Zero-Knowledge Machine Learning: Use Cases & Benefits
This article is a summary of a YouTube video "Dcbuilder - Zero-Knowledge Machine Learning and its use cases" by [EthCC] Livestream 4
TLDR Zero-knowledge machine learning, which combines fully homomorphic encryption, verifiable computation, and privacy, allows for the proof of computation within a machine learning model while hiding certain parts of the computation, opening up new possibilities for privacy-preserving machine learning and enhancing efficiency and adaptability in various fields such as blockchain networks, financial transactions, and yield routing.
Zero-knowledge cryptography allows for the verification of state transitions in blockchain networks, reducing the need for re-executing transactions on all computers in the network.
Zero-knowledge machine learning focuses on proof of inference, which involves using the trained model to perform tasks, potentially revolutionizing the field of machine learning.
Zero-Knowledge Machine Learning (zkml) combines fully homomorphic encryption, verifiable computation, and privacy, enabling the proof of computation within a machine learning model while hiding certain parts of the computation.
ZK technology has reached a stage where it is now possible to create proofs of machine learning models, opening up new possibilities for privacy-preserving machine learning.
Zero-knowledge machine learning can enable the use of computationally expensive machine learning models on the blockchain by creating proofs of running these models off-chain.
Zero-Knowledge Machine Learning can be used to detect fraudulent transactions and anomalies, providing a safer environment for financial transactions.
By providing proofs that a machine learning model has generated a new set of parameters, the risk parameters within DeFi protocols can be updated accordingly, enhancing efficiency and adaptability.
The use of a hidden model in privacy-preserving protocols can ensure the best yield routing without compromising the privacy of the data.