数据保密哲学博士

许多代理商收集他们打算与他人共享的数据。但是，发布收集的数据可能会揭示数据主体的身份或敏感属性。当数据包含其他变量（例如，人口统计变量或就业/教育历史记录）时，意图不当的用户可以将其与外部数据文件进行匹配时，诸如删除名称和地址之类的简单操作可能不足以保护机密性。因此，机构需要复杂的方法来促进安全的数据共享和分发。该系的教职员工广泛研究此类方法的理论和应用，包括（i）合成数据，其中高披露风险的原始数据值被替换为从概率分布中模拟的值，该值被指定为可重现原始关系（ii）机构可以用来量化由于机密性保护程序而导致的
Many agencies collect data that they intend to share with others. However, releasing the data as collected might reveal data subjects' identities or sensitive attributes. Simple actions like stripping names and addresses may not sufficiently protect confidentiality when the data contain other variables, such as demographic variables or employment/education histories, that ill-intentioned users can match to external data files. Thus, agencies need sophisticated methods to facilitate safe data sharing and dissemination. Faculty members in the department work extensively on theories and applications of such methods, including (i) synthetic data in which original data values at high risk of disclosure are replaced with values simulated from probability distributions specified to reproduce as many of the relationships in the original data as possible, (ii) metrics that agencies can use to quantify risks of disclosures and potential loss of information due to confidentiality protection procedures, and (iii) methods of secure data analysis that offer users output from statistical models without allowing them to see the actual data. Applications areas include government databases, such as data from the U.S. Census Bureau, and health data.