🥙ZKML

zero-knowledge mechine learning

AOS (AI Optimistic Sampling) is an AI inference verification and sampling network designed for the Hetu protocol on Eigenlayer. It leverages the power of ZKML to bring enhanced security and efficient verification services to AI networks operating within the OPML (Optimistic Machine Learning) framework.By integrating ZKML capabilities, AOS ensures that the off-chain computations and resulting machine learning models adhere to the strictest standards of privacy and security. It employs advanced zero-knowledge proof techniques to verify the integrity and correctness of AI inferences, providing an additional layer of trust and transparency to the decentralized AI services facilitated by the OPML approach.

Introduction

Zero-Knowledge Machine Learning (ZKML) is a cutting-edge technology that combines the principles of zero-knowledge proofs (ZKPs) with machine learning techniques. It enables the training and inference of machine learning models without revealing the underlying data or model parameters, ensuring privacy and confidentiality.

What is ZKML?

ZKML leverages secure multi-party computation (MPC) and homomorphic encryption techniques to perform computations on encrypted data without decrypting it. This allows for the collaborative training of machine learning models across multiple parties while preserving the privacy of their individual data contributions.

Applications and Use Cases

Privacy-Preserving Machine Learning

One of the primary applications of ZKML is in privacy-preserving machine learning scenarios. By leveraging ZKML, organizations can benefit from the predictive power of machine learning models while maintaining the confidentiality of sensitive data, such as financial transactions or medical records.

Trustless Collaboration

ZKML enables secure and trustless collaboration between multiple parties in a decentralized ecosystem. Parties can contribute their data to train a global model without revealing their individual data points, and the resulting model can be verified as accurate using ZKPs.

Decentralized Oracles

ZKML has potential applications in decentralized oracles, where machine learning models can be used to provide off-chain data to smart contracts in a trustless and verifiable manner.

Decentralized Identity Management

ZKML can help protect sensitive personal information while still enabling authentication and access control in decentralized identity management systems.

Advantages and Benefits

• Privacy and confidentiality: ZKML ensures that the underlying data and model parameters remain private during training and inference.

• Trustless collaboration: Parties can collaborate on training machine learning models without revealing their individual data contributions.

• Verifiable results: The accuracy of the resulting machine learning models can be verified using ZKPs, promoting trust and transparency.

• Decentralized computation: ZKML enables decentralized and privacy-preserving computation, aligning with the principles of blockchain and decentralized technologies.

Conclusion

Zero-Knowledge Machine Learning (ZKML) is a groundbreaking technology that addresses privacy and confidentiality concerns in machine learning while enabling trustless collaboration and decentralized computation. By combining ZKPs with machine learning techniques, ZKML unlocks new possibilities for secure and transparent data analysis and decision-making in various domains, including blockchain and decentralized applications.