Multi random projection inner product encryption, applications to proximity searchable encryption for the iris biometric

Abstract

Biometric databases collect people's information and perform proximity search (finding records within bounded distance of the query) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric.Prior work proposed to build proximity search from inner product functional encryption (Kim et al., SCN 2018). This work identifies and closes two gaps in this approach:1.Biometrics use long vectors, often with thousands of bits. Many inner product encryption schemes have to invert a matrix whose dimension scales with this size. Setup is then not feasible on commodity hardware. We introduce a technique that improves setup efficiency without harming accuracy.2.Prior approaches leak distance between queries and all stored records. We propose a construction from function hiding, predicate, inner product encryption (Shen et al., TCC 2009) that avoids this leakage.Finally, we show that our scheme can be instantiated using symmetric pairing groups, which improves search efficiency.