Zero Knowledge

Zero-knowledge proofs (ZKPs) are cryptographic techniques allowing verification of a statement's truth without revealing any underlying information. Current research focuses on applying ZKPs to enhance the security and privacy of machine learning, particularly in federated learning, by verifying model integrity, fairness, and the authenticity of outputs without compromising sensitive data or model parameters. This is achieved through various algorithms and architectures, including zk-SNARKs and specialized protocols for specific machine learning operations like attention mechanisms and gradient aggregation. The widespread adoption of ZKPs holds significant potential for building trust in AI systems, improving data security in collaborative settings, and enabling verifiable model evaluations.