PAC to the Future: Zero-Knowledge Proofs of PAC Private Systems

2026-02-12

AI Summary

This work addresses the challenge of simultaneously ensuring computational correctness and regulatory compliance of privacy mechanisms in untrusted computing environments. We propose the first framework that integrates Probably Approximately Correct (PAC) privacy with non-interactive zero-knowledge proofs (ZKPs), enabling verifiable guarantees—without revealing system internals—that a machine learning computation was executed correctly and that privacy-preserving noise was properly applied. By bridging the gap between formal privacy guarantees and cryptographic verification, our approach overcomes the longstanding limitation of traditional privacy-preserving techniques, which lack mechanisms for external validation. The resulting system achieves verifiable PAC privacy in cloud settings, establishing a new paradigm for privacy-preserving machine learning and database systems that jointly ensures security, integrity, and practical deployability.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

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.

Problem

• PAC Privacy
• Zero-Knowledge Proofs
• Verifiable Privacy
• Trustless Computing
• Privacy-Preserving Machine Learning

Innovation

• PAC Privacy
• Zero-Knowledge Proofs
• Verifiable Privacy
• Non-interactive ZKP
• Trustless Computation