Wyatt Benno, Alberto Centelles, Antoine Douchet, Khalil Gibran
Jolt Atlas introduces a zero-knowledge machine learning framework that efficiently verifies model inference using a lookup-centric approach, supporting privacy and security in various applications.
Jolt Atlas is a new framework that allows machine learning models to be verified in a way that keeps the data private and secure. It uses a method called zero-knowledge proofs, which means you can prove something is true without revealing any details about it. This framework is particularly useful for ensuring privacy in situations where you might not trust the environment, such as on shared devices or in competitive settings. Jolt Atlas works efficiently even in environments with limited memory and does not require special hardware, making it accessible and practical for a wide range of applications.
