论文标题
差异隐私的信息设计
Information Design for Differential Privacy
论文作者
论文摘要
公司和统计机构必须保护其收集，分析和发布数据的个人的隐私。这些组织越来越多地通过使用满足差异隐私的出版机制来做到这一点。我们考虑选择这种机制的问题，以最大程度地提高其对最终用户的产出价值。我们表明，在兴趣的统计数据中增加噪声的机制（例如，大多数在实践中使用的机制）通常在统计量是一个幅度数据的总和或平均值（例如收入）时并不是最佳的。但是，我们还表明，当统计量是具有一定特征的数据输入计数时，添加噪声始终是最佳的，并且从对称分布中绘制了基础数据库（例如，如果个人的数据是I.I.D.）。此外，当数据使用者具有超模型的回报时，我们表明，通过使用一种新颖的比较静态，该静态始终是最佳的，该新型比较静态根据信息结构在超模块化决策问题中对其有用性进行排名。
Firms and statistical agencies must protect the privacy of the individuals whose data they collect, analyze, and publish. Increasingly, these organizations do so by using publication mechanisms that satisfy differential privacy. We consider the problem of choosing such a mechanism so as to maximize the value of its output to end users. We show that mechanisms which add noise to the statistic of interest--like most of those used in practice--are generally not optimal when the statistic is a sum or average of magnitude data (e.g., income). However, we also show that adding noise is always optimal when the statistic is a count of data entries with a certain characteristic, and the underlying database is drawn from a symmetric distribution (e.g., if individuals' data are i.i.d.). When, in addition, data users have supermodular payoffs, we show that the simple geometric mechanism is always optimal by using a novel comparative static that ranks information structures according to their usefulness in supermodular decision problems.
