Bayesian Differential Privacy on Correlated Data
Bayesian Differential Privacy on Correlated Data
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DOI：
10.1145/2723372.2747643
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Bin Yang;Issei Sato;Hiroshi Nakagawa
中科院分区：
文献类型：
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作者：
Bin Yang;Issei Sato;Hiroshi Nakagawa
差分隐私为评估扰动算法的隐私提供了严格的标准。人们普遍认为，差分隐私是一个通用的定义，处理独立和相关的数据和差分隐私算法可以保护隐私免受任意的对手。然而，最近的研究表明，如果数据是相关的，差分隐私可能无法保证对任意对手的隐私。本文主要研究相关数据上的私有扰动算法。我们研究了以下三个问题：（1）数据相关性对隐私的影响;（2）对手先验知识对隐私的影响;（3）当数据相关时，对于数据中元组的任何子集的先验知识，一般扰动算法是私有的。我们提出了隐私的河豚定义，称为贝叶斯差分隐私，即使数据相关且先验知识不完整，也可以通过该定义评估概率扰动算法的隐私级别。提出了一种高斯相关模型来精确描述数据相关性的结构，并在此基础上分析了扰动算法的贝叶斯差分隐私性。我们的研究结果表明，隐私是最穷的对手谁拥有最少的先验知识。我们进一步扩展这个模型，考虑不确定的先验知识更一般的。
Differential privacy provides a rigorous standard for evaluating the privacy of perturbation algorithms. It has widely been regarded that differential privacy is a universal definition that deals with both independent and correlated data and a differentially private algorithm can protect privacy against arbitrary adversaries. However, recent research indicates that differential privacy may not guarantee privacy against arbitrary adversaries if the data are correlated. In this paper, we focus on the private perturbation algorithms on correlated data. We investigate the following three problems: (1) the influence of data correlations on privacy; (2) the influence of adversary prior knowledge on privacy; and (3) a general perturbation algorithm that is private for prior knowledge of any subset of tuples in the data when the data are correlated. We propose a Pufferfish definition of privacy, called Bayesian differential privacy, by which the privacy level of a probabilistic perturbation algorithm can be evaluated even when the data are correlated and when the prior knowledge is incomplete. We present a Gaussian correlation model to accurately describe the structure of data correlations and analyze the Bayesian differential privacy of the perturbation algorithm on the basis of this model. Our results show that privacy is poorest for an adversary who has the least prior knowledge. We further extend this model to a more general one that considers uncertain prior knowledge.