec using Apple M1 Pro (16 threads). 
  • According to ZKPrize winners’ benchmarks, it takes 10 minutes to prove the target of 50 signatures over 100B to 1kB messages on a consumer device (Macbook pro with 32GB of memory).

Whom to follow for client-side ZKPs updates: Aztec Labs, Miden, Aleo

MPC (Multiparty computation) 

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:

  • Learn private inputs of other parties;
  • Cause the result of computations to be incorrect.

Hence in the context of MPC security, one wants to ensure that:

  • All private inputs stay private (i.e. each party knows its input and nothing else);
  • The output was computed correctly and each party received its correct output.

To think about MPC security in an exhaustive way, we should consider three perspectives:

  1. How many parties are assumed to be honest?
  2. The specific methods of corrupting parties.
  3. What can corrupted parties do?

How many parties are assumed to be honest?

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.

The specific methods of corrupting parties

There are three main corruption strategies:

  1. Static – parties are corrupted before the protocol starts and remain corrupted to the end. 
  2. Adaptive – parties can be corrupted at different stages of protocol execution and after execution remain corrupted to the end. 
  3. Proactive – parties can switch between malicious and honest behavior during the protocol execution an arbitrary number of times, etc. 

Each of these assumptions will assume a different security model.

What can corrupted parties do?

Two definitions of malicious behavior are: 

  1. Semi-honest (also referred to as honest but curious, or passive adversary) – following the protocol as prescribed but trying to extract some additional information.
  2. Malicious – deviating from the protocol.

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. 

  • According to the “Efficient Arithmetic in Garbled Circuits” paper, for general-purpose MPC, the computation costs are the following: at most O(n · ℓ · λ) bits per gate, with each multiplication gate using O(ℓ · λ) bits where  ℓ is the bit length of values, λ is a computational security parameter, and n is the number of gates. A value can be translated from arithmetic to Boolean (and vice versa) at cost O(ℓ · λ) bits (e.g. to perform comparison operation).

Source

  • As a matter of illustration, we are also providing an example of a specialized MPC protocol:
    According to dWallet Labs, their implementation of 2PC-MPC protocol (2-party ECDSA protocol) completes the signing phase in 1.23 and 12.703 seconds, for 256 and 1024 parties (emulating the second party in 2PC), respectively (claiming the number of parties can be scaled further).
  • Worldcoin jointly with TACEO made a number of optimizations to existing Secure Multi-Party Computation (SMPC) protocol, that enabled them to apply SMPC to the problem of iris code uniqueness. Early benchmarks show that one can achieve 10 iris uniqueness checks per second in ~6M database.

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

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:

  • There are two areas in the hardware, trusted and untrusted. 
  • The application creates an enclave in the trusted area and makes a call to the trusted function. (The function is a piece of code developed for working inside the enclave.) Only trusted functions are allowed to run in the enclave. All other attempts to access the enclave memory from outside the enclave are denied by the processor.
  • Once the function is called, the application is running in the trusted space and sees the enclave code and data as clear text.
  • When the trusted function returns, the enclave data remains in the trusted memory area.

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:

  • SGX is subject to side-channel attacks. Observing a program’s indirect effects on the system during execution might leak information if a program’s runtime behavior is correlated with the secret input content that it operates on. Different attack vectors include page access patterns, timing behavior, power usage, etc.
  • Using SGX requires trusting Intel. Users must assume that everything is fine since the hardware is delivered with the private key already inside the trusted enclave. 
  • As a large enterprise, Intel is pretty slow in terms of patching new attacks. Check sgx.fail to find a list of publicly known SGX attacks that are yet to be fixed by Intel.
  • Application developers who use SGX are dependent on specific hardware produced by Intel. The company might eventually decide to deprecate or significantly change all or specific versions in ways that might make some or all applications incompatible. Or even break them. For example in 2021, SGX was deprecated on consumer CPUs. 
  • It might be hard to detect cheating fast enough if it takes place in a private domain (like with SGX). 
  • In the case of a network relying purely on TEE for privacy (i.e. a number of nodes run inside TEE and each node has complete information), exploiting one node in the network is enough to exploit the whole network (i.e. leak secrets).

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:

  • Taiko is running an execution client inside the SGX (utilizing TEE for integrity). 
  • Secret Network’s validators run their code inside a TEE (utilizing TEE for privacy).
  • Flashbots are running SUAVE testnet on SGX.

Whom to follow for TEE updates: Secret Network, Flashbots, Andrew Miller, Oasis, Phala, Marlin, Automata, TEN.

FHE (Fully Homomorphic Encryption)

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:

  • If the user (who encrypts information) outsources computations to an external party, they have to trust that the computations were done correctly.
    To handle the trust issue, (i) theoretically ZK can be used (though practically it’s not feasible today), (ii) economic consensus can be used. However, as FHE requires custom hardware (as computations to be done are very heavy), the number of participants in the FHE consensus network will always be limited, which is a problem for security. 
  • In the case of the FHE blockchain, there is one key for the whole network. Who holds the decryption key? The same will apply to dApps. For example, if an FHE computation modifies a liquidity pool total supply, that “total supply” must be decrypted at some point. But who possesses the key? (If you’re curious about FHE key attacks, check out this paper by Li and Micciancio).
  • If an external party provides encrypted input, how can the party performing computations be sure that the external party knows the input and that the input was encrypted correctly? (This can be mitigated with zero-knowledge proof of knowledge, which will be discussed in the ZK-FHE section).
  • While using FHE, one should ensure that the decrypted output doesn’t contain any private information that should not be revealed. Otherwise, formally it breaks privacy.
    One should note that there are two different types of decryption: (i) to reveal the entire network (e.g. reveal cards at the end of the game), (ii) reencryption (i.e. decryption and encryption) as a view function (e.g. view your own cards). 
  • FHE is “heavy.” When considering FHE computation cost (both in terms of computation volume and memory required), related considerations include (i) operations computation cost, (ii) communication cost, and (iii) evaluation keys size (a separate public key that is used to control the noise growth or the ciphertext expansion during homomorphic evaluation).
    One might think about FHE hardware similar to Bitcoin hardware (highly performant ASICs).


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.”

Source

What can be done with FHE today: 

  • According to Ingonyama: With an LLM like GPT2, processing time for a single token is approximately 14.5 hours.
    Token is a unit of text, for example, one english word ≈ 1.3 tokens. Each text request to GPT2 consists of a number of tokens. Based on the processing time of one token, one can define the processing time of the whole request.
    With parallel processing, deploying 10,000 machines, the time is 5 seconds/token. With a custom ASIC designed, the time can be decreased to 0.1 second/token, but this would require huge initial investments in data centers and ASIC design.
  • According to Zvika Brakerski: When asked the question “Can we build production-level systems where FHE brings value?” he responds, “I don’t know the answer yet.”
  • According to Zama: A toy-implementation of Shazam (a music recognition app) with Zama FHE library takes 300 milliseconds to recognize a single song out of 1,000. But how will that change as the database grows? (The real Shazam library has 45M songs.)
  • According to Inco, FHE is usable today for simple blockchain use cases (i.e. smart contracts with simple logics). For example, in a confidential ERC-20 transfer that’s FHE-based, you are performing an FHE addition, subtraction, comparison, and conditional multiplexer (cmux/select) to update the balances of the sender and recipient. With CPU, Inco can do 10 TPS, and with GPU – 20-30 TPS. 

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.

Does it make sense to combine any of these, and is doing so feasible?

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). 

ZK-MPC

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. 

Source

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: 

  • MPC techniques applied directly to elliptic curves to make curve operations cheap.
  • The N shares are themselves elliptic curve points, and the secret is reconstructed by a weighted linear combination of a sufficient number of shares.
  • An optimized MPC protocol is utilized for computing sequences of partial products. 

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:

  • Existing state-of-the-art MPC protocols achieving malicious security against a dishonest majority of workers rely on relatively heavyweight public-key cryptography, which has a non-trivial computational overhead. 
  • These MPC protocols require expressing the computation as an arithmetic circuit, including expressing complex operations such as elliptic curve multi-scalar multiplications and polynomial arithmetic that is expensive.

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.

MPC-FHE

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).

ZK-FHE

MPC-FHE has two issues that can potentially be mitigated with ZK:

  1. Were inputs encrypted correctly?
  2. Were the computations on encrypted data performed correctly?

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.

ZK-MPC-FHE (a sum of MPC-FHE and ZK-FHE)

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.

TEE-{everything}

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:

Source

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).

Conclusions: should we combine them all?

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? 

Source

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:

  • A paper, “Secure Multiparty Computation (MPC)” by Yehuda Lindell.
  • An article, “Introduction to FHE: What is FHE, how does FHE work, how is it connected to ZK and MPC, what are the FHE use cases in and outside of the blockchain, etc.”
  • A talk, “Trusted Execution Environments (TEEs) for Blockchain Applications” by Ari Juels.
  • An article, “Why multi-prover matters. SGX as a possible solution.” 
  • A paper, “Experimenting with Collaborative zk-SNARKs: Zero-Knowledge Proofs for Distributed Secrets” by Alex Ozdemir and Dan Boneh.
  • A paper, “EOS: Efficient Private Delegation of zkSNARK Provers” by Alessandro Chiesa, Ryan Lehmkuhl, Pratyush Mishra, and Yinuo Zhang.
  • A paper, “Practical MPC+FHE with Applications in Secure Multi-Party Neural Network Evaluation” by Ruiyu Zhu,  Changchang Ding, and Yan Huang.
  • An article, “Between a Rock and a Hard Place: Interpolating between MPC and FHE”
  • A talk, “Building Verifiable FHE using ZK with Zama.”
  • An article, “Client-side Proof Generation.”
  • An article, “Does zero-knowledge provide privacy?”

Read more
Aztec Network
Aztec Network
21 Jul
xx min read

Introducing Alpha V5

The Aztec Network today activated Alpha V5, a major protocol upgrade passed by token-holder governance and executed onchain. Alpha V5 reduces private-transaction proving times by more than 2x compared to the previous version, lowers the cost of a fully private transaction by roughly 50%, resolves the critical issues found in V4, and sees the first wave of apps go live. Users can now send private transactions and earn yield on Aave simply by connecting their Ethereum wallets on Nyx, bridge from Ethereum to Aztec using Shield or TRAIN, privately collect NFTs on RavenHouse, or play Dark Forest Aztec, a hidden-information strategy game in a universe that lives entirely onchain. 

"Alpha V5 continues Aztec's work at the frontier of client-side proving, with cryptographic breakthroughs that cut proving times by more than half this release," said Zac Williamson, Co-founder, Aztec Foundation. "We believe Aztec is now the fastest system in the world for proving a fully private transaction entirely on a user's own device, and every release moves the industry closer to private transactions at public transaction speeds."

As the only decentralized privacy L2, Aztec is the credibly neutral privacy layer for Ethereum. Aztec allows anyone to write smart contracts that include both private and public aspects – every private transaction is proven on the user's own device, so no operator, sequencer, or intermediary can see the data. The Alpha V5 proving improvements come from cryptographic advances that make this client-side proving faster than any prior release. The network remains in alpha, but with V5 it is ready for teams to begin building and deploying applications.

Performance - 2.5 second fully private transactions 

Making private transactions practical comes down to how quickly a proof can be generated on a user's own device, without offloading that work to a server that would learn what the user is doing. On Alpha V5, proving a private token transfer natively now takes approximately 2.5 seconds on a consumer laptop, down from 5.2 seconds on V4, and about 6.8 seconds in a browser, down from 12.5 seconds. Across every measured transaction flow, client-side proving times improved by approximately 2x compared with V4.

Bench machine: an M2 MacBook (12 cores, throttled to 8). "Native" runs Aztec's C++ proving binary; "WASM" runs the same prover in a browser engine (Node on V8).

Alpha V5 lowers ECDSA signature-verification cost by approximately 2x, speeds up Poseidon2 hashing by approximately 3x, and reduces the protocol circuit gate count by approximately 50% (gate count is the number of individual operations a proving circuit must perform, and it is the main driver of how long a proof takes to generate). Each of these lowers the amount of work a device performs to prove a transaction, and the reduction in gate count in particular compounds across every proof the network generates.

Apps - send, receive, and earn privately on Ethereum

Alpha V5 launches the first wave of apps on a network where privacy is built into the protocol rather than managed by an operator. On other networks that claim privacy, transactions still pass through an operator or node that reads them in plaintext, or depend on a viewing key that a third party holds, so users rely on someone else to protect their data and to decide when it gets disclosed. On Aztec, every private transaction is proven on the user's own device, so the app, the sequencer, and any operator never need to see the underlying data. Nyx is one of these apps, allowing users to privately send transactions and privately earn yield on Aave. 

"On Ethereum, everything you do is public. That's why we built Nyx: a private account governed by your Ethereum wallet", said Nikhil, Co-founder of Nyx. "Now you can send, receive and earn in private. Nyx was the first app live on the Aztec Alpha, and we're excited to expand participation to more users with the added stability of Alpha V5."

Other apps on Alpha V5 include Azguard and Nethermind (wallets), Shield, TRAIN, and RavenHouse (bridges), and the Aztecscan block explorers. Also launching is Dark Forest Aztec, a game where users explore a universe, control planets, manage planetary energy, expand territory, and launch attacks through strategic play with private state and hidden actions.

Dark Forest Aztec private universe-building gameplay

Lower costs, higher security 

Transaction fees on Aztec come from two main sources: the cost of proving a transaction and the cost of verifying the rollup proof on Ethereum. Alpha V5 reduces both. It lowers the network's proving-cost parameter by 50%, and it reduces the L1 gas required to verify a rollup proof by approximately 40%. Because rollup proofs are verified on Ethereum and that cost is shared across all transactions in a batch, the L1 reduction lowers fees for every user, while the lower proving-cost parameter reduces the per-transaction proving fee directly. Together, these bring the average cost of a fully private token transfer to under a $0.05 transaction cost.

Alpha V5 also hardens the network on several fronts. It resolves critical vulnerabilities found in Alpha V4 along with additional bugs discovered since launch. Aztec's bug bounty program on Cantina also drew more than 234 security researchers to participate. The network remains in alpha, and further bugs may surface as usage grows, but each release has closed the issues found in the last and strengthened the protocol against new ones. With the critical V4 issues resolved and these safeguards in place, Alpha V5 is stable enough for teams to begin building and deploying applications.

Availability

Alpha V5 is live now, view the Alpha V5 landing page for a full list of features, performance updates, and live apps to explore. 

About Aztec

Aztec is the only decentralized, privacy-first Layer 2 on Ethereum. Developers write private and public logic in the same smart contract, and private functions are executed and proven on the user's own device, so no operator sees the underlying data. The protocol is upgraded through onchain governance, and the network settles to Ethereum. For more information, visit aztec.network.

Aztec Network
Aztec Network
30 Jun
xx min read

Inside an Aztec Transaction

On Ethereum today, each transaction reveals everything publicly. The token you moved, the size, the timing, the wallet it came from, every action you take. Given the limitations of this type of transparent network, the industry is now focusing on bringing privacy onchain as a top priority. The response to this has mostly been to enable private transactions that shield transfers in various ways. But when we look at how privacy works on Web2, it’s clear that users and developers need granular privacy controls: the ability to decide what is public or private and who is able to see different types of data.

Aztec was built so that one transaction can carry two halves. A private half that runs on your own device and never leaves it, and a public half that the network runs in the open. Apps can choose which aspects are private or public, and users can choose what they want to reveal and when.

This article will follow an example transaction on Aztec: a vote in an onchain election built on Aztec, where who you are and which candidate you chose stay private, while the running tally for each candidate stays public for anyone to verify.

Public and private in one move

Picture the vote you cast in our example as two aspects that seamlessly weave together. In the first step, you act in private: an app records your vote on your device and hands the network a proof that the vote is valid without revealing it. In the second, the network acts in public: it checks that proof, then adds one to the chosen candidate's public tally. It is one transaction: one part stays with you, one part goes to the network. Both parts end up recorded onchain, in two separate state trees, one private and one public. The walkthrough below follows how these two aspects work together and what this means for how your transaction lands onchain. 

It starts on your device

You open the voting app and connect an Aztec wallet. That first step looks like any onchain app. The difference is inside the wallet. An Aztec wallet carries a private execution environment, the PXE, pronounced "pixie", which runs on your phone or in your browser. The PXE is where the private half of your transaction executes, and where the proof of that work gets made, on your hardware, under your exclusive control.

Every account on Aztec is a smart contract rather than a bare key. That design, account abstraction, allows a wallet to authorize a transaction however its owner chooses without writing an identity onto the network for everyone to read. The wallet is the front door, and on Aztec you can decide if the door is open or closed, who you share your information with. 

The private half runs on your device

The voting app is a smart contract with two kinds of functions. The private functions run first, and they run inside your PXE. Your identity and the candidate you picked are the private inputs, and they stay on your device.

The only thing to leave your device is a proof confirming the legitimacy of your vote. Aztec's client-side proving system, Chonk, takes the private execution and produces a zero-knowledge proof: a compact cryptographic receipt that your vote followed the rules, that you are eligible, and have not voted before, while revealing nothing about who you are or who you voted for. Think of it as a sealed ballot the network can confirm is valid without opening it. The network learns only that a legitimate vote happened. It does not learn how you voted, or even which account voted. 

This is the part that used to be too slow to be practical. Generating a proof on a phone was the bottleneck every privacy app hit. Aztec’s Chonk is purpose-built for fast proving on low-memory devices, both natively and in the browser, so the private half runs on the device in your hand instead of on someone else's server.

The public half runs in the open

Some elements of a vote should be public. The tally is shared infrastructure, the number everyone relies on to trust the result. Thanks to programmable privacy on Aztec, the app marks that part public. Public functions live on the network and run in the open, the way functions do on Ethereum.

On Aztec, private and public logic live in the same contract, and the developer decides which is which, function by function and variable by variable. Programmable privacy is a dimmer, not a switch. The voting app turns it up on the individual ballot and turns it down on the running tally. That boundary is a design decision written into the contract, and it is the thing no transparent chain and no fixed-privacy chain can offer.

The network checks the proof and runs the public part

Your vote leaves your device as a bundle: the zero-knowledge proof of the private half, plus the call to the public function that updates the count. It goes to Aztec's sequencers, a decentralized set of thousands of independent operators, with more than 3,500 of them running the network today.

The sequencers do two jobs at once. They verify the proof of your private vote, confirming it is valid and eligible without seeing the choice behind it, and they run the public function that adds one to the chosen candidate and updates the public tally. Your ballot stays sealed. The count goes up by one for everyone to see. The same proof guarantees you cannot vote twice, even though no one learns which ballot is yours.

Two state trees, both onchain

Aztec has two main state trees, and both live onchain. One holds private state, the other holds public state, so the full record of what happened sits on the network rather than on any one person's laptop. The two trees store each record in two different ways depending on if it needs to be private or public. 

The private tree uses a UTXO model, the same note-based design used by Zcash. In this model, state is written as commitments: each entry is a sealed record that a valid vote was cast, with the voter and the choice kept private. Just like with Zcash or Bitcoin, you do not edit a private entry in place. You write a new one, and the design stops the same vote from being cast twice (old state is nullified). The vote stays private, and the record of a legitimate vote happening is onchain for the network to check.

The public tree uses an account-based model, the same shape Ethereum uses: values that update in place, readable by anyone. This is where each candidate's tally lives.

One transaction wrote information to both trees. The private tree recorded that you voted, sealed. The public tree recorded the new totals, in the open. Everything is onchain. The difference between the two trees is how much each one reveals.

Every private app on Aztec writes into that same private tree. A vote, a payment, and a payroll run all land in one shared record of activity, so each user's privacy grows stronger as the network grows, instead of splitting into a separate pool for every app.

A block is proposed, and Ethereum records it

Aztec is an L2 on Ethereum, so everything settles to Ethereum L1. A sequencer on Aztec gathers transactions into a proposed block. Other sequencers validate it before it goes to Ethereum's pending chain. At that point the block sits on Ethereum, ordered and recorded, waiting for its proof. The network has agreed on what happened and the proposed block is just waiting a final proof. 

Anyone can prove it

Proving a block is its own job, and on Aztec, it belongs to no one in particular. A decentralized, permissionless set of provers competes to take a full epoch, a 32-block stretch of the chain, and compresses it into a single zero-knowledge proof of the entire epoch. Anyone with the hardware can run a prover and bid for the work. There is no privileged operator, no committee you have to trust, no outside network holding a key.

That openness is the whole point of a privacy layer. A system that protects your data but routes it through one trusted server has only moved the exposure rather than removed it. Aztec keeps proving permissionless and your private inputs on your device, thereby avoiding any exposure.

The economics land in the voter's favor too. As an L2 network, Aztec spreads the cost of that one L1 proof across thousands of transactions in the rollup, so a vote costs pennies, not the millions of gas a private proof would cost verified alone on Ethereum.

Settled on Ethereum, verifiable by anyone

A prover then posts the epoch proof to Ethereum's proven chain, and the Aztec state is final. Ethereum verifies one proof and inherits the correctness of everything inside it. Aztec extends Ethereum and settles to Ethereum, so your hybrid transaction carries Ethereum's security without carrying Ethereum's enforced transparency.

Anyone can now verify that the result is valid and that every counted vote was legitimate. No one can see how any individual voted. The tally is on the shared ledger where it belongs, and your ballot stayed yours the whole way through.

What this unlocks

For the voter, their ballot was never a broadcast. The candidate you chose stayed yours, with no record tying your wallet to a name for anyone to read later, and you can still check that your vote was counted and the result is honest. You took part without your choice becoming data for systems built to act on it.

For a founder, the election app in this walkthrough is easy to implement without needing to build extensive custom code. Secret ballots with a public, verifiable count, in one contract, is a product category that opens up only because the boundary is programmable. You can build governance, elections, and polls where people vote without fear and the result still proves itself. And of course you can build anything that requires both public and private state to work seamlessly together. 

For an infrastructure provider, the same machinery serves clients who need a result they can stand behind without exposing the people who produced it. Selective disclosure lets a client prove exactly what a counterparty needs to see, the count and the integrity of the process, and protect everything else, on their own terms. That is a guarantee a transparent chain cannot make.

A real vote needs two things at once: a secret ballot and a count anyone can check. A transparent chain makes you give up the first to get the second. On Aztec, you get both. The tally settled on Ethereum for anyone to verify, and how you voted stayed yours. The infrastructure is in place, what will you create with it? 

->Review the Aztec Basics

->Head to the docs and start building today

Aztec Network
Aztec Network
23 Jun
xx min read

The Devil's Bargain - Privacy Without Credible Neutrality

Crypto is in a long night. It is no secret that the industry is facing challenging circumstances and there has been a clear consolidation of the industry. Right now we are seeing a focus on real traction, demonstrable value projects shipping practical solutions that will meaningfully reach users. 

Some of that discipline is overdue. However, in times like these the properties that made crypto structurally different begin to look expendable. Decentralization slows you down. It makes upgrades harder. It makes institutional sales harder. It removes the control surfaces that the existing financial world knows how to buy.

We used to accept those costs as the price of building something durable. But, in a famine, they look like unaffordable affectations. Discarding them wholesale, however, is like selling the land out from under our feet.

Permissionless, uncensorable transaction networks with rich composability - this is the clay from which our industry was grown. The long term commercial health of our industry depends on preserving these properties in an age of privacy and institutional adoption.

These trade-offs become more challenging and pernicious when privacy is involved. Privacy is the narrative for crypto in 2026, and for good reason. It’s the missing piece that will deliver the traction and real use-cases that the industry so desperately needs. 

The challenges of decentralization multiply under the constraints of privacy and what we are seeing in the industry is not a pivot, but a complete capitulation of all of the differentiable value that made crypto valuable.

I have spent nearly a decade building a network that marries programmable privacy with decentralization. A network where users keep their data, where applications are composable with one another, where transactions can settle without a privileged party learning everyone’s business or deciding which products are allowed to exist. That required new cryptography, new programming models, new state architecture, new wallets, and a fairly insane number of tradeoffs that are invisible until you try to build the thing yourself. There are easier products to ship. 

A centralized privacy service can give institutions something legible quickly, replicating how the existing financial sector works: a responsible operator, a viewing key, a way to block transactions, a way to explain the whole thing to a risk committee. Some of these products will be useful. Some will be good businesses. But they are not the thing we came here to build.

The Devil’s Bargain

Institutional and enterprise adoption is one of the core growth areas in this crypto-winter and the playbook is simple: use the language of crypto as a skin-suit to sell products and services that pattern match onto existing financial rails, with their need for complete visibility, censorship, centralized network operators and all of the liabilities this incurs.

This is a tempting bargain because it shortens the path to adoption. It gives buyers and regulators a shape they understand. A company. A contract. A switch. But the moment you accept that bargain, the system changes character. It may still be encrypted. It may still contain proofs. It may still call itself private. But, it now behaves like and is an operated service. 

There is a party with privileged knowledge and privileged control. Builders must shape themselves around it. Institutions negotiate with it. Regulators may pressure it. Attackers target it. Users ultimately depend on it. By a backdoor I mean something specific: a network or protocol-level viewing key where the product developer does not control who can see their users’ data, especially when paired with network-level controls that can block transactions or ban smart contracts entirely. I do not mean application-level controls. I do not mean user-authorised disclosure. I do not mean a dapp deciding that users must prove something before using it. Regulated applications will need rules. The issue is that the disclosure boundary of your application belongs to somebody else, and the same layer that sees can also decide whether your users are allowed to transact. In short, users lack a platform that has credible neutrality.

The Platform Risk

Privacy on top of centralized rails is fatal. If one party can see everything and stop anything, that party may be treated as responsible for seeing and stopping.

This compounds into substantial platform risk. If an entity builds on top of such a system they must surrender visibility and control to the network operator to satisfy their liabilities without consideration for yours. Decentralization and ultimately credible neutrality is the difference between whether you own durable infrastructure or are renting a service whose rules can change on a whim. Worse, you cannot “just build things”. For novel transaction flows approval must be sought and granted. Tell me, would Ethereum have grown if every smart contract deployment required approval from the Ethereum Foundation?

Privacy needs the same freedom. A private credit market, for example, touches identity, collateral, repayment history, payment flows, liquidation logic, lender disclosures, auditor access and borrower privacy. If every component lives inside a different permissioned service, each with its own operator and viewing assumptions, that is a bureaucratic friction that negates blockchain’s core value proposition; composability.

A decentralized and credibly neutral privacy network prevents the settlement layer from becoming the single place where all surveillance and censorship obligations naturally accumulate. It allows product developers to scope their code to satisfy their own narrow requirements without consideration for the obligations of a centralized operator.

Building for credible neutrality

A lot of today’s privacy narrative treats architecture as if it were a detail. It is not. You cannot take a transparent ledger, staple confidentiality onto the edge, add a viewing key for comfort, and expect to get programmable private infrastructure.

If the state model is not private from the ground up you get wrappers, third party tools, data custodians, ad hoc disclosure paths and a pile of assumptions that every application drags into the next. Developers do not get a normal programming model where private contracts can call private contracts and users keep state on their own devices. They do not get composability.

The difference matters. In a real private execution environment, users generate transactions locally. They do not outsource their intent to a third party who learns what they are doing. Private contracts interact through a state model designed for privacy. The network settles proofs without becoming the party that knows everyone’s business. Privacy is part of the architecture.

This is why Aztec has taken so long. We built something that makes programmable private state and decentralised settlement live inside the same system. That means proving systems that run on consumer hardware, a transaction architecture built around local private execution, and a programming model where privacy is idiomatic and just works out of the box.

A centralized service can skip much of this. It can hold the key, run the prover, approve the flow and call the result privacy. It gets to market faster because it is not trying to arrive at the same place.

The edge

Adding decentralization does not make obligations disappear. Applications, issuers, frontends, custodians and regulated businesses will continue to exist in a web of obligations and responsibilities. Anyone pretending otherwise is unserious.

The question is where those obligations live. If they are pushed into the settlement layer, the settlement layer is no longer credibly neutral. It needs visibility into everyone and controls over everyone. 

The better answer is selective disclosure. Users and applications should prove specific facts to specific parties for specific purposes. A regulated application may need to know that a user passed a check, that a transaction satisfies a policy, or that an auditor can inspect a particular flow. None of that requires the base network to hold a permanent key into everyone’s activity.

This will be harder to explain to the existing world. New infrastructure always fails to fit the categories built for the old infrastructure. Bitcoin did not arrive as a neatly regulated bank product. Ethereum did not wait for every lawyer to understand smart contracts. Stablecoins and DeFi forced institutions, regulators and users to develop new language around rails that kept existing.

If the standard for privacy infrastructure is to plug into the old world without changing anything, the answer will always be a service with a backdoor. And the result will be to catch crumbs falling from the tables of the old world.

The market worth building

The market we should be building is, well, a market. A private financial system that compounds: assets, liquidity, identity, credentials, credit and applications interacting through a shared settlement layer without forcing users to surrender their data to whoever sits in the middle. 

Traditional finance is built out of vertically integrated information silos. Those silos are its moat. Banks, exchanges, custodians, payment processors and data brokers all benefit from controlling the information that flows through them. A global private settlement layer attacks that advantage directly. It lets liquidity and credentials move while outsourcing information custody to neutral cryptographic infrastructure. 

A company wants a moat. A settlement layer wants surface area. A permissioned privacy provider can ration access, raise fees, exclude applications, shape disclosure rules and define acceptable use around its own risk tolerance. These are products pretending to be networks, and not durable financial infrastructure. What bothers me is this compounding category confusion. Networks adding protocol-level viewing keys and transaction controls are using the same language as decentralised programmable privacy, and commentators are treating them as variations of the same thing. They are not.

We have spent nine years walking the hard road. Now, just as we are close, the market has lost faith. Everyone is reaching for whatever lifeline looks immediate. Some of those lifelines will be real. Some will make money. But if crypto responds to its long night by rebuilding financial privacy as permissioned services, then we will have survived by surrendering the property that made the industry worth building.

Markets can grow when the platform is removed from the position where it can dictate the rules. It would be perverse to forget that lesson while building privacy, the domain where control over information matters most.

The land we till

Crypto is in a famine. The land is struggling. We could sell our land for a pittance and survive the season. But the famine will pass, and when it does the land will blossom again. Without the land we are nothing.

We have struggled immensely to create a permissionless network that can marry privacy with decentralisation: an indestructible network whose users cannot be surveilled and whose transactions cannot be censored. This is the soil we have to grow our crops. To surrender a backdoor or a centralized operator for temporary relief is to sell our land for the price of a stablecoin. And we cannot sell the land.


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Aztec Network
Aztec Network
2 Jun
xx min read

Who controls your privacy off-switch?

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:

  1. Who sees your data?
  2. Who can prove the network followed its own rules?
  3. Who controls when something gets disclosed?

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.

TL;DR

Here’s how each network handles the selective disclosure privacy off-switch, and who has control over your privacy: 

  • Aztec: Only you can see your data, client-side proofs settled to Ethereum let anyone verify every transaction without trusting an operator, and the off-switch stays in your hands, allowing you selectively share information.
  • Canton: Participant nodes read your data in plaintext, no outside party can verify the global ledger, and your off-switch sits with those nodes rather than with you, since disclosure depends on them staying honest.
  • Starknet: No operator ever sees your plaintext because proofs are generated client-side, and those proofs verify the rules, but your off-switch is a standing viewing key that a designated auditor can use to decrypt and trace your entire history on request.
  • Tempo: The zone operator sees every transaction in plaintext, mainnet validity proofs let anyone verify the zone ran correctly, and the operator holds the off-switch, so you are private from the public but not from the operator.
  • zkSync: The operator reads every transaction in plaintext while a validity proof on Ethereum proves it cannot forge state, and the operator holds the off-switch over who sees what, giving you privacy from the outside world but not from the operator.

The Comparison In One View

Comparison chart of privacy on Aztec, Canton, Starknet, Tempo, and zkSync

Comparing your privacy off-switch 

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

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

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

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 STRK20

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.

Aztec

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.

Users need to hold their own off-switch, not a promise to look away

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.

Let's Build

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.