论文标题
统计数据隐私：隐私和实用性之歌
Statistical Data Privacy: A Song of Privacy and Utility
论文作者
论文摘要
为了量化对开放数据共享需求的增加与敏感信息披露的疑虑之间的权衡，统计数据隐私（SDP）方法论分析了数据发布机制，这些机制可以根据机密数据对输出进行消毒。存在两个主导框架：统计披露控制（SDC），以及最新的差异隐私（DP）。尽管有框架差异，但SDC和DP都具有相同的统计问题。对于推理问题，我们可以设计满足披露风险界限的最佳释放机制和相关估计器，或者我们可以调整现有的消毒输出以创建新的最佳估计器。这两个问题都取决于评估风险和效用的不确定性量化。在这篇综述中，我们讨论了SDC和DP共有的统计基础，重点介绍了SDP的主要发展，并在私人推论中提出了令人兴奋的开放研究问题。
To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data release mechanisms which sanitize outputs based on confidential data. Two dominant frameworks exist: statistical disclosure control (SDC), and more recent, differential privacy (DP). Despite framing differences, both SDC and DP share the same statistical problems at its core. For inference problems, we may either design optimal release mechanisms and associated estimators that satisfy bounds on disclosure risk, or we may adjust existing sanitized output to create new optimal estimators. Both problems rely on uncertainty quantification in evaluating risk and utility. In this review, we discuss the statistical foundations common to both SDC and DP, highlight major developments in SDP, and present exciting open research problems in private inference.
