Secure Machine Learning

Definition ∞ Secure machine learning is a domain of artificial intelligence that emphasizes the protection of data and models regarding confidentiality, integrity, and accessibility across the entire learning cycle. This employs methodologies such as federated learning, homomorphic encryption, and differential privacy to shield sensitive information during model training and prediction. It holds central importance for implementing AI in privacy-conscious blockchain contexts. This discipline ensures data protection during computational processes.
Context ∞ The subject of secure machine learning frequently appears in cryptocurrency news, particularly in discussions about privacy-preserving artificial intelligence on blockchain and the utilization of distributed data. Reports often detail breakthroughs in confidential computation, verifiable AI, and the application of zero-knowledge proofs to machine learning algorithms. The convergence of secure ML with blockchain systems is viewed as a vital progression for constructing reliable and privacy-adherent decentralized AI frameworks.