Points of the local optimal privacy utility tradeoff

Abstract

With the increasing prevalence of data sharing and publishing, striking a balance between data privacy and data utility, known as the privacy utility tradeoff problem, has emerged as a core challenge. Recent studies treat this tradeoff as an optimization process within the privacy protection process for certain privacy protection mechanism. However, the ability to achieve an optimal tradeoff is inherently constrained by the chosen privacy protection mechanism. In this paper, we provide a new perspective by conceptualizing the privacy utility tradeoff as a series of distinct “tradeoff points,” where the inference privacy and inference utility serve as the components to represent a tradeoff point. To identify local optimal tradeoff points, we first select those that maximize utility for a given level of privacy. Then, we discard those points that do not ensure optimal privacy for the corresponding utility. Simulations on four real-world datasets using three state-of-the-art methods demonstrate that existing tradeoff solutions are limited by their underlying privacy mechanisms, while our solution helps integrate local optimal tradeoff points into the design of privacy protection mechanisms.