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

BIGDATA：F：通过差分隐私多重合成保护数据隐私

基本信息

批准号：
1546373
负责人：
Fang Liu
金额：
$ 24.35万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-01-01 至 2019-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546373&HistoricalAwards=false
关键词：
BIGDATA Protection Data Privacy via

项目摘要

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.

该项目寻求更好的方法来保护大数据中的个人隐私，而不影响研究和公共使用的人口水平信息的准确性。差分隐私社区已经探索了数据集的私人发布，但这在很大程度上是在计算机科学理论社区内，并没有严格评估这些方法的实际效用。该项目开发技术和工具来创建合成的“替代数据集”，其结构和统计特性与原始数据集相同，但满足差异隐私。这项工作包括开发技术，以生成适合统计分析的合成数据，评估现实生活中的大数据技术，并开发和发布数据集创建的开源工具。这个项目带来了统计学家的观点的效用问题，并评估两个模拟数据，人口普查记录为基础的数据集经常使用的匿名化研究，和两个真实的数据集，一个与医院住院病人的数据和其他社会科学研究贫困。这项工作正在几个社区外展计划中进行，以激发K-12学生对STEM职业的兴趣。该项目建立在多重合成（从后验分布导出的充分统计数据生成多个数据集）的基础上。该项目首先建立理论和方法基础，包括但不限于常用统计模型中充分统计量的全局敏感性的数学推导，建立保证发布数据中个人隐私保护的理论，以及建立合成数据的大样本推理理论。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大量使用。为了确保大数据的可扩展性，正在研究其标量分量不随着数据项数量的增加而增加的足够的统计数据。所开发的方法进行评估的模拟研究和应用程序的真实的生活数据集（包括社会/金融数据和医疗保健数据）基准对当前的方法，释放个人层面的数据。最后，正在开发开源软件，以便在Comprehensive R Archive Network上发布，该软件生成与原始数据模式相匹配的合成数据集，以及某些统计数据，以解释披露风险并支持数据效用分析。