AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation

TL;DR

AMPPERE introduces a universal abstract model for privacy-preserving entity resolution that is platform-agnostic, enabling two parties to match data without revealing sensitive information, with demonstrated feasibility and performance analysis.

Contribution

It presents a novel abstract computation model, AMPPERE, for universal privacy-preserving entity resolution applicable across multiple platforms and approaches.

Findings

Effective privacy-preserving entity resolution demonstrated on real-world data

Performance overhead characterized for different approaches on MPC and homomorphic encryption platforms

Provides a formal analysis of privacy guarantees and computational costs

Abstract

Entity resolution is the task of identifying records in different datasets that refer to the same entity in the real world. In sensitive domains (e.g. financial accounts, hospital health records), entity resolution must meet privacy requirements to avoid revealing sensitive information such as personal identifiable information to untrusted parties. Existing solutions are either too algorithmically-specific or come with an implicit trade-off between accuracy of the computation, privacy, and run-time efficiency. We propose AMMPERE, an abstract computation model for performing universal privacy-preserving entity resolution. AMPPERE offers abstractions that encapsulate multiple algorithmic and platform-agnostic approaches using variants of Jaccard similarity to perform private data matching and entity resolution. Specifically, we show that two parties can perform entity resolution over their data, without leaking sensitive information. We rigorously compare and analyze the feasibility, performance overhead and privacy-preserving properties of these approaches on the Sharemind multi-party computation (MPC) platform as well as on PALISADE, a lattice-based homomorphic encryption library. The AMPPERE system demonstrates the efficacy of privacy-preserving entity resolution for real-world data while providing a precise characterization of the induced cost of preventing information leakage.