Developing Non-Stochastic Privacy-Preserving Policies Using Agglomerative Clustering

Abstract

We consider a non-stochastic privacy-preserving problem in which an adversary aims to infer sensitive information S from publicly accessible data X without using statistics. We consider the problem of generating and releasing a quantization X^ of X to minimize the privacy leakage of S to X^ while maintaining a certain level of utility (or, inversely, the quantization loss). The variables S and X are treated as bounded and non-probabilistic, but are otherwise general. We consider two existing non-stochastic privacy measures, namely the maximum uncertainty reduction L0(S→X^) and the refined information I∗(S;X^) (also called the maximin information) of S . For each privacy measure, we propose a..