论文标题
从当地差异隐私的角度来看，隐私漏斗
The Privacy Funnel from the viewpoint of Local Differential Privacy
论文作者
论文摘要
我们考虑一个数据库$ \ vec {x} =（x_1，\ cdots，x_n）$，其中包含$ n $用户的数据。数据聚合器希望宣传数据库，但希望对数据集进行消毒以隐藏敏感数据$ S_I $与$ x_i $相关。在隐私渠道中考虑了此设置，该设置使用共同信息作为泄漏指标。这种方法的不利因素是，互信息不能提供最坏的保证，而找到最佳的疗养方案可能在计算上是过度的。我们通过使用不同的隐私指标来解决这些问题，并考虑一次在一个条目上运行的本地协议。我们表明，在当地差异隐私和本地信息隐私泄漏指标下，可以有效地获得最佳协议。但是，本地信息隐私既与隐私漏斗场景的隐私要求都更加紧密，又可以更有效地计算。 We also consider the scenario where each user has multiple attributes (i.e. $X_i = (X^1_i,\cdots,X^m_i)$), for which we define \emph{Side-channel Resistant Local Information Privacy}, and we give efficient methods to find protocols satisfying this criterion while still offering good utility.探索实验证实了这些方法的有效性。
We consider a database $\vec{X} = (X_1,\cdots,X_n)$ containing the data of $n$ users. The data aggregator wants to publicise the database, but wishes to sanitise the dataset to hide sensitive data $S_i$ correlated to $X_i$. This setting is considered in the Privacy Funnel, which uses mutual information as a leakage metric. The downsides to this approach are that mutual information does not give worst-case guarantees, and that finding optimal sanitisation protocols can be computationally prohibitive. We tackle these problems by using differential privacy metrics, and by considering local protocols which operate on one entry at a time. We show that under both the Local Differential Privacy and Local Information Privacy leakage metrics, one can efficiently obtain optimal protocols; however, Local Information Privacy is both more closely aligned to the privacy requirements of the Privacy Funnel scenario, and more efficiently computable. We also consider the scenario where each user has multiple attributes (i.e. $X_i = (X^1_i,\cdots,X^m_i)$), for which we define \emph{Side-channel Resistant Local Information Privacy}, and we give efficient methods to find protocols satisfying this criterion while still offering good utility. Exploratory experiments confirm the validity of these methods.
