Privacy-Preserving Inconsistency Measurement

TL;DR

This paper introduces methods for measuring inconsistency between knowledge bases of multiple agents in a privacy-preserving manner using secure multi-party computation, ensuring input privacy.

Contribution

It presents two concrete cryptographic protocols for quantifying inconsistency without revealing knowledge base contents, advancing privacy-preserving multi-party data analysis.

Findings

Protocols satisfy input privacy requirements

Methods effectively measure inconsistency without data disclosure

Protocols are practical for multi-party knowledge base comparison

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.