Enhancing Utility in the Watchdog Privacy Mechanism

TL;DR

This paper improves the data utility of the privacy watchdog method by partitioning high-risk symbols and privatizing each subset individually, using an agglomerative merging algorithm to optimize privacy and utility trade-offs.

Contribution

It introduces a novel partitioning approach and an agglomerative merging algorithm to enhance data utility in the privacy watchdog framework.

Findings

The proposed algorithm achieves higher utility than uniform merging.

Numerical simulations validate the effectiveness of the partitioning method.

Enhanced privacy-utility trade-off demonstrated through experiments.

Abstract

This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog method filters out the high-risk data symbols through applying a uniform data regulation scheme, e.g., merging all high-risk symbols together. While this method entirely trades the symbols resolution off for privacy, we show that the data utility can be greatly improved by partitioning the high-risk symbols set and individually privatizing each subset. We further propose an agglomerative merging algorithm that finds a suitable partition of high-risk symbols: it starts with a singleton high-risk symbol, which is iteratively fused with others until the resulting subsets are private.~Numerical simulations demonstrate the efficacy of this algorithm in privately achieving higher utilities in the watchdog scheme.