Developing Non-Stochastic Privacy-Preserving Policies Using Agglomerative Clustering
Ni Ding, Farhad Farokhi
IEEE Transactions on Information Forensics and Security | Institute of Electrical and Electronics Engineers | Published : 2020
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..View full abstract
The work of Ni Ding was supported by the Doreen Thomas Postdoctoral Fellowship at The University of Melbourne. The work of Farhad Farokhi was supported by the Melbourne School of Engineering, The University of Melbourne. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Marina Blanton.