The Utility Cost of Robust Privacy Guarantees
The Utility Cost of Robust Privacy Guarantees
复制标题
强大的隐私保证的公用事业成本
DOI：
10.1109/isit.2018.8437735
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
L. Sankar
中科院分区：
文献类型：
--
作者：
Hao Wang;Mario Díaz;F. Calmon;L. Sankar
考虑具有公共和私有功能的数据集的数据发布设置。发布者的目标是最大限度地增加公开数据集中关于公共特征的信息量，同时保持关于私有特征的信息泄露的界限。本文的目标是分析构建的隐私机制的性能，这些机制是为匹配从数据集学习的分布而构建的。考虑了两种不同的场景：(I)机制被设计为为学习分布提供隐私保证；以及(Ii)机制被设计为为学习分布的给定邻域中的每个分布提供隐私保证。对于第一种情况，在给定任何隐私机制的情况下，给出了学习分发和真实分发的隐私效用保证之间的差异的上界。在第二种情况下，给出了由于提供统一隐私保证而导致的效用降低的上界。
Consider a data publishing setting for a data set with public and private features. The objective of the publisher is to maximize the amount of information about the public features in a revealed data set, while keeping the information leaked about the private features bounded. The goal of this paper is to analyze the performance of privacy mechanisms that are constructed to match the distribution learned from the data set. Two distinct scenarios are considered: (i) mechanisms are designed to provide a privacy guarantee for the learned distribution; and (ii) mechanisms are designed to provide a privacy guarantee for every distribution in a given neighborhood of the learned distribution. For the first scenario, given any privacy mechanism, upper bounds on the difference between the privacy-utility guarantees for the learned and true distributions are presented. In the second scenario, upper bounds on the reduction in utility incurred by providing a uniform privacy guarantee are developed.
影响因子：
2.5
作者：
Calmon, Flavio du Pin;Polyanskiy, Yury;Wu, Yihong
通讯作者：
Wu, Yihong