ProBE: Proportioning Privacy Budget for Complex Exploratory Decision Support

TL;DR

This paper introduces algorithms for privacy-preserving complex decision support queries that balance privacy loss and accuracy guarantees, enabling reliable analysis of multi-condition aggregate data.

Contribution

It formally defines complex decision support queries with accuracy bounds and proposes algorithms to proportion privacy budgets effectively, optimizing privacy-utility trade-offs.

Findings

Algorithms maintain utility guarantees on real datasets

Effective privacy budget proportioning reduces privacy loss

Supports complex queries with multiple conditions

Abstract

This paper studies privacy in the context of complex decision support queries composed of multiple conditions on different aggregate statistics combined using disjunction and conjunction operators. Utility requirements for such queries necessitate the need for private mechanisms that guarantee a bound on the false negative and false positive errors. This paper formally defines complex decision support queries and their accuracy requirements, and provides algorithms that proportion the existing budget to optimally minimize privacy loss while supporting a bounded guarantee on the accuracy. Our experimental results on multiple real-life datasets show that our algorithms successfully maintain such utility guarantees, while also minimizing privacy loss.