Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach

TL;DR

This paper introduces a privacy-boosting framework for differential privacy that improves utility while controlling privacy loss, especially effective with high-sensitivity queries, through a novel approach compatible with existing DP mechanisms.

Contribution

The paper proposes a new privacy-boosting framework that enhances utility constraints in differential privacy by adjusting output likelihoods and privacy loss, applicable to various DP mechanisms.

Findings

Achieves lower privacy loss than standard DP mechanisms under utility constraints.

Effective in scenarios with large query sensitivity.

Provides a flexible approach for utility-constrained differential privacy.

Abstract

Data engineering often requires accuracy (utility) constraints on results, posing significant challenges in designing differentially private (DP) mechanisms, particularly under stringent privacy parameter $\epsilon$. In this paper, we propose a privacy-boosting framework that is compatible with most noise-adding DP mechanisms. Our framework enhances the likelihood of outputs falling within a preferred subset of the support to meet utility requirements while enlarging the overall variance to reduce privacy leakage. We characterize the privacy loss distribution of our framework and present the privacy profile formulation for $(\epsilon,\delta)$-DP and R\'enyi DP (RDP) guarantees. We study special cases involving data-dependent and data-independent utility formulations. Through extensive experiments, we demonstrate that our framework achieves lower privacy loss than standard DP mechanisms under utility constraints. Notably, our approach is particularly effective in reducing privacy loss with large query sensitivity relative to the true answer, offering a more practical and flexible approach to designing differentially private mechanisms that meet specific utility constraints.