论文标题
Privug：使用概率编程来量化隐私风险分析中的泄漏
Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis
论文作者
论文摘要
数据分析结果的披露具有重要的科学和商业理由。但是，如果没有勤奋的对受试者隐私风险的调查，就不得披露任何数据。 Privug是一种探索数据分析和匿名程序的信息泄漏属性的方法。在Privug中，我们使用现成的工具进行贝叶斯推断以对信息流进行信息理论分析，从而重新解释程序。对于隐私研究人员，Privug提供了一种快速，轻巧的方式来试验隐私保护措施和机制。我们证明Privug是准确，可扩展的，并且适用于一系列泄漏分析方案。
Disclosure of data analytics results has important scientific and commercial justifications. However, no data shall be disclosed without a diligent investigation of risks for privacy of subjects. Privug is a tool-supported method to explore information leakage properties of data analytics and anonymization programs. In Privug, we reinterpret a program probabilistically, using off-the-shelf tools for Bayesian inference to perform information-theoretic analysis of the information flow. For privacy researchers, Privug provides a fast, lightweight way to experiment with privacy protection measures and mechanisms. We show that Privug is accurate, scalable, and applicable to a range of leakage analysis scenarios.
