论文标题
由于查询的线性属性而引起的差异隐私的意外信息泄漏
Unexpected Information Leakage of Differential Privacy Due to Linear Property of Queries
论文作者
论文摘要
差异隐私是隐私保护的一种广泛接受的概念，拉普拉斯机制是处理数值数据的差异隐私机制的著名实例。在本文中，我们发现差异隐私不会考虑查询的衬里属性，从而导致意外信息泄漏。在特定的情况下，线性属性使一个查询可以将一个查询分为两个查询，例如$ q（d）= q（d_1）+q（d_2）$，如果$ d = d_1 \ cup d_1 \ cup d_2 $和$ d_1 \ cap d_2 = \ emptySet $。如果攻击者试图获得$ q（d）$的答案，他们不仅可以发出查询$ q（d）$，而且可以发出$ q（d_1）$，并自己计算$ q（d_2）$，只要他们知道$ d_2 $。通过一个查询的不同部门，攻击者可以从差异隐私机制中获得多个不同的答案。但是，从攻击者的角度和差异隐私机制的角度来看，如果设计精细，完全消耗的隐私预算是不同的。差异会导致意外信息泄漏，因为隐私预算是控制从差异隐私机制中法律发布信息量的关键参数。为了证明意外信息泄漏，我们提出了针对拉普拉斯机制的会员推理攻击。
The differential privacy is a widely accepted conception of privacy preservation and the Laplace mechanism is a famous instance of differential privacy mechanisms to deal with numerical data. In this paper, we find that the differential privacy does not take liner property of queries into account, resulting in unexpected information leakage. In specific, the linear property makes it possible to divide one query into two queries such as $q(D)=q(D_1)+q(D_2)$ if $D=D_1\cup D_2$ and $D_1\cap D_2=\emptyset$. If attackers try to obtain an answer of $q(D)$, they not only can issue the query $q(D)$, but also can issue the $q(D_1)$ and calculate the $q(D_2)$ by themselves as long as they know $D_2$. By different divisions of one query, attackers can obtain multiple different answers for the query from differential privacy mechanisms. However, from attackers' perspective and from differential privacy mechanisms' perspective, the totally consumed privacy budget is different if divisions are delicately designed. The difference leads to unexpected information leakage because the privacy budget is the key parameter to control the amount of legally released information from differential privacy mechanisms. In order to demonstrate the unexpected information leakage, we present a membership inference attacks against the Laplace mechanism.
