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 that sanitize outputs based on confidential data. Two dominant frameworks exist: statistical disclosure control (SDC) and the more recent differential privacy (DP). Despite framing differences, both SDC and DP share the same statistical problems at their core. For inference problems, either we may design optimal release mechanisms and associated estimators that satisfy bounds on disclosure risk measures, or we may adjust existing sanitized output to create new statistically valid and optimal estimators. Regardless of design or adjustment, in evaluating risk and utility, valid statistical inferences from mechanism outputs require uncertainty quantification that accounts for the effect of the sanitization mechanism that introduces bias and/or variance. 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.

中文翻译：

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统计数据隐私：隐私与实用之歌

为了量化日益增长的开放数据共享需求与对敏感信息泄露的担忧之间的权衡，统计数据隐私 (SDP) 方法分析了基于机密数据清理输出的数据发布机制。存在两个主要框架：统计披露控制（SDC）和最近的差分隐私（DP）。尽管框架存在差异，SDC 和 DP 的核心都存在相同的统计问题。对于推理问题，我们可以设计满足披露风险度量界限的最佳发布机制和相关估计器，或者我们可以调整现有的净化输出以创建新的统计上有效的最佳估计器。无论设计或调整如何，在评估风险和效用时，来自机制输出的有效统计推论都需要不确定性量化，以解释引入偏差和/或方差的清理机制的影响。在这篇综述中，我们讨论了 SDC 和 DP 的共同统计基础，重点介绍了 SDP 的主要发展，并提出了私人推理中令人兴奋的开放研究问题。