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Jun 26th, 2024
## min read

Can blockchains and zero-knowledge help humanity survive? 47 real-world use cases

Worldwide, the average person’s daily screen time is 6 hours and 58 minutes. This is approximately 41% of waking time. Does it mean that a modern human is 41% digital?

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Written by
Lisa A.
Edited by

Are we becoming cyborgs in 2024?

Many thanks to Elias, Palla, Mahima, Hannes, and Rafal for review. Separate thanks to João Montenegro for an awesome discussion.

Worldwide, the average person’s daily screen time is 6 hours and 58 minutes. This is approximately 41% of waking time. Does it mean that a modern human is 41% digital?

Let’s think.

  • Personal relations are becoming digital (17% of marriages and 20% of relationships begin online) as well as business and consumer relations (81% of consumers prefer communicating in a messenger).
  • Society is becoming “cashless”: only 9% of Americans pay with cash, and in some countries it accounts for only 1% (e.g. Norway, Honk Kong, Sweden, etc.).
  • 71% of smartphone owners sleep with or next to their mobile phones.
  • Google search engine acts almost like humanity's hive-mind.
  • “Information and communications technologies are more widespread than electricity.” – Shoshana Zuboff.

One thing can be said for sure: our everyday life is a blend of the physical and digital worlds and this trend won’t reverse. Furthermore, the share of digital reality is going to grow.

Humanity has rich experience (approx. 300,000 years) living in physical reality.

Within this time we’ve learnt some useful skills. For example:

  • How to build reliable constructions such as houses, bridges, roller coasters, and military offices.
  • How to treat our bodies in such a way that they serve us as long as possible.
  • How to build meaningful relations that last for years and make us feel good, loved, and needed.

With digital reality, our experience is approximately 4,878x shorter (assuming 1983 being the internet’s year of birth). So, it seems we are still at the beginning of the learning curve of living in digital reality.

What part of ourselves is digital? Let’s think.

  • Most of our documents, files, and data (e.g. financial and health) are digital and stored on smartphones or laptops.
  • Most of our communications and social interactions are digital: through emails, social media, video calls, etc.
  • Most of our work is done digitally: collaborative work using Google services and GitHub, web services (e.g. Figma), storing and managing files in the cloud (e.g. Dropbox).
  • Most of our memorable moments and things that make us feel good are stored digitally: pictures from birthdays and weddings, screenshotted messages from those we love and hate, playlists we’ve been collecting for years, movie ratings, etc.
  • Authentications and passwords for all services and accounts we use.

What exactly does “digital” mean in all these cases? In most cases, it means stored in cloud services or on a company server. That is saying that 99.9% of our digital “us” is outside of our control.

Google won’t close tomorrow, will it?

Humans like to be optimistic. Google won’t close tomorrow, right? It just doesn’t make any sense. Humanity pays it so much money. Humanity gives it all its data.

Okay, thinking rationally, let’s assume that Google’s best option is to exist forever. But what about our Google accounts? What about the digital “us”? What about data authenticity? What about the part of our life that happens digitally every day? Will it exist tomorrow?

Let’s for a second move from that optimistic perspective to a more realistic one:

  • Whether we have access to our money or not depends on whether the bank allows us or not.
  • Whether we can access our iPhone or not depends on whether Apple allows us or not.
  • Whether we can access our data backup on iCloud depends on whether Apple allows us or not.
  • Whether we can leave a comment on a forum or social media depends on whether we meet the criteria of being a “good user” or not.

So, the first “layer” is: in order to have access to our digital life, we need to meet the criteria of being a “good customer” every day. Every single day. Doesn’t sound nice, but not a big deal, right?

Source

At the end of the day, we are good humans, we have nothing to hide, we trust our governments and these pretty multibillion corporations. They are nice guys. Is there anything else to worry about?

Hmm, let us think what happens if…

  • Artificial general intelligence (AGI) kidnaps the internet and floods it with bots?
  • Company servers are destroyed because of war or natural disasters?
  • Governmental databases break down, destroying all information about citizens and pushing the world into anarchy?
  • Autopilot transports and machinery get programmed for self-destruction on Wednesday 9:00 Eastern Time?
  • Banking system gets lost or corrupted, so we lose all our money because it is just a digital entry on a centralized server?

We still can stick to an optimistic mindset and say: okay, the probability of this happening is very low. Let’s wait until one such incident happens (e.g. nuclear war) and then we should start worrying. Today we still can access banking apps, scroll Instagram, and order a poke bowl. Why do we need to worry now?

There are several reasons to think about it today:

  1. We can’t think about saving the world after the world has been destroyed because the world won’t exist anymore.
  2. Saving the world might take time. A couple of seconds might not be enough. And if the world is going to be destroyed, no one will kindly tell us 20 years in advance.
  3. We still can eat poke while trying to save the world. There is no contradiction therein.

Now let’s be more precise. While appealing to “saving the world”, by “the world”, we mean “the digital world”, a part of our life and identity that exists purely online or in a hybrid state of offline and online (e.g. medical devices implanted into human bodies having software components). By “saving”, we mean making it robust. That is to say neither governments, corporations, AGI, nor bots can kidnap, destroy, control, or limit a digital part of us.

We must make the internet more robust

Comparing our physical reality to digital reality, if in the former we developed robust materials such as carbon, metal, glass, and ultra-high molecular weight polyethylene fiber, in the latter we are still building twig huts.

The good news is that today we are already equipped well enough to start making the internet more robust. Three components that will allow us to resist factors such as AGI or dictatorships are blockchains, privacy incorporated into blockchains, and cryptography. Why these three?

To have control of our digital “us”, we need four components:

  • Permissionlessness
  • Immutability
  • Verifiability
  • Privacy

Let’s abstract from the wording “the internet”. Instead, let’s use the word “cyberspace”, meaning “everything digital” including the internet, computer networks, and whatnot.

By permissionlessness, we mean that cyberspace is equal for any human on Earth. Create an account, deploy code, own a digital object, talk to another human in cyberspace – whatever is possible there for one human is possible for everyone.

By immutability, we mean that humans have control over their life in cyberspace and if they make an action in cyberspace (e.g. open an account, issue an ID, or deploy code), no one can undo it. One should note that immutability might work as a double-edged sword, as if being immutable means nothing can ever be forgotten. Thus, we should be very conscious while building of what exactly we are making immutable and how it might manifest as a negative externality one day.

By verifiability, we mean something like an objective source of truth. If human 54835934 tells human 82849934 that “this statement” is true, human 82849934 has a tool to objectively verify its truthfulness.

By privacy, we mean control over what is disclosed, when, and to whom. How sincere are we claiming “we have nothing to hide”? Even if we don’t mind “someone” having access to all our correspondence, medical data, pictures and videos (including those hot nudes), food delivery, e-commerce, and porn browser history… What if we take it one step further, right to brain-computer interfaces (BCI)? If someone (whether governments, corporations, AGI or another party with exceptionally good intentions) now has access to all our thoughts and body chemistry, do we still have nothing to hide?

These four properties allow us to ensure that our digital “us” will last at least as long as our physical “us” (or maybe even longer, but that is a topic for another article).

Blockchains and cryptography

No technology in isolation is a magic pill. However, combining the right technologies and applying them in the right cases allow us to come closer to inventing a magic pill.

Let’s first clarify what technologies we have in our “survival kit” and what properties of theirs we are interested in.

Blockchain provides permissionlessness and immutability

Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a network.

In this definition, the key property is “shared”. The question is how exactly it is shared or who are the participants of the network. To be shared “for real”, we want the blockchain to have thousands or even millions of nodes that are independent of each other. As of the 15th of April 2024, Ethereum has 5,912 nodes in the network where by nodes we mean different independent parties running infrastructure (one node might run many validators). And even though some of them are run by institutions, Ethereum can be fairly called a shared and immutable ledger. That is to say, Ethereum is good enough to be the base of the robust internet.

Cryptography provides verifiability

Cryptography’s history is almost as long as humanity’s history. But with the advent of the digital era, some specific kinds of cryptography that fit the needs of computers and the internet started evolving at an insane pace.

The history of zero-knowledge cryptography started in 1989, although it wasn’t obvious it would be a perfect complement for blockchains (even though the idea of blockchains was proposed earlier).

A zero-knowledge protocol consists of two parties, a Prover and a Verifier, where the Prover can prove to the Verifier that a certain huge statement is true while conveying only a tiny bit of information. This property is called “succinctness” and is a key component of cyberspace, where individuals need to coordinate with each other all the time and the communication has to be as lightweight as possible.

Privacy-preserving blockchains provide privacy

Privacy-preserving blockchains allow individuals to choose what information to disclose, when, to whom, and in what form. This is another crucial element of cyberspace, as disclosing one bit of data while preserving private other bits of data is a must-have mechanism for efficient coordination.

For example, in the case of elections, an individual wants to tell the tally that (i) they are eligible to vote, (ii) they have voted, (iii) they voted only once, but they don’t want to disclose any details about their vote or their identity.

One should note that privacy on the application layer (e.g. private identity or transfers) can’t be added to a transparent blockchain purely by means of cryptography. Privacy should be incorporated into blockchain design at the level of smart contract anatomy and state management.

Now that we’ve looked at what components we need to build robust cyberspace, let’s explore specific cases we need to fix and decide if we really can fix them with these tools.

47 use cases and 75 examples for blockchains, privacy, and zero-knowledge

In the second part of this article, we will explore 47 use cases and 75 examples of how blockchains, privacy, and verifiability can disrupt, heal, expand, and modify the world around us, addressing its problems, weaknesses, points of failure, and fragilities.

But before we dive into the full list, let’s cover three quick examples as an introductory illustration:

  1. Programmable identity: Today, when someone needs to confirm their identity, age, citizenship, or residential address, they provide a passport, ID, or other formal documents. With zero-knowledge proofs, one can manage all their documents client-side and provide proofs of specific properties upon request, without requiring documents.

One should note that for this use case we strictly need privacy-preserving composable blockchain (e.g. Aztec).

  1. Combining several data sets and a model without datasets and model disclosure between parties.
  1. Smart home that processes all data client-side (i.e. inside of smart home) without sharing its inhabitants’ data with anyone (even Google!).

Now we have some intuition behind how zero-knowledge proofs and privacy-preserving blockchains can create an alternative to the current reality, with several of the cases we’ve mentioned involving fixing data privacy and individual sovereignty issues and unlocking new forms of collaboration and coordination. Finally we are ready to dive into the longlist of blockchain, privacy, and verifiability use cases, where these three factors become the real game changers for the world and humanity’s future.

Most of the ideas described below were borrowed from brilliant minds, either from their public presentations or personal talks. Credit to Barry, Steven, Zac, freeatnet.eth, Jessica, Henry, Tarun, and Joe for being brilliant minds.

We will also categorize all use cases into categories:

  1. Efficient coordination – allows small numbers of individuals coordinating efficiently to compete with much larger agents.
  2. Verifiable computation – “truth objectivity” allows individuals to be sure that something is true without relying on honesty of institutions or other individuals.
  3. Data immutability and robustness – makes the cyberspace environment more long-lasting, reliable, and independent of institutions and individual agents.
  4. Improved world economic efficiency – allows individuals and institutions to utilize the resources at their disposal in a more economically efficient manner.
  5. Privacy – just privacy.

In the table below, we categorize a number of solutions to specific problems we will probably meet within the next decades while the digital share of the world is expanding and rooting deeper and deeper into our reality. We classify them according to whether they require blockchain, privacy, verifiability (provided by zero-knowledge), or a specific combination of these three. In most cases, blockchain serves as a coordination layer, privacy is engaged in all cases where participants can’t go with 100% transparency of all data, and zero-knowledge adds verifiability.

Under blockchain, we assume a decentralized one where nodes are run by thousands of diverse, independent parties. This decentralization makes the network credibly neutral and provides strong security guarantees. Today, Ethereum fits this criteria more than anything else. Layers 2 on top of Ethereum inherit its security property, however, their credible neutrality depends on design details such as approach to block building and upgrade mechanism.

Under privacy, we mean that whatever we want to stay private should be fully processed client-side on the user's device and should not be exposed to any other parties.

We also consider combining blockchain, privacy, and zero-knowledge with other existing technologies such as:

  • MPC and 2PC – enables multiple parties (or two parties in 2PC case) – each holding their own private/secret data – to compute the value of a public function using that private data while keeping their own piece of data secret.
  • TEE – a secure area of a main processor that prevents unauthorized entities from outside the TEE from reading data, and prevents code in the TEE from being replaced or modified by unauthorized entities.
  • Any existing cryptographic primitive such as an ECDSA signature (an example of a public key cryptography encryption algorithm).

Why can't we use these technologies separately and why do we need to combine them with zero knowledge and blockchain? Because we don’t just want to trust those using them that they are acting in good faith; we want to be able to verify that they are acting in good faith.

Efficient coordination

Use case Blockchain Privacy Verifiability
Proof of eligibility (for participation, access, etc.) Example 1: Access to the building (e.g. office).
Example 2: Access to military data regarding ongoing war where if this data is leaked it will lead to numbers of deaths.
Optional Need Need
Proof of innocence
Example: Compliance (proof of not interacting with agents meeting specific criteria, e.g. North Korean hackers).
Need Need Need
A mechanism ensuring collective agreement for making crucial decisions
Example 1: Minimum threshold for pushing the nuclear button where the voting members represent all parties that should be represented in a fair manner (MPC+ZK).
Example 2: Minimum threshold to approve decisions of the board of directors with huge financial impact.
Need Need Need
Prevention from malicious cooperation such as bribery or voting system “tampering” Example 1: Voting mechanism where it is impossible to prove that one has voted for a specific candidate.Example 2: Voters can independently verify that their vote was fairly recorded in the election tally. Need Need Need
“Programmable” trust: verification of specific properties of individuals (e.g. belonging to specific groups, visiting specific events, etc.) that allows the building of “safe spaces”
Example: If one studied at the same university and is a member of the same charity organization, it increases the probability that we share similar values and my default trust in this person can rationally be higher than in those who don’t meet these properties.
Need Need Need
“Temperature check” before making decisions/actions with extremely high general costs or the cost of being among the first.
Example: A government member can check how many other government members are ready to oppose the current ruling coalition and start to act only if they are sure they will have sufficient supporters (MPC+ZK).
Need Need Need
Check the alignment or expectations regarding a specific question in a neutral way as (i) participants don’t need to disclose sensitive information to each other and (ii) no one needs to express their preferences first, so there is no time priority.
Example 1: Negotiation of salary between the employer and employee where no single party needs to reveal their preference first, allowing the second party to adjust their preferences based on the disclosed information.
Example 2: Negotiation of the type of relationship that works best of all for all participants, such as “Should friends start romantic relationships”? (2PC+ZK or MPC+ZK depending on the number of participants).
Need Need Need
Private DAOs as a type of a legal entity
Example: Programmable organization with task-based system where compensation payments fully depend on executed milestones.
Need Need Need
Neutral personal data regulation
Example: Instead of governments enforcing the laws and standards around personal data (e.g. the EU’s GDPR), these standards are designed and regulated by a neutral third party while its execution is verifiable.
Need Need Need
Fair incentives mechanisms
Example: Corporations commit to a specific max level of negative externality regarding the greenhouse effect. If the max level is exceeded even by a tiny amount, the agent is “slashed”. By slashed we might mean monetary or other types of slashing (e.g. “reputational slashing” through public reporting for a worldwide audience).
Need Need Need
Blended experience between offline and online domains
Example: Pokémon Go, where blockchain serves as a coordination layer for a number of players.
Need Need Need

Verifiable computations / truth objectivity

Use case Blockchain Privacy zk
Ensuring that an autonomous automatic system will work exactly in the way it is expected to work (i.e. according to the designed algorithm)Example: Self-driving cars, autopilot planes, and self-navigating rockets can execute only the algorithm they committed to. Optional Optional Need
Executing the algorithm while keeping it a secret, i.e. executing any algorithm that should be executed by an external party while the algorithm owner would prefer not to disclose the underlying mechanism (either for commercial or personal motivations)
Example: Dating matching algorithm (2PC/MPC+ZK).
Optional Need Need
Content authenticity: verifying specific criteria of content (pictures, videos, etc.) such as no-edits, produced by a human, time and place of production, etc.
Example: Distinguishing between deepfakes and authentic sources.
Optional Optional Need
Ensuring that some technology was used in a proper way
Example 1: Ensuring that FHE encryption for cloud data storage was done correctly.Example 2: Zk-snark wrapped up around an NFC signature from a chip's public key to prove that one owns a valid signature without revealing the raw signature itself.
Optional Need Need
Direct data verification from a web2 service
Example: Verifying one’s Twitter username to be used in an external (non-twitter) service.
Optional Need Need
Optimizing subjective manual processes by converting them into objective non-manualExample: Today, to confirm a bank transfer over a specific threshold, one needs to confirm this transfer manually through the bank call center. This manual process can be eliminated by using zero-knowledge proof to pass the bank’s security check. Optional Optional Need
Converting post-factum verification/check into preliminary
Example: In the modern blockchain world, compliance and KYC information is supplied post-factum, after the transaction was executed when it might be too late to address it. Using zero-knowledge, the transaction can be valid only if the counterparties proved that they meet a specific criteria (e.g. possess some kyc token provided by some kyc provider). Hence, any fraud attempts can be handled in time.
Need Need Need
Proof of ML models’ integrity
Example 1: The model owner proves that the result of the ML model running is fair without revealing any information about the model itself.Example 2: Proving that the submitted model was executed on the committed data without revealing data. Combining both examples, the model owner publicly commits to a model, and generates a zk-proof that the committed model was applied to the user’s submitted input, yielding the claimed result.Example 3: In case of LLM making decisions on its own (e.g. shouting military goals), proving how LLM made the decision when the information is fragmented and different parties have access to different fragments. One should be able to see how the data was put together and be able to question it without revealing all data to one specific party.
Optional Need Need
Proof of treating all participants in the same wayExample: Proof of no price discrimination. Need Need Need
Verifiability in supply chains
Example 1: Proving the origin and authenticity of goods and materials.
Example 2: Proving that materials meet compliance standards of supply chains within modern economies without revealing sensitive corporate information such as the identities of their suppliers and customers.
Need Need Need
Verified rating services
Example: Employer review (e.g. Glassdoor) where all the reviewers (i) stay anonymous, (ii) provide proofs of being eligible to review specific company/role.
Need Need Need
Proof of a mistake in a decision made by big data machine
Example 1: If one’s transaction was identified as a fraud but it is not, one can generate verifiable proof that the transaction is legitimate.
Example 2: If one was marked as an individual affiliated with a specific person (e.g. a terrorist) when one is not, one can generate verifiable proof that there are no connections/interactions with this party.
Optional Optional Need
List of attested events (created client-side) based on the data stream
Example 1: Using zk email as a data stream, based on the parking bill from a government authority, to generate a proof that the email owner has a car and lives in a specific city.
Example 2: Based on the shipment delivery updates delivered through zk email, generate a proof of order delivery.
Optional Need Need

Data immutability and robustness

Use case Blockchain Privacy zk
Robustness of interoperability Example: Guaranteed access configurations to Twitter API. That is to say Twitter can’t withdraw or modify this API version and thus break the operations of those using it. Need Need Need
Games/virtual worlds ownershipExample: Games’ immutability protects gamers from game studios’ “dictatorship”, enforcing the ownership over game assets and rules, which is meaningful for professional gamers who spend a huge chunk of their lives playing. Need Need Optional
Immutability for ownership guarantee
Example 1: Impossibility of suspending social media accounts by automated algorithms (especially those with large audiences, e.g. creators) without proof of violation.
Example 2: Impossibility of freezing ad accounts in social media by automated algorithms without proof of violation.
Optional Need Need
Signed internet history that is recoverable in case of web2 internet servers destruction (e.g. putting every internet website as a rollup)
Example 1: Proving data authenticity in case of “sybil attacks”. Example 2: Resisting AI DDOS attack.
Example 3: Impossibility of malicious power’s actions because of data immutability (e.g. revocation of citizenship).
Example 4: Preserving core digital infrastructure in case of internet shutdown/firewalling/censoring (e.g. in case of revolution or war in countries with oppressive regimes).
Need Need Need
A “car” transferring sealed data between various web2 and web3 services (as an alternative to current API mechanism). This allows (i) avoid trusting APIs (e.g. Facebook), (ii) get the data in the format one needs, (iii) data authenticity is verifiable.Example 1: Web2 and web3 oracles.
Example 2: Services built on top of existing services such as “who unfollowed me” on top of Instagram.
Example 3: Cross-chain verification.
Need Need Need
Physical and digital property ownership history
Example: Verifiable ownership history of rare antiques or pieces of art.
Need Need Need
Eliminating the risks of personal database leaks: because of client-side personal data processing, there is no need for collecting and storing huge personal data databases that can be leaked or hacked Need Need Need

Efficiency

Use case Blockchain Privacy zk
Creating global settlement layer of the world’s assets: digitizing real-world assets (e.g. property, pieces of art, IP, etc) to be able to use them in the digital world
Example: Use physical assets as collateral for digital lending.
Need Dearly need Need
Improving efficiency of decentralized services so that they are able to compete with centralized ones
Example: For democracies (which require a lot of multiparty coordination) to be able to compete with the authoritarian regimes that are highly centralized and coordinated.
Need Need Need
Neutral databases of authorized attestations
Example: Proof of holding a certificate issued by the International Culinary Institute that can be verified by anyone without additional requests to the institution that issued it.
Need Need Need
DeFi as an alternative to traditional financial institutions to (i) provide banking services for underbanked parts of the world, (ii) provide humans with censorship resistance and immutability where institutions can’t limit access to assets and number of allowed actions. One should note that even though we have been talking about DeFi for quite a while, the DeFi we have today is fully transparent. That is to say, if one borrows money, everyone can see it, all payments (e.g. salaries), savings, and assets are easily observable. To make DeFi the real alternative to traditional financial institutions, we need privacy-preserving composable blockchains, otherwise it’s a toy example.
Example 1: Buying stablecoins in countries where people are not allowed to buy foreign currency.
Example 2: Immutable private transactions or private compliant transactions.
Example 3: Providing basic banking services in countries with low GDP and weak institutions that can’t provide their population with access to banking.
Example 4: Broker-dealer network for securities trading services.
Need Deadly need Need
By reducing the amount of data that needs to be transmitted and processed, ZKPs can also significantly reduce the energy demands of IoT devices, improving efficiency and reducing costs. Optional Optional Need
Programmable spending based on the on-chain income
Example: Recurring monthly debt payment as the first spending from the salary.
Need Need Optional

To glimpse how all these use cases are possible through combining zero-knowledge and privacy-preserving blockchains, one should note that even though these technologies are absolutely awesome, they are still not magic pills. We need developers, engineers, and architects to combine them in smart ways. That is to say that the real magic pills are the developers, engineers, and architects capable of doing this and caring about our common future as much as the Aztec team does.

The components (i.e. blockchain, privacy, and verifiability) have been clear at a theoretical level for years. But even when they started to be implemented, most implementations were not really feasible for real-world usage and were hard to combine with each other. This was a serious issue, as web3 applications should be competitive with the existing web2 stuff we already use.

Aztec Labs addressed this issue and has been developing Noir, an open-source, domain-specific language that makes it easy for developers to write programs with arbitrary programmable logic, zero-knowledge proofs, and composable privacy. The program logic can vary from simple “I know X such that X < Y” to complex RSA signature verification. If you’re curious about Noir, check the talk “Learn Noir in an afternoon” by José Pedro Sousa.

Privacy-preserving blockchain and zero-knowledge in spacetech and defense

For those who are still skeptical about these 47 use cases above as too cypherpunk-ish, the next section talks about the need for privacy-preserving blockchains and zero-knowledge cryptography in a very specific domain for solving very specific problems.

Blockchains and zero-knowledge in space

Today, operating in near-Earth space is a part of everyday life: countries, industries, and businesses rely on the day-to-day operation of satellites and the complex infrastructure created in Earth’s orbit. That is to say, a number of parties (some of them are obviously hostile towards each other) manage a number of programmable “nodes” with sensitive data. That sounds exactly like a coordination problem that privacy-preserving blockchains and zero-knowledge are able to handle!

  • Case 1: satellite coordination
    Most large countries have their satellites flying in space for some specific missions. That reminds one of car or plane traffic, but instead there are a number of satellites going from point A to point B. The goal is to manage this traffic in a way that there are no collisions or accidents. The coordination problem is that space is everyone’s – that is, the model of plane traffic doesn’t work.

Instead, satellite owners have to coordinate with each other (and as a consequence trust each other) to negotiate space management. This requires pretty deep data disclosure and trust that other parties have good intentions.

We can utilize zero-knowledge to provide proof of correct code execution, meaning that the satellite will perform exactly what its owner promises. For example, it can generate “proof of route”, which is a crucial component of space coordination as one additional zero in the code can send the satellite spinning forever or force it to change its trajectory and crash into other satellites. Privacy-preserving blockchain can be used as a coordination layer to deploy protocols for specific use cases.

  • Case 2: operating over hostile areas
    Sometimes satellites need to fly over the area of the countries with whom they are “not friends”. While flying over these areas, enemy representatives might be interested in hacking the satellite, intervening in its operating activity, and data forgery. For example, providing AI-generated images instead of authentic ones. To mitigate this risk, we can use zero-knowledge to provide proof of data authenticity or proof of metadata.

  • Case 3: planetary defense
    Planetary defense is the effort to monitor and protect Earth from asteroids, comets, and other objects in space. Life on Earth has been drastically altered by asteroid impacts before. For example, once a planet-shaking strike led to the extinction of the non-avian dinosaurs.
    Planetary defense combines comet and asteroid detection, trajectory assessment, tracking over time, and developing tools for possible collision prevention (includes slamming a spacecraft into the target, pulling it using gravity, and nuclear explosions).
    Planetary defense is operated by NASA, the European Space Agency, and other organizations, all representing different countries and dealing with sensitive data collection and secret technologies (e.g. satellite engineering mechanisms).
    ZKPs and privacy-preserving blockchains can be a coordination layer to process data collected by different parties without exposing it to others.  

Blockchains and zero-knowledge for LLMs on battlefields, enterprise, and everyone’s daily lives

LLMs are (today, already, obviously) very valuable, are shaping an absolutely different economy, and will have a huge impact on daily humans’ lives, geopolitical balance, and enterprise operations.

One of the issues with LLMs is that they can’t tell you how they reached their conclusions. However, some of these conclusions change the world and impact millions, if not billions, of people, being used for business, for countries, for battlefields. Take for example the case of using LLMs to detect targets on a battlefield. The model tells the commander “This is the target”, but it doesn’t provide any proof that this target was detected correctly. In this case, the cost of wrong detection is at least a life, maybe a dozen lives, or several hundred thousand lives.

Zero-knowledge proofs can be a “neutral arbiter” providing the proof of what data the model was trained on, what data the model used to make a conclusion, how this data was put together, what the underlying algorithm is, etc. Furthermore, it can be done without revealing any specific information about either the data or the model.

One should note that today we are not talking about whether or not we should use LLMs for specific use cases. The reality is they are already used everywhere, by all major corporations, countries, and their governments. But what we still are able to do while LLMs start flooding our world is make the model owners accountable for their models – that is to say enforce some formal LLM compliance.

Similar to private data processing and collection today, some legislation for AI regulation will be set up as well. However, mere legislation is not enough; standards should be transparent and equal for everyone, and they should be followed.

Can a trusted third party who is believable, shares pro-democratic values, and is neutral enforce legislation compliance? In a domain such as AI, where at stakes are at the very least huge amounts of money (if not shaping the geopolitical landscape of the world for years to come), there are no neutral parties – everyone has their own interests and skin in the game.

In LLMs we don’t have custodial relations to the data, we can’t prove how the decision was made. But at the same time, we need to know how the data was put together and be able to question it. That is, for example, an absolute necessity for the presumption of innocence. ZKPs as a source of truth and privacy-preserving blockchains as a coordination layer can solve this issue to enforce AI standards compliance.

Conclusion

We are just at the very beginning. Privacy-preserving blockchains and zero-knowledge cryptography are needed in spacetech, agrotech, medtech, biotech, AI/ML, military technology, social networks, retail, robotics, big data, IoT, media and entertainment, edtech, fintech, logistics, neurotech, etc.

The 47 use cases mentioned above are kind of obvious today, but the real landscape of ZKPs and blockchain usage in 20 years will be much wider, deeper, and more diverse. Some of them can be predicted today, while some of them are almost impossible to imagine (unless you’re a true visionary).

One thing is absolutely clear, however: world verifiability is an absolutely required property while our world merges offline and online universes deeper and deeper. ZKPs as a “source of truth” and privacy-preserving blockchains as a coordination layer are a very promising duo to make the world verifiable.

It will take us time. The right moment to start was yesterday. But today is also a good day: for those ready to act together with Aztec – fill in the form.

Sources:

  • An article “Planetary defense: Protecting Earth from space-based threats” by Vicky Stein.
  • A talk “on The Global Tech Race” by Alex Karp.
  • A talk “Charting Taiwan DID as a Showcase & Experiment” by Noah Yeh.
  • A talk “State of ZK ECDSA” by Gauthier.
  • A talk 2PC is for Lovers by Barry Whitehat.
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Aztec Network
Aztec Network
7 Aug
xx min read

Alpha V5 Proving System Vulnerability

Status

Core contributors identified a critical vulnerability affecting the V5 Alpha proving system on 27 July 2026 through internal AI-assisted auditing.

V5 remains Alpha software. Critical findings can arise during this phase, and the audit process exists to identify them before broader deployment. This finding places V5 funds, applications, and contract state at risk.

Treat funds and applications on V5 as exposed to a protocol-level failure until contributors complete incident response work and operators carry out the required network actions.

What we are disclosing

An attacker may be able to exploit a flaw in the current V5 proving system by constructing a proof that passes verification for a transaction the network should reject. If accepted, that transaction could produce a state transition outside the rules V5 intends to enforce.

Contributors cannot determine whether anyone exploited the flaw before this finding. The affected system lacks the information needed to distinguish ordinary accepted transactions from transactions accepted through the flawed proving path. Historical chain activity cannot establish whether exploitation occurred or quantify its impact.

Application safeguards

We expect application teams to prepare safeguards in the coming weeks.

Those safeguards may include changes to application controls, deployment procedures, user flows, and migration plans. We expect each team to assess its contracts and determine which protections fit its architecture and users.

We expect teams planning a V5 deployment to pause that work until contributors publish further guidance. We expect teams with live contracts to review their ability to limit user exposure, isolate affected functionality, and move users to fresh deployments if needed.

We expect applications that maintain administrative or emergency controls to assess whether those controls can reduce user risk during the incident timeframe.

Next steps

Core contributors are working with operators, application teams, and bridge operators as applications add security guards around affected flows.

The findings from this incident will inform the V6 release, including circuit updates that prevent the network from accepting proofs tied to an affected proving system.

V5 launched as Alpha software, with V6 planned for later in 2026. Contributors will publish a security roadmap covering the remaining work and release path.

Known vulnerability status

Reviewers have not identified other high-severity or critical V5 Alpha vulnerabilities at this time.

Internal and external human audits have completed, and contributors continue AI-assisted auditing. Alpha is the period for identifying faults before production deployment.

Community
Community
4 Aug
xx min read

Dark Forest Aztec Game Goes Live

Dark Forest is a real-time strategy game played across a procedurally generated universe where most of the map is hidden. You cannot see rival players, their planets, or their fleets. You only know what you have explored. Everyone shares one universe, and nobody has the full picture.

In most onchain games, every position and every move is public, because the chain is public. Dark Forest used zero-knowledge proofs to break that assumption: players prove their moves are valid without revealing where those moves came from. The result is a game of hidden information running on a public network.

Dark Forest Aztec ports the original Dark Forest 0.6 to Aztec. It keeps the gameplay from the original and rebuilds the privacy layer on Aztec's programmable privacy.

A note before diving in: this is early, experimental software on Aztec Alpha V5. Treat it as an alpha and play accordingly.

The universe you cannot see

You start on a single home planet with almost the entire map dark. To find anything you mine the universe, running a client that explores coordinates and reveals what sits there: unclaimed planets, resources, and eventually the edges of other players' territory.

You are never handed a view of the board. You earn it one region at a time, and everyone else works under the same fog.

What is hidden on Aztec

Your home coordinates and your fleet movements are private state, expressed as first-class private notes on Aztec. Your location and where you send energy stay hidden, enforced in the contracts by zero-knowledge cryptography.

What sits onchain is a set of cryptographic commitments. Instead of storing every planet's full details in the open, the contracts store Poseidon2 hashes of entity state. When you make a move, your client supplies the full state, the contract checks it against the stored hash, applies the change under zero-knowledge constraints, and writes a new hash back. Full game state lives offchain and gets rebuilt from public logs by an indexer, which is what renders your map without exposing every player's position.

So you can prove you made a legal move from a planet you own without revealing where that planet is. Aztec applies the same principle to private payments and private contracts.

How you play

Four actions carry the game.

Explore. Your explorer sits in the bottom left. Set it running and it uncovers the map around you, surfacing planets, resources, and other players.

Send energy. Most planets produce energy. Click and drag from a planet you own toward a target to capture or weaken it.

Route silver. Asteroid fields produce silver. Move it to your planets and spend it on upgrades, or send it to a Spacetime Rip to convert it into score.

Hunt artifacts. Some planets hold artifacts. Your Gear ship discovers them. Once harvested, you deposit them on planets to boost stats.

Four stats drive most decisions.

Energy is the core resource. Planets generate it over time up to a capacity, and you spend it on everything: claiming planets, reinforcing your own, attacking rivals. Two details matter. Moves are taxed, so a flat percentage of a planet's total capacity burns every time you send energy, which discourages small frequent moves. And energy decays over distance, so send it too far and almost nothing arrives. A common rule of thumb is to let a planet fill to about 75%, then send it down to about 25%.

Defense reduces the damage incoming energy does when it lands. Higher-level planets often have lower defense, but they hold much more energy, so they still take more to capture. Defense matters most on front lines.

Range sets how far a planet can send energy. It governs how fast you expand and how efficiently you move energy inside your own empire, since shorter relative distances mean less decay. Good range also lets you strike deep into an opponent's territory.

Speed sets how quickly a move arrives. Usually secondary, though a fast strike can land before a rival reacts, and some playstyles reward capturing many nearby planets quickly.

Planets can also be upgraded with silver and enhanced with artifacts. Space types carry different multipliers, from mild Nebula to punishing Dead Space, so where a planet sits changes how it plays.

How scoring works

There is a scoreboard, and territory alone does not win it. This round scores two activities: discovering artifacts with your Gear ship, and withdrawing silver through Spacetime Rips.

Point values from the in-game help page:

  • Each unit of silver withdrawn: 1
  • Common artifact: 2,000
  • Rare: 10,000
  • Epic: 200,000
  • Legendary: 3,000,000
  • Mythic: 20,000,000

Silver accrues one point at a time. A single Mythic artifact is worth twenty million of them, so artifact hunting decides rounds and silver withdrawal sets your floor.

Silver has two competing uses. Spend it on upgrades and your planets get stronger, extending range and hardening defense. Withdraw it through a Spacetime Rip and it becomes scored points, but it is gone. Every unit is a choice between building the empire and banking points.

Upgrades tend to win early, since a stronger empire reaches more asteroid fields and finds more artifacts. Late in a round that calculation flips, because a planet you never use is worth less than points already scored.

Artifacts do both jobs at once. They score on discovery, and once deposited they boost a planet's stats, which makes the next expedition easier.

Why you explore

Nothing happens until you find something to act on. Your explorer turns dark space into planets you can capture, asteroid fields you can mine, and artifact-bearing planets you can raid. Sitting still means no new energy, no silver, no score.

Exploring also buys information. The map you have uncovered is an advantage nobody else holds. Knowing where high-level planets sit, which asteroid fields are unclaimed, and where space types shift lets you plan further ahead than someone still working through their starting region.

You find other players as a byproduct. There is no player list. You explore outward until your revealed region touches territory someone already owns: a planet in another player's colors, sitting where you were about to expand. Their home coordinates stay private, so you learn something narrow. Someone is here, roughly this direction, holding this much. You infer the rest, and you have no way of knowing whether they found you first.

What happens when you run into someone

You have three broad options.

Stay quiet and keep growing. Nothing forces you to engage. Keep exploring elsewhere, keep routing silver, keep upgrading. Your positions stay private, so silence costs you only time, which is what you want if they are stronger. The risk is that they are doing the same thing faster.

Fortify the border. If the contact sits somewhere you cannot lose, spend energy hardening the planets facing them. Defense is worth most where an attack will actually land. This keeps the option to fight without committing to one.

Attack. Send enough energy to overwhelm the target's defense and the planet becomes yours, along with its production and its position as a staging post. Higher-level planets are the prize and take proportionally more to crack.

Attacking costs more than energy. A move that lands tells your rival where you strike from, and that you are close enough to be worth answering. Retaliation can then come from directions you have not explored, launched from planets you cannot see.

Multiplayer in practice

Everyone plays one shared universe in real time. No turns, no lobbies. Energy regenerates whether you are watching or not, moves stay in flight while you sleep, and rivals expand while you are away from the screen.

Most strategy games let you watch a threat approach. Here you tend to see the consequences: a planet you owned this morning in someone else's colors, an incoming move you notice once it is already close.

That produces a particular kind of paranoia. You are trying to find everyone else while avoiding being found, and every expansion is a strategic bet that the space ahead is empty.

Information becomes tradeable, because it is scarce. Players compare notes, warn each other about aggressive neighbors, and agree who expands where, then break those agreements when the scoreboard makes it worth breaking.

Why it matters beyond the game

A fully onchain game where players cannot see each other's positions is hard to build, and building it well says something about the platform underneath.

Hidden state, private notes, and client-side proving are the same building blocks behind private applications across Aztec. Dark Forest is a way to watch them work.

Getting started

Dark Forest Aztec is playable now as an alpha. Expect a learning curve; the original was famous for it. DFArchon maintains onboarding material and a community for new players. Round One is live. The universe is dark, and everyone else is out there somewhere. Go find them, quietly.

Play Now

Follow the Builders

DFArchon on X

Source and docs

Aztec Network
Aztec Network
22 Jul
xx min read

How Gas Works on Aztec

Gas on Aztec

Gas on Aztec is known as Fee Juice and is used to pay for transaction costs. This is the same as $ETH on Ethereum. Some apps will handle transaction costs for you under the hood, but if you are using a browser extension wallet, you will not be able to send transactions without it. Fee Juice can be obtained by bridging the $AZTEC token on Ethereum to the Aztec Network L2. This means that under the hood, all activity that happens on Aztec is underpinned by the $AZTEC token bridged into the network. Some bridges like Shield (by human.tech) handle this for you, allowing you to allocate a portion of your bridged transaction to convert into Fee Juice and land in your wallet automatically.

Public vs Private Assets

Assets and transactions on the Aztec Network can be either public or private. If you bridge publicly, your tokens will arrive as public, traceable tokens visible to all. Privately bridging, on the other hand, will give you private assets that are visible only to you. These assets can then be sent privately to another user or wallet without ever revealing who you are, what tokens were sent, how many, or who the recipient is.

Public vs Private Gas

Like tokens on the Aztec Network, Fee Juice (gas) can also be public or private. The reason for this is that even if what you are sending is private, the gas you spend to execute that transaction could still be visible if you are using public Fee Juice, potentially revealing transaction patterns and activity. Private Fee Juice keeps your entire transaction footprint hidden. When you send a private transaction, you can use private Fee Juice, and when you send a public transaction, you can use public Fee Juice, which means your transaction costs are always aligned with the type of transaction you're making.

Fee Juice in Apps

Aztec has native fee abstraction, which means apps could let you pay for transactions in any token you want, or cover your fees entirely. Apps like Nyx may choose to cover part or all of a user's transaction costs, or allow you to pay in tokens that are convenient for you. This means you will most likely never see Fee Juice in an app; instead, you'll pay in whatever makes sense for what you're doing, on your terms. Similarly, you might never even see an Aztec wallet at all, because the app itself becomes your interface that you connect to using your MetaMask wallet.

Fee Juice in Browser Wallets

If you're using a browser extension like Azguard, you'll manage Fee Juice directly in your wallet alongside your private and public balances, converting between tokens as needed to cover transaction costs.

When you bridge tokens in, you'll need enough Fee Juice to cover the cost of your first transaction, then you'll need to monitor how much Fee Juice you have available to make transactions. Browser wallets will allow you to send either publicly or privately to other users and will default to using either public or private Fee Juice depending on the type of transaction. Both private Fee Juice and public Fee Juice will appear by default in your token list.

Wrapping up

How you handle Fee Juice depends on where you're transacting: apps can abstract it away entirely and let you pay in any token, while a browser wallet like Azguard puts it in your hands to manage across public and private balances. Match your gas to your transaction, keep private activity private down to the fee, and you move on your terms.

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.