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.

翻译：本文研究了由多个不同聚合统计量条件通过析取与合取运算符组合而成的复杂决策支持查询中的隐私保护问题。此类查询的效用需求要求隐私保护机制必须保证假阴性误差与假阳性误差存在上界。本文正式定义了复杂决策支持查询及其精度要求，并提出在支持精度有界保证的前提下，通过优化分配现有隐私预算以最小化隐私损失的算法。我们在多个真实数据集上的实验结果表明，所提算法在成功维持此类效用保证的同时，能够有效最小化隐私损失。