论文标题
图形对称对称性绑定在频道信息泄漏下的洪水隐私下
A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy
论文作者
论文摘要
Blowfish隐私是对差异隐私的最新概括，可以改善效用，同时以语义保证来维持隐私政策，这是一个促进了计算机科学中差异隐私的普及的因素。本文将Bliplish隐私与通信理论社区中信息渠道的隐私丢失的重要度量联系起来：最小内向泄漏。输入数据相邻关系中的对称性对于差异隐私和最小内部泄漏之间的已知连接至关重要。但是，尽管差异隐私表现出强烈的对称性，但由于该框架的灵活隐私政策，洪水相邻关系对应于任意简单的图形。为了结合池塘私有机制的最小内向泄漏，我们组织了分析对应于图形自动形态轨道的对称分区的分析。我们与渐近平等的建筑会议表明了紧密度。
Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees, a factor that has driven the popularity of differential privacy in computer science. This paper relates Blowfish privacy to an important measure of privacy loss of information channels from the communications theory community: min-entropy leakage. Symmetry in an input data neighbouring relation is central to known connections between differential privacy and min-entropy leakage. But while differential privacy exhibits strong symmetry, Blowfish neighbouring relations correspond to arbitrary simple graphs owing to the framework's flexible privacy policies. To bound the min-entropy leakage of Blowfish-private mechanisms we organise our analysis over symmetrical partitions corresponding to orbits of graph automorphism groups. A construction meeting our bound with asymptotic equality demonstrates tightness.
