Engineering Methods for Differentially Private Histograms: Efficiency Beyond Utility

TL;DR

This paper evaluates differentially private histogram methods focusing on both utility and efficiency, providing optimized combinations and practical guidance for time-critical applications.

Contribution

It introduces a novel experimental evaluation framework that considers efficiency alongside utility, optimizing and combining existing scheme components for better trade-offs.

Findings

Existing schemes share common components and can be optimized.

Novel block combinations improve efficiency-utility trade-offs.

Schemes derived from these combinations are best for time-critical applications.

Abstract

Publishing histograms with $\epsilon$-differential privacy has been studied extensively in the literature. Existing schemes aim at maximizing the utility of the published data, while previous experimental evaluations analyze the privacy/utility trade-off. In this paper we provide the first experimental evaluation of differentially private methods that goes beyond utility, emphasizing also on another important aspect, namely efficiency. Towards this end, we first observe that all existing schemes are comprised of a small set of common blocks. We then optimize and choose the best implementation for each block, determine the combinations of blocks that capture the entire literature, and propose novel block combinations. We qualitatively assess the quality of the schemes based on the skyline of efficiency and utility, i.e., based on whether a method is dominated on both aspects or not. Using exhaustive experiments on four real datasets with different characteristics, we conclude that there are always trade-offs in terms of utility and efficiency. We demonstrate that the schemes derived from our novel block combinations provide the best trade-offs for time critical applications. Our work can serve as a guide to help practitioners engineer a differentially private histogram scheme depending on their application requirements.