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Robust Privatization with Multiple Tasks and the Optimal Privacy-Utility Tradeoff by Liu, Ta-Yuan; Wang, I-Hsiang is a scholarly article available to read on EtoBox.

What is Robust Privatization with Multiple Tasks and the Optimal Privacy-Utility Tradeoff about?

In this work, fundamental limits and optimal mechanisms of privacy-preserving data release that aims to minimize the privacy leakage under utility constraints of a set of multiple tasks are investigated. While the private feature to be protected is typically determined and known by the sanitizer, the target task is usually unknown. To address the lack of information on the specific task, utility constraints laid on a set of multiple possible tasks are considered. The mechanism protects the specific privacy feature of the to-be-released data while satisfying utility constraints of all possible tasks in the set. First, the single-letter characterization of the rate-leakage-distortion region is derived, where the utility of each task is measured by a distortion function. It turns out that the minimum privacy leakage problem with log-loss distortion constraints and the unconstrained released rate is a non-convex optimization problem. Second, focusing on the case where the raw data consists of multiple independent components, we show that the above non-convex optimization problem can be decomposed into multiple parallel privacy funnel (PF) problems with different weightings. We explicit

Author

Liu, Ta-Yuan; Wang, I-Hsiang

Published

2020

Language

EN