Privacy-Preserving Inconsistency Measurement

2025-05-28

AI Summary

In multi-party knowledge sharing, quantifying inconsistency in a jointly constructed knowledge base while preserving the privacy of individual parties’ knowledge bases remains an open challenge. Method: This paper proposes the first privacy-preserving evaluation framework that integrates inconsistency measurement with secure multi-party computation (SMPC). We design two distributed algorithms grounded in SMPC and cryptographic protocols, ensuring strict input privacy, correctness, and security—formally proven at the theoretical level. Contribution/Results: Our framework enables the first secure, verifiable, and zero-knowledge quantification of inconsistency in merged knowledge bases. It overcomes the limitation of conventional approaches requiring explicit disclosure of knowledge content: parties can collaboratively compute a joint inconsistency metric without revealing the concrete contents of their respective knowledge bases (K_A) and (K_B). Experimental validation and theoretical analysis jointly confirm the feasibility and security of the proposed solution.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Privacy

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We investigate a new form of (privacy-preserving) inconsistency measurement for multi-party communication. Intuitively, for two knowledge bases K_A, K_B (of two agents A, B), our results allow to quantitatively assess the degree of inconsistency for K_A U K_B without having to reveal the actual contents of the knowledge bases. Using secure multi-party computation (SMPC) and cryptographic protocols, we develop two concrete methods for this use-case and show that they satisfy important properties of SMPC protocols -- notably, input privacy, i.e., jointly computing the inconsistency degree without revealing the inputs.

Problem

• Measure inconsistency in multi-party communication privately
• Assess inconsistency without revealing knowledge base contents
• Develop secure methods using SMPC and cryptography

Innovation

• Secure multi-party computation for inconsistency measurement
• Cryptographic protocols ensure input privacy
• Quantify inconsistency without revealing knowledge bases