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This paper introduces a framework for verifiable privacy in machine learning by combining PAC privacy with zero-knowledge proofs (ZKPs). It enables users to verify the correctness of computations and the application of privacy-preserving noise in cloud-based systems. The authors leverage non-interactive ZKP schemes to generate proofs attesting to the correct implementation of PAC privacy mechanisms, demonstrating the feasibility of verifiable PAC privacy in outsourced computation.
Finally, a way to verify that privacy-preserving noise was actually applied in outsourced machine learning computations, using zero-knowledge proofs.
Privacy concerns in machine learning systems have grown significantly with the increasing reliance on sensitive user data for training large-scale models. This paper introduces a novel framework combining Probably Approximately Correct (PAC) Privacy with zero-knowledge proofs (ZKPs) to provide verifiable privacy guarantees in trustless computing environments. Our approach addresses the limitations of traditional privacy-preserving techniques by enabling users to verify both the correctness of computations and the proper application of privacy-preserving noise, particularly in cloud-based systems. We leverage non-interactive ZKP schemes to generate proofs that attest to the correct implementation of PAC privacy mechanisms while maintaining the confidentiality of proprietary systems. Our results demonstrate the feasibility of achieving verifiable PAC privacy in outsourced computation, offering a practical solution for maintaining trust in privacy-preserving machine learning and database systems while ensuring computational integrity.