Identification with Encrypted Biometric Data

TL;DR

This paper presents a novel biometric identification method that preserves privacy by using encrypted data and combines Bloom Filters with hashing techniques to handle fuzzy matching efficiently.

Contribution

It introduces the first non-trivial scheme for biometric identification that operates on encrypted, error-tolerant data in a Hamming space.

Findings

Achieves privacy-preserving biometric identification

Handles fuzzy matching in encrypted data efficiently

Combines Bloom Filters with Locality-Sensitive Hashing

Abstract

Biometrics make human identification possible with a sample of a biometric trait and an associated database. Classical identification techniques lead to privacy concerns. This paper introduces a new method to identify someone using his biometrics in an encrypted way. Our construction combines Bloom Filters with Storage and Locality-Sensitive Hashing. We apply this error-tolerant scheme, in a Hamming space, to achieve biometric identification in an efficient way. This is the first non-trivial identification scheme dealing with fuzziness and encrypted data.