An Adaptive Mechanism for Accurate Query Answering under Differential Privacy

TL;DR

This paper introduces an adaptive mechanism for answering counting queries under differential privacy, optimizing accuracy by selecting and answering strategy queries to derive workload answers with minimal error.

Contribution

It presents a novel algorithm that approximates the optimal strategy for any workload of linear counting queries, improving accuracy without additional privacy cost.

Findings

Achieves near-optimal error for many workloads.

Significantly outperforms prior approaches.

Works under (, )-differential privacy.

Abstract

We propose a novel mechanism for answering sets of count- ing queries under differential privacy. Given a workload of counting queries, the mechanism automatically selects a different set of "strategy" queries to answer privately, using those answers to derive answers to the workload. The main algorithm proposed in this paper approximates the optimal strategy for any workload of linear counting queries. With no cost to the privacy guarantee, the mechanism improves significantly on prior approaches and achieves near-optimal error for many workloads, when applied under (\epsilon, \delta)-differential privacy. The result is an adaptive mechanism which can help users achieve good utility without requiring that they reason carefully about the best formulation of their task.