Paper 2023/736

Private Eyes: Zero-Leakage Iris Searchable Encryption

Abstract

This work introduces Private Eyes, the first zero-leakage biometric database. The only leakage of the system is unavoidable: 1) the log of the dataset size and 2) the fact that a query occurred. Private Eyes is built from oblivious symmetric searchable encryption. Approximate proximity queries are used: given a noisy reading of a biometric, the goal is to retrieve all stored records that are close enough according to a distance metric. Private Eyes combines locality sensitive-hashing or LSHs (Indyk and Motwani, STOC 1998) and oblivious maps which map keywords to values. One computes many LSHs of each record in the database and uses these hashes as keywords in the oblivious map with the matching biometric readings concatenated as the value. At search time with a noisy reading, one computes the LSHs and retrieves the disjunction of the resulting values from the map. The underlying oblivious map needs to answer disjunction queries efficiently. We focus on the iris biometric which requires a large number of LSHs, approximately $1000$. Boldyreva and Tang's (PoPETS 2021) design yields a suitable map for a small number of LSHs (their application was in zero-leakage $k$-nearest-neighbor search). Our solution is a zero-leakage disjunctive map designed for the setting when most clauses do not match any records. For the iris, on average at most $6\%$ of LSHs match any stored value. We evaluate using the ND-0405 dataset; this dataset has $356$ irises suitable for testing. To scale our evaluation, we use a generative adversarial network to produce synthetic irises. Accurate statistics on sizes beyond available datasets is crucial to optimizing the cryptographic primitives. This tool may be of independent interest. For the largest tested parameters of a $5000$ synthetic iris database, a search requires $18$ rounds of communication and $25$ms of parallel computation. Our scheme is implemented and open-sourced.

Note: Efficiency updates