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Published November 2018 | Submitted
Journal Article Open

Robust Privacy-Utility Tradeoffs Under Differential Privacy and Hamming Distortion

Abstract

A privacy-utility tradeoff is developed for an arbitrary set of finite-alphabet source distributions. Privacy is quantified using differential privacy (DP), and utility is quantified using expected Hamming distortion maximized over the set of distributions. The family of source distribution sets (source sets) is categorized into three classes, based on different levels of prior knowledge they capture. For source sets whose convex hull includes the uniform distribution, symmetric DP mechanisms are optimal. For source sets whose probability values have a fixed monotonic ordering, asymmetric DP mechanisms are optimal. For all other source sets, general upper and lower bounds on the optimal privacy leakage are developed and necessary and sufficient conditions for tightness are established. Differentially private leakage is an upper bound on mutual information leakage: the two criteria are compared analytically and numerically to illustrate the effect of adopting a stronger privacy criterion.

Additional Information

© 2018 IEEE. Manuscript received July 21, 2017; revised January 22, 2018 and April 5, 2018; accepted April 6, 2018. Date of publication April 30, 2018; date of current version May 23, 2018. The work of K. Kalantari and L. Sankar was supported by the National Science Foundation under Grant CIF-1422358. The work of A. D. Sarwate was supported in part by the National Science Foundation under Grant CCF-1453432 and Grant SaTC-1617849, in part by DARPA, and in part by SSC Pacific under Grant N66001-15-C-4070. This paper was presented in part at the 52nd Annual Allerton Conference [1] and in part at the 2016 IEEE International Symposium on Information Theory [2]. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Tobias Oechtering.

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September 22, 2023
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