We propose a novel mechanism for answering sets of counting 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 ( , δ)-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.
Given a series of counting queries, the mechanism proposed by Li and Miklau REF selected a subset of queries to answer privately and used their noisy answers to derive answers for the remaining queries.
An Adaptive Mechanism for Accurate Query Answering under Differential Privacy
PVLDB
PVLDB
Computer Science