Information Theoretic-Based Privacy Protection on Data Publishing and Biometric Authentication

Abstract

We are interested in providing privacy protection for applications that involve sensitive personal data. In particular, we focus on controlling information leakages in two scenarios: data publishing and biometric authentication. In both scenarios, we seek to extend the existing privacy protection techniques to cater for some commonly deployed setting. We seek privacy protection techniques that are based on information theoretic analysis, which provide unconditional guarantee on the amount of information leakage. We extend biometric protection mechanisms to cater for asymmetric setting and multi-factor setting. The extensions provide better privacy protections with respect to the remaining entropy of the biometric secret. We also give a differentially private mechanism for publishing pointset data. We proposed a notion of delta-neighbourhood that can be more appropriate under certain scenarios, and we give constructions for spatial dataset and temporal dataset which the notion can provide a good tradeoff for better utility.