论文标题
具有更颗粒状差异隐私的算法保证算法
Algorithms with More Granular Differential Privacy Guarantees
论文作者
论文摘要
差异隐私通常使用比理论更大的隐私参数应用于理想的参数。已经提出了对大型隐私参数容忍的各种非正式理由。在这项工作中，我们考虑部分差异隐私（DP），该隐私允许以每个属性为基础量化隐私保证。在此框架中，我们研究了几个基本数据分析和学习任务，并设计了其每个属性隐私参数的算法，其较小的是一个人的整个记录（即所有属性）的最佳隐私参数。
Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed. In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. In this framework, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes).
