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Privacy-Preserving Bayesian Inference: Foundations and Extensions

隐私保护贝叶斯推理：基础和扩展

基本信息

批准号：
1916002
负责人：
Ruobin Gong
金额：
$ 10万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916002&HistoricalAwards=false
关键词：
Privacy Preserving Bayesian Inference Foundations

项目摘要

This project provides a theoretical foundation and computational methodologies to conduct Bayesian statistical inference using differentially private data. Differential privacy is a mathematical framework that allows the release of potentially sensitive data in such a way that protects the confidentiality of individual records without unduly sacrificing its overall usefulness for statistical analysis. As our society today grapples with the privacy implications that accompany the exploding growth of large-scale datasets, the invention of differential privacy provides a solution to protect personal demographic and biological information without deterring the accumulation of public knowledge. The U.S. Census Bureau has officially adopted differential privacy as the disclosure avoidance method for the 2020 Census. Other data collectors and curators are expected to follow suit in the near future. This project answers the pressing need for new statistical theory and methods to appropriately understand and efficiently analyze differentially private data. The project will expand the repertoire of tools available to researchers, and contribute to the cause of creating a better informed and more transparent society while respecting individual privacy. The PI will work on a theoretical formulation of the definitions of differential privacy using imprecise probability constructions, including interval of measures and coherent upper-lower probability measures. In the Bayesian context, such a formulation delivers a robust-likelihood conception of the model and allows for the computation of bounds on posterior quantities based on differentially private data for arbitrary prior specifications. The PI also proposes the differentially private approximate Bayesian computation (ABC) algorithm, a noisy ABC algorithm that delivers exact posterior inference given differentially private observations subject to arbitrary additive noise. The algorithm permits differentially private inference from large-scale Bayesian models with intractable likelihoods. The project bridges the classic theories of robust Bayes and generalized Bayes, with the novel literature on statistical privacy, and derives practical implementations based on privacy-preserving data releases. The project will supply analysts and researchers in a timely fashion with inferential methodologies tailored for differentially private input that are both theoretically sound and computationally efficient.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目提供了一个理论基础和计算方法进行贝叶斯统计推断，使用不同的私人数据。差分隐私是一种数学框架，允许以保护个人记录机密性的方式发布潜在敏感数据，而不会过度牺牲其对统计分析的整体有用性。随着我们今天的社会努力应对伴随着大规模数据集爆炸式增长的隐私问题，差分隐私的发明提供了一种解决方案，可以保护个人人口统计和生物信息，而不会阻碍公共知识的积累。美国人口普查局已正式采用差异隐私作为2020年人口普查的披露避免方法。预计其他数据收集者和管理者将在不久的将来效仿。该项目回答了对新的统计理论和方法的迫切需求，以适当地理解和有效地分析差异化的私人数据。该项目将扩大研究人员可用的工具库，并有助于在尊重个人隐私的同时建立一个更知情和更透明的社会。 PI将使用不精确的概率构造（包括测量间隔和连贯的上下概率测量）来研究差分隐私定义的理论公式。在贝叶斯的上下文中，这样的配方提供了一个强大的似然模型的概念，并允许计算后验量的界限的基础上的差分私有数据为任意的先验规范。PI还提出了差分私有近似贝叶斯计算（ABC）算法，这是一种有噪声的ABC算法，可以在任意加性噪声的情况下提供精确的后验推断。该算法允许从大规模贝叶斯模型与棘手的似然性差异私人推断。该项目将鲁棒贝叶斯和广义贝叶斯的经典理论与统计隐私的新颖文献联系起来，并基于隐私保护数据发布导出实际实现。该项目将为分析人员和研究人员提供及时的推理方法，这些方法针对不同的私人输入，在理论上是合理的，在计算上是有效的。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。