Multimodal Privacy-Preserving Entity Resolution with Fully Homomorphic Encryption

TL;DR

This paper presents a new multimodal framework for privacy-preserving entity resolution that handles large, heterogeneous datasets while ensuring data confidentiality using fully homomorphic encryption.

Contribution

It introduces a novel cryptographic approach enabling accurate entity matching across diverse data sources without exposing sensitive information.

Findings

Achieves low equal error rate in entity resolution

Maintains data confidentiality with cryptographic guarantees

Handles large-scale, heterogeneous datasets efficiently

Abstract

The canonical challenge of entity resolution within high-compliance sectors, where secure identity reconciliation is frequently confounded by significant data heterogeneity, including syntactic variations in personal identifiers, is a longstanding and complex problem. To this end, we introduce a novel multimodal framework operating with the voluminous data sets typical of government and financial institutions. Specifically, our methodology is designed to address the tripartite challenge of data volume, matching fidelity, and privacy. Consequently, the underlying plaintext of personally identifiable information remains computationally inaccessible throughout the matching lifecycle, empowering institutions to rigorously satisfy stringent regulatory mandates with cryptographic assurances of client confidentiality while achieving a demonstrably low equal error rate and maintaining computational tractability at scale.