Differentially-private software frequency profiling under linear constraints
Differentially-private software frequency profiling under linear constraints
复制标题
线性约束下的差分私有软件频率分析
DOI：
10.1145/3428271
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发表时间：
2020
影响因子：
--
通讯作者：
Rountev, Atanas
中科院分区：
文献类型：
--
作者：
Zhang, Hailong;Hao, Yu;Latif, Sufian;Bassily, Raef;Rountev, Atanas
差异隐私已经成为隐私保护数据收集和分析的主要理论框架。它允许为人口收集有意义的统计数据，而不会透露关于人口中任何个人成员的“太多”信息。对于软件剖析，这种机制允许以隐私保护的方式收集和分析来自部署的软件系统的许多用户的剖析数据。这样的解决方案是吸引了许多利益相关者，包括软件用户，软件开发商，基础设施提供商，和政府agency.We提出了一种方法，从软件执行的频率向量的差异私人收集。报告频率信息时添加了来自拉普拉斯分布的随机噪声。我们方案设计背后的一个关键观察结果是，由于静态代码结构，事件频率密切相关。差别隐私保护必须考虑到这种关系;否则，一个非常强大的隐私保障实际上比它看起来更弱。出于这一观察，我们提出了一种新的和一般的差分私人剖析方案时，频率之间的相关性可以表示通过线性不等式。使用线性规划公式，我们展示了如何确定随机噪声的大小，应该添加到这样的线性约束下，以实现有意义的隐私保护。接下来，我们开发了一个有效的实例，这个一般的机械约束的一个重要子类。而不是LP，我们的解决方案使用的约束图的可达性分析。作为一个例子，我们采用这种方法来实现Android应用程序的差异私有方法频率分析。任何差异私有方案都必须平衡两个相互竞争的方面：隐私和准确性。通过实验研究来表征这些权衡，我们（1）表明，我们提出的随机化实现了更高的准确性相比，相关的先前的工作，（2）证明，高准确性和高隐私保护可以同时实现，（3）突出的重要性，线性约束的随机化设计。这些有希望的结果提供了证据，我们的方法是一个很好的候选人的隐私保护部署的软件的频率分析。
Differential privacy has emerged as a leading theoretical framework for privacy-preserving data gathering and analysis. It allows meaningful statistics to be collected for a population without revealing ``too much'' information about any individual member of the population. For software profiling, this machinery allows profiling data from many users of a deployed software system to be collected and analyzed in a privacy-preserving manner. Such a solution is appealing to many stakeholders, including software users, software developers, infrastructure providers, and government agencies.We propose an approach for differentially-private collection of frequency vectors from software executions. Frequency information is reported with the addition of random noise drawn from the Laplace distribution. A key observation behind the design of our scheme is that event frequencies are closely correlated due to the static code structure. Differential privacy protections must account for such relationships; otherwise, a seemingly-strong privacy guarantee is actually weaker than it appears. Motivated by this observation, we propose a novel and general differentially-private profiling scheme when correlations between frequencies can be expressed through linear inequalities. Using a linear programming formulation, we show how to determine the magnitude of random noise that should be added to achieve meaningful privacy protections under such linear constraints. Next, we develop an efficient instance of this general machinery for an important subclass of constraints. Instead of LP, our solution uses a reachability analysis of a constraint graph. As an exemplar, we employ this approach to implement differentially-private method frequency profiling for Android apps.Any differentially-private scheme has to balance two competing aspects: privacy and accuracy. Through an experimental study to characterize these trade-offs, we (1) show that our proposed randomization achieves much higher accuracy compared to related prior work, (2) demonstrate that high accuracy and high privacy protection can be achieved simultaneously, and (3) highlight the importance of linear constraints in the design of the randomization. These promising results provide evidence that our approach is a good candidate for privacy-preserving frequency profiling of deployed software.
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DOI：
10.1145/1993498.1993551
发表时间：
2011-06
期刊：
--
影响因子：
--
作者：
Aditya Budi;D. Lo;Lingxiao Jiang;Lucia
通讯作者：
Aditya Budi;D. Lo;Lingxiao Jiang;Lucia
哥德尔奖
DOI：
--
发表时间：
2013
期刊：
影响因子：
--
作者：
M. Nielsen
通讯作者：
M. Nielsen
已发布软件的分析策略及其对测试活动的影响的实证研究
DOI：
10.1145/1007512.1007522
发表时间：
2004
期刊：
2011 33rd International Conference on Software Engineering (ICSE)
影响因子：
--
作者：
Sebastian G. Elbaum;M. Hardojo
通讯作者：
M. Hardojo
DOI：
--
发表时间：
2017-08
期刊：
--
影响因子：
--
作者：
Tianhao Wang;Jeremiah Blocki;Ninghui Li;S. Jha
通讯作者：
Tianhao Wang;Jeremiah Blocki;Ninghui Li;S. Jha
剩余测试覆盖率监控
DOI：
10.1145/302405.302637
发表时间：
1999
期刊：
Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002)
影响因子：
--
作者：
C. Pavlopoulou;M. Young
通讯作者：
M. Young