Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy

TL;DR

This paper introduces the low-rank mechanism (LRM), a practical approach for answering batch linear queries under differential privacy, significantly improving accuracy over existing methods for both exact and approximate privacy guarantees.

Contribution

The paper presents the first practical, high-accuracy differentially private method for batch linear query processing, applicable to both exact and approximate privacy models.

Findings

LRM outperforms state-of-the-art solutions in accuracy.

Extensive experiments confirm the effectiveness of LRM.

Guidelines provided for setting privacy parameters.

Abstract

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an appropriate strategy, processing a batch of correlated queries as a whole achieves considerably higher accuracy than answering them individually. However, to our knowledge there is currently no practical solution to find such a strategy for an arbitrary query batch; existing methods either return strategies of poor quality (often worse than naive methods) or require prohibitively expensive computations for even moderately large domains. Motivated by this, we propose low-rank mechanism (LRM), the first practical differentially private technique for answering batch linear queries with high accuracy. LRM works for both exact (i.e., $\epsilon$-) and approximate (i.e., ($\epsilon$, $\delta$)-) differential privacy definitions. We derive the utility guarantees of LRM, and provide guidance on how to set the privacy parameters given the user's utility expectation. Extensive experiments using real data demonstrate that our proposed method consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.