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What is Zero Knowledge Machine Learning (ZKML)?

Zero knowledge and machine learning. That is the new hot trend that is emerging not only within the cryptocurrency world, but also in different industries as such. It is a rather intriguing blend of two technologies that have sparked a lot of interest on their own from cryptocurrency companies, organisations and developers.

What is this technological union of zero knowledge and machine learning? What it offers, how can it be used within different industries and how can it become part of the cryptocurrency world? Those and other questions will be answered within the next few paragraphs. But before we do that, let’s remind ourselves what zero knowledge is.

What is zero knowledge (ZK)?

Zero-knowledge is a cryptographic concept that refers to the ability to prove the validity of a statement or claim without revealing any additional information beyond the fact that the statement is true. In other words, it allows one party, called the prover, to convince another party, called the verifier, that a certain statement is true, without revealing any knowledge or details about the statement other than its truthfulness.

The idea behind zero-knowledge is to provide a method for proving knowledge or the truth of a statement without disclosing any unnecessary information. This concept has important implications for privacy and security, as it allows for the authentication and verification of data or claims without exposing sensitive or confidential information.

To achieve zero-knowledge, interactive proof systems are typically used. In these systems, the prover and verifier engage in a series of interactions where the prover demonstrates the validity of the statement. Through these interactions, the verifier gains confidence in the truth of the statement without learning any additional information that could be used to reproduce or mimic the proof.

Currently, zero-knowledge proofs (ZKPs) are implemented in the cryptocurrency world to enhance privacy and security. Some applications include:

  • Confidential transactions: ZKPs hide transaction amounts while ensuring validity (e.g., Zcash).
  • Privacy coins: ZKPs like ring signatures obfuscate sender identities (e.g., Monero, Dash).
  • zk-SNARKs: ZKPs for efficient and private verification of transactions and smart contracts (e.g., Zcash, Ethereum).
  • Decentralized exchanges: ZKPs enable trustless and private trading without revealing details.
  • Identity and authentication: ZKPs offer secure and private authentication systems.
  • Scalability and verification: ZKPs improve scalability by aggregating transactions (e.g., zk-rollups).

These implementations enhance privacy, security, and scalability in the cryptocurrency space. There are countless teams currently working on improving different zero knowledge technologies. 

Some of them are more concerned with the use cases of it, while others try to tackle the availability of this technology to the masses. It is however expected that as ZK matures, less powerful machines will be able to prove bigger models in a shorter time period. This would mostly be thanks to improvements in specialised hardware.

Is there a need for ZKML?

This would however also allow the union of zero knowledge and machine learning. Machine learning can benefit from zero knowledge on personal data level, where the user would be able to obtain the outcome of the model inference on the given data without having to disclose their input to anyone, for instance.

What does the combination of ZKML bring to the table. Source:

This is one of the examples of why the zero knowledge machine learning models and applications are getting more traction. In general, ZKML would thus solve for the need of verifying sensitive information between parties, without actually having to share them. The desire to hide an input would be one of the main drivers of the advancement of this technology.

The best example from real life would be the healthcare sector. ZKML can be utilised in the field for disease prediction thanks to running ML models over sensitive medical data. This would lead not only to better efficiency in diagnosis, but also ensure that the sensitive data of patients remain private.

However, for the correct output of any information, the downstream entities that use the machine learning models would need to be certain that the input was correct, as the incorrect input would lead to incorrect output (garbage in, garbage out). And thus, the ZK can be used to benefit both parties and satisfy their seemingly contradictory demands.

Use cases of ZKML in the real world

When looking at the combination of zero knowledge proofs and machine learning, there are several potential areas where this blend of technologies can be beneficial and where it can be applied. These include:

  • Model authenticity: Zero-knowledge proofs can verify that a machine learning model being accessed through an API is indeed the intended one, preventing fraudulent substitution of models. This is achieved through frameworks like functional commitments and SNARK-based zero-knowledge commitment schemes.
  • Model integrity: Zero-knowledge proofs can ensure that the same machine learning algorithm is applied uniformly to different users’ data. This is particularly useful in sensitive domains like credit scoring or medical applications, where biases should be avoided. Functional commitments can be employed to verify that the model ran with the committed parameters for each user’s data.
  • Attestations: Zero-knowledge proofs can integrate attestations from external verified parties into on-chain models or smart contracts. By verifying digital signatures using zero-knowledge proofs, it is possible to ensure the authenticity and provenance of information, such as images or sensor data.
  • Decentralized inference or training: Zero-knowledge proofs can enable decentralized machine learning inference or training. Existing models can be deployed on-chain, and zero-knowledge proofs can be used to compress the models. Projects like EZKL and Gensyn are developing methods for verifying and training models on-chain using zero-knowledge proofs.
  • Proof of personhood: Zero-knowledge proofs can verify that an individual is unique without revealing personal information. For example, biometric scanning or encrypted submission of government ID can be used for verification, and zero-knowledge proofs can confirm the verification without exposing identity details.

Use cases of ZKML in the crypto world

Obviously, not all of the previously mentioned use cases could be directly applied to the cryptocurrency world. That, however, does not mean that this new emerging technology could not benefit this sector. On the contrary, analysts and experts have already listed several possible areas, where this technology could come in handy.

  • Decentralized finance (DeFi): ZKML could be used to validate yield-maximising strategies or rebalancing of pools for the users.
  • Gaming: ZKML could be used to validate betting mechanisms or players.
  • Identity: ZKML could be used to perform AI analysis of the biometric information of the user.

The last point of the usage of ZKML within the identity/ID sector can probably be best explained with the example of Worldcoin. With Worldcoin, the users have to scan their iris to provide their “proof-of-personhood.” With ZKML, they would be able to hold their own biometrics in encrypted storage, for instance on their mobile phone. 

Upon downloading of the ML model for the iris code generation, they would be able to create a ZKP locally right on their device or within their account and prove their iris code was generated using the correct models. It also possesses benefits with iris code upgradeability, however, that is a much more complex and even more advanced topic.

Examples of crypto projects working on or using ZKML. Source:

While this technology is still only in its infancy, there are already numerous projects that are looking at it and how it can be implemented not only within the cryptocurrency sector, but in other industries as well. Besides the above mentioned Worldcoin, these include for instance Giza, Modulus Labs, Axiom, Secret Network or Oasis Network.


Zero knowledge machine learning is without any doubt an intriguing concept that will be explored more in depth in the coming months. However, it is still a very new concept that will need a lot of time to prove that it is really usable in the real world or at least in the world of cryptocurrencies. Without it, it could potentially quickly become just another overhyped technological innovation that has no usability or need.


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