ZKML

zkML is an innovative approach that combines zero-knowledge proofs with machine learning, ensuring secure and private AI insights and verification without exposing sensitive data. It's particularly crucial in sectors like healthcare and finance where privacy is paramount. zkML operates by training machine learning models across nodes, each holding a segment of data, then generating cryptographic proofs that verify aspects of the data without revealing it.

This technology is still emerging, focusing on integrating zero-knowledge proofs into the model inference phase, which is effective for verifying outputs of AI models like GPT-4 or DALL-E 2 without exposing sensitive inputs. Although challenges remain in managing large models due to computational constraints, significant strides have been made, such as Modulus Labs' success in creating proofs for a model with 18 million parameters.