BIGDATA: F: Protection of Data Privacy via Differentially Private Multiple Synthesis

该项目寻求更好的方法来保护大数据中的个人隐私，而不会损害用于研究和公共使用的人口级别信息的准确性。差异隐私社区探索了数据集的私下发布，但这在很大程度上是在计算机科学理论社区内进行的，并没有严格评估这些方法的实用价值。该项目开发了一些技术和工具来创建与原始数据集具有相同结构和统计特性、但满足不同隐私的合成“代理数据集”。这项工作包括开发生成符合统计分析的合成数据的技术，评估现实生活中的大数据技术，以及开发和发布作为数据集创建的开放源码工具。该项目将统计学家的观点引入效用问题，并对模拟数据和两个真实数据集进行了评估，其中一个是基于人口普查记录的成人数据集，经常用于匿名化研究，另一个是关于贫困的社会科学研究。这项工作出现在几个社区外展项目中，以激发K-12学生对STEM职业的兴趣。该项目建立在多重综合的基础上(从后验分布生成多个数据集，得出充分的统计数据)。该项目首先建立了理论和方法基础，包括但不限于对常用统计模型中充分的统计数据的全局敏感性进行数学推导，建立在公布的数据中保障个人隐私保护的理论，以及建立关于合成数据的大样本推理理论。大量使用了概率论、随机过程、渐近理论、贝叶斯建模和计算以及缺失数据分析技术。为了确保大数据的可扩展性，正在调查其标量分量不会随着数据项数量增加而增加的足够统计数据。开发的方法通过模拟研究和对现实生活数据集(包括社会/金融数据和医疗保健数据)的应用，以当前发布个人水平数据的方法为基准进行评估。最后，正在开发开放源码软件，以便在综合R档案网络上发布，该软件产生一个与原始数据模式相匹配的合成数据集，以及某些统计数据，以解释披露风险和支持数据效用分析。

This project seeks better ways to protect individual privacy in big data without compromising the accuracy of population-level information for research and public use. The differential privacy community has explored private release of datasets, but this has largely been within the computer science theory community and has not rigorously evaluated the practical utility of the methods. This project develops techniques and tools to create synthetic "surrogate datasets" with the same structure and statistical properties as the original dataset, but satisfying differential privacy. The work includes development of techniques to generate synthetic data amenable to statistical analysis, evaluation of the techniques in real-life big data, and to develop and release as open source tools for dataset creation. This project brings a statistician's viewpoint to the utility question, and evaluates against both simulated data, the census record based ADULT dataset frequently used in anonymization studies, and two real datasets, one with hospital inpatient data and the other a social science study on poverty. The work is being featured in several community outreach programs to stimulate interests in STEM careers among K-12 students.The project builds on multiple synthesis (generating multiple datasets from posterior distribution-derived sufficient statistics). The project is first establishing theoretical and methodological foundations, including but not limited to mathematical derivation of the global sensitivity of the sufficient statistics in commonly used statistical models, establishment of a theory that guarantees individual privacy protection in released data, and establishment of large-sample inferential theories on the synthetic data. Probability theory, stochastic process, asymptotic theory, Bayesian modelling and computing, and missing data analysis techniques are heavily employed. To ensure scalability to Big Data, sufficient statistics whose scalar components do not increase as the number of data items increases are being investigated. The developed method is evaluated by simulation studies and applications to real life data sets (including social/financial data and health care data) benchmarked against current methodologies for releasing individual-level data. Finally, open-source software is being developed for release on the Comprehensive R Archive Network that produces a synthetic dataset matching the schema of the original data, as well as certain statistics to explain disclosure risk and support analysis of data utility.