arXiv 2004.030022020-04-06cs.CR

两个可以走在一起：隐私增强方法和防止跟踪用户

Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users

Moni Naor, Neil Vexler

备注：: 45 pages, 4 figures. To appear on FORC 2020

摘要：

我们提出了一个新的问题时，从个人收集的数据，从试图减轻隐私泄漏在多个报告：跟踪用户参与数据汇集通过增加的机制，以提供隐私。受差分隐私框架的启发，我们提出了几种不可跟踪机制的定义。具体来说，我们将可跟踪参数定义为一组报告源自单个用户的概率与同一组报告源自两个用户（具有相同的私有值）的概率之间的最大比率的对数。我们将探讨这一新定义的含义。我们展示了如何差分私人和不可跟踪的机制可以结合起来，以实现一定的用户改变他们的私人价值时，检测问题的界限。检查谷歌的永久隐私部署的解决方案，我们表明，RAPPOR（Erlingsson et al. ACM CCS，2014）是可跟踪的，在我们的框架中的参数在他们的论文。我们分析了一种随机响应的变体，用于收集单个比特的统计数据，Bitwise Everlasting Privacy，它实现了良好的准确性和永久的隐私，同时只有合理的不可跟踪，特别是报告数量线性增长。为了从更大的域收集有关数据的统计信息（对于直方图和重打击者），我们提出了一种机制，可以防止跟踪有限数量的响应。我们还提出了机制链的概念，使用一个机制的输出作为另一个的输入，在差分隐私的范围内，并证明了一个$\varepsilon_1$-LDP机制与$\varepsilon_2$-LDP机制的链接是$\ln\frac{e^{\varepsilon_1+\varepsilon_2}+1}{e^{\varepsilon_1}+e^{\varepsilon_2}}$-LDP，这个界是紧的。

英文摘要：

We present a new concern when collecting data from individuals that arises from the attempt to mitigate privacy leakage in multiple reporting: tracking of users participating in the data collection via the mechanisms added to provide privacy. We present several definitions for untrackable mechanisms, inspired by the differential privacy framework. Specifically, we define the trackable parameter as the log of the maximum ratio between the probability that a set of reports originated from a single user and the probability that the same set of reports originated from two users (with the same private value). We explore the implications of this new definition. We show how differentially private and untrackable mechanisms can be combined to achieve a bound for the problem of detecting when a certain user changed their private value. Examining Google's deployed solution for everlasting privacy, we show that RAPPOR (Erlingsson et al. ACM CCS, 2014) is trackable in our framework for the parameters presented in their paper. We analyze a variant of randomized response for collecting statistics of single bits, Bitwise Everlasting Privacy, that achieves good accuracy and everlasting privacy, while only being reasonably untrackable, specifically grows linearly in the number of reports. For collecting statistics about data from larger domains (for histograms and heavy hitters) we present a mechanism that prevents tracking for a limited number of responses. We also present the concept of Mechanism Chaining, using the output of one mechanism as the input of another, in the scope of Differential Privacy, and show that the chaining of an $\varepsilon_1$-LDP mechanism with an $\varepsilon_2$-LDP mechanism is $\ln\frac{e^{\varepsilon_1+\varepsilon_2}+1}{e^{\varepsilon_1}+e^{\varepsilon_2}}$-LDP and that this bound is tight.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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