Statistically Valid Inferences from Privacy-Protected Data
Statistically Valid Inferences from Privacy-Protected Data
复制标题
来自受隐私保护的数据的统计上有效的推论
DOI：
10.1017/s0003055422001411
复制
发表时间：
2023
影响因子：
6.8
通讯作者：
Abhradeep Thakurta
中科院分区：
文献类型：
--
作者：
Georgina Evans;Gary King;M. Schwenzfeier;Abhradeep Thakurta
可以帮助社会科学家理解和改善人类社会挑战的空前数量的数据目前被锁在公司，政府和其他组织内部，部分原因是隐私问题。我们解决这个问题的通用数据访问和分析系统的数学保证研究对象的隐私，并为寻求社会科学见解的研究人员的统计有效性保证。我们建立在“差异隐私”的标准，纠正由隐私保护程序引起的偏见，提供了一个适当的会计不确定性，并施加最小的限制的统计方法和数量估计的选择。我们通过复制最近发表的两篇文章中的关键分析来说明，并展示了我们如何在保护隐私的同时获得大致相同的实质性结果。我们的方法使用简单，计算效率高;我们还提供实现我们所有方法的开源软件。
Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of privacy concerns. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for research subjects, and statistical validity guarantees for researchers seeking social science insights. We build on the standard of “differential privacy,” correct for biases induced by the privacy-preserving procedures, provide a proper accounting of uncertainty, and impose minimal constraints on the choice of statistical methods and quantities estimated. We illustrate by replicating key analyses from two recent published articles and show how we can obtain approximately the same substantive results while simultaneously protecting privacy. Our approach is simple to use and computationally efficient; we also offer open-source software that implements all our methods.
影响因子：
--
作者：
Wang, Yue;Kifer, Daniel;Lee, Jaewoo
通讯作者：
Lee, Jaewoo
费恩伯格问题：如何在差异隐私时代进行人类交互式数据分析
影响因子：
--
作者：
Dwork, Cynthia;Ullman, Jonathan
通讯作者：
Ullman, Jonathan
影响因子：
10.7
作者：
Abowd, John M.;Schmutte, Ian M.
通讯作者：
Schmutte, Ian M.