Collaborative Research: Record Linkage and Privacy-Preserving Methods for Big Data

这项研究项目将开发完善的统计和机器学习技术，以保护关联数据的隐私。社会实体及其行为模式是社会科学中的一个重要课题。现代信息基础设施的发展、数据收集和存储的便利性以及新的计算数据分析技术的发展，为这一领域的研究注入了活力。然而，在许多应用领域中，相关和敏感信息通常位于多个数据库中。如果不合并数据库，数据分析本质上是不可能的，但代价是增加了侵犯隐私的风险。这项研究将解决在大数据时代如何在存在多个数据源、数据共享和隐私的情况下执行有效的统计推断的问题。研究人员为推理和不确定性量化建立的新模型结构将有助于统计学和以统计学为主要工具的许多学科。这些方法将在社会、经济和行为科学中有广泛的应用，包括医学、遗传学、官方统计和侵犯人权。研究人员将与博士后研究员以及研究生和本科生合作。统计方法将被封装在开放源码软件包中，允许从业者使用现成的方法，同时促进更详细的控制和推广。这一跨学科研究项目将利用统计学和机器学习的最新技术，改进记录链接和隐私方面的方法。记录链接是合并可能有噪音的数据库的过程，目的是删除重复条目。隐私保护记录链接(PPRL)试图识别引用多个数据库中的相同实体的记录，而不会损害这些记录所代表的实体的隐私。这项研究将集中于三个目标：(1)为PPRL开发新的贝叶斯方法，其中错误可以准确地跨整个链接过程传播到统计推断中，包括新的隐私措施，以捕捉链接数据库中任何单个风险的效用和风险之间的权衡；(2)开发新的健壮方法，以实现具有差异隐私保证的链接后发布的合成数据及其放松，以解决额外的隐私层和支持更广泛的数据共享；以及(3)探索变分推理等大数据方法，以解决链接和隐私中存在的可扩展性和潜在的集群互换性问题，从而使新方法可以扩展到多个和大型数据库。新方法将是可扩展的，并在整个链接和隐私过程中评估不确定性，并可以使用贝叶斯披露风险和贝叶斯差异隐私进行评估。作为支持调查和统计方法研究的联合活动的一部分，该项目得到了方法学、测量和统计方案和一个联邦统计机构联盟的支持。

This research project will develop sound statistical and machine learning techniques for preserving privacy with linked data. Social entities and their patterns of behavior is a crucial topic in the social sciences. Research in this area has been invigorated by the growth of the modern information infrastructure, ease of data collection and storage, and the development of novel computational data analyses techniques. However, in many application areas relevant and sensitive information is commonly located across multiple databases. Data analysis is inherently impossible without merging databases, but at the cost of increasing the risk of a privacy violation. This research will address the problem of how to perform valid statistical inference in the presence of multiple data sources, data sharing, and privacy in the age of "big data." The investigators' new modeling construct for inference and uncertainty quantification will contribute to both statistics and the many disciplines for which statistics is a principal tool. The methods will have a wide range of applications in the social, economic, and behavioral sciences, including medicine, genetics, official statistics, and human rights violations. The investigators will collaborate with post-doctoral researcher and with graduate and undergraduate students. The statistical methods will be encapsulated in open-source software packages, allowing off-the-shelf use by practitioners while facilitating more detailed control and extensions.This interdisciplinary research project will improve upon methods in record linkage and privacy using state-of-the-art techniques from statistics and machine learning. Record linkage is the process of merging possible noisy databases with the goal of removing duplicate entries. Privacy-preserving record linkage (PPRL) tries to identify records that refer to the same entities from multiple databases without compromising the privacy of the entities represented by these records. The research will focus on three aims: (1) development of new Bayesian methods for PPRL, where the error can be propagated exactly across the entire linkage process and into statistical inference, including new privacy measures to capture a tradeoff between utility and risk of any individual risk in a linked database; (2) development of new robust methods for realizing synthetic data releases post-linkage with differential privacy guarantees and its relaxations to address additional layers of privacy and support broader data sharing; and (3) exploration of "big data" methods such as variational inference to address scalability and latent cluster exchangeability issues existing within linkage and privacy, such that the new methods can scale to multiple and large databases. The new methods will be scalable and assess uncertainty throughout the entire linkage and privacy process and can be evaluated using Bayesian disclosure risk and Bayesian differential privacy. The project is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.