Anonymized distance filter in hamming space

Abstract

Search algorithms typically involve intensive distance computations and comparisons. In privacy-aware applications such as biometric identification, exposing the distance information may lead to compromise of sensitive data that have privacy and security implications. In this paper, we design an anonymized distance filter that can test and rank instances in a Hamming-ball search without knowing explicit distance values. We demonstrate the effectiveness of our method on both simulated and real data sets in the context of biometric identification.