Zero-Knowledge Machine Learning

Zero-knowledge machine learning is a cryptographic technique that facilitates the verification of machine learning models on blockchain protocols without disclosing the underlying computations or data.

What Is Zero-Knowledge Machine Learning?

Machine learning (ML) models typically require intensive computations, demanding vast amounts of data processing capabilities to simulate human decision-making and adaptability. And when it comes to verifying such data amounts on blockchains, the on-chain operations become expensive. This underscores the need for a verifiable methodology for establishing trust in ML models, especially where accuracy, privacy, and transparency are paramount. This is where zero-knowledge machine learning comes in.

Zero-knowledge machine learning (zkML) is a technology that uses cryptography to verify ML algorithms and their results, all while using zero-knowledge proofs to keep the input data private.

Technically speaking, zkML creates a certificate or cryptographic “receipt” that verifies an ML algorithm’s inference (the process of computing outcomes from a given dataset on an AI or ML model). This certificate contains details such as model size and parameters, confirming that a certain computation has been done while hiding sensitive information from the verifiers (the parties validating the computation and outcome).

In summary, a zkML protocol allows the party calculating an AI model’s output to also produce a cryptographic proof of the computation. By utilizing ZKPs, ML calculations can be performed off-chain while retaining a method for on-chain verifiability.

What Are the Use Cases of zkML in Crypto?

This technology can be used for:

• Decentralized advertisements and marketing – zkML can analyze user behavior and content preferences without disclosing the actual content. As a result, it can enable personalized experiences, recommendations, and ad targeting on decentralized applications while maintaining user privacy. By using blockchain for data distribution, the consumers also retain complete control over their personal information.
• Transform DeFi – Through verifiable data and smart contracts, zkML allows DeFi platforms to perform sophisticated trading strategies and risk analysis while improving the overall user experience. As such, its applications in DeFi can include dynamic spread computations in automated market makers (AMMs) for liquidity protection, on-chain derivatives pricing models, and automated risk assessment.
• Revolutionize blockchain gaming – It can also be used on on-chain gaming apps to create intelligent non-player characters and AI-driven gameplay. This enhances the user experience and increases game complexity. For example, you can train your fighter using AI models and deploy them in battles.
• Enhance identity verification (IDV) – zkML can help ensure users submit a ZKP that validates their scanned personal identity documents to an IDV-based protocol or service.