CT-T: Goal-Oriented Privacy-Preservation

CT-T：目标导向的隐私保护

• 批准号: 0524671
• 负责人: Jeffrey Naughton
• 金额: $ 160万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0524671&HistoricalAwards=false
• 关键词: CT; Goal; Oriented; Privacy; Preservation

Access to many important datasets in health care, biomedical informatics, sociology, and homeland security is restricted by HIPAA regulations on the release of detailed "microdata", impeding basic research in fields dependent on such data, and the problem will only get worse as more medical data is collected. A primary focus of this project is to "unlock" such critical datasets by developing techniques and tools to anonymize very large databases. Such tools will facilitate the release of datasets to researchers, while insuring that the privacy of individuals is maintained.Many organizations, including the Census Bureau and departments of health at the local, state, and federal levels, routinely publish aggregated forms of data because they can be used to answer statistical queries over selected subsets of the data. However, existing methods for aggregation have significant weaknesses and proposed improvements scale poorly. In addition, the problem of building accurate predictive models (e.g., decision trees) from aggregated data has not been widely studied. There are many opportunities for building accurate predictive models from data aggregated to preserve privacy; conversely, the possibility of building such models suggests another way that sensitive information can be inadvertently "leaked" even when only aggregated data is published. This project aims to investigate the trade-off between privacy guarantees and the utility of the published data for specific analysis tasks, including: (a) privacy-preserving algorithms for aggregating large data sets, (b) algorithms for building predictive models using aggregated data, and (c) conditions under which accurate predictive models can be constructed from such data.The project team includes the Chief Epidemiologist for the State of Wisconsin, who is the curator for many health-related datasets, a cancer researcher whose research program relies in part on datasets curated by the State, and three computer scientists with expertise in data mining and management.This project will train graduate and undergraduate students at the University of Wisconsin in the tradeoffs between privacy protection and accurate model building, and the results and tools will be widely disseminated through publications and the project's Web site (www-db.cs.wisc.edu/dbprivacy).

医疗保健、生物医学信息学、社会学和国土安全等领域的许多重要数据集的访问受到HIPAA关于发布详细“微数据”的规定的限制，阻碍了依赖这些数据的领域的基础研究，而且随着收集的医疗数据越来越多，这个问题只会变得更糟。该项目的一个主要重点是通过开发匿名非常大的数据库的技术和工具来“解锁”这些关键数据集。这些工具将有助于向研究人员发布数据集，同时确保个人隐私得到维护。许多组织，包括人口普查局和地方、州和联邦各级的卫生部，经常发布汇总形式的数据，因为它们可以用来回答对选定数据子集的统计查询。然而，现有的聚合方法存在明显的缺陷，所提出的改进措施可伸缩性较差。此外，从聚合数据中构建准确的预测模型(例如，决策树)的问题还没有得到广泛的研究。从聚合的数据构建准确的预测模型以保护隐私的机会很多；相反，构建此类模型的可能性表明，即使只发布聚合的数据，敏感信息也可能被无意中“泄露”。该项目旨在调查隐私保障和发布数据对特定分析任务的效用之间的权衡，包括：(A)用于聚合大型数据集的隐私保护算法，(B)用于使用聚合数据构建预测模型的算法，以及(C)从此类数据构建准确预测模型的条件。该项目团队包括威斯康星州首席流行病学家，他是许多与健康相关的数据集的策划人，一名癌症研究人员，其研究计划部分依赖于国家管理的数据集，这个项目将培训威斯康星大学的研究生和本科生，在隐私保护和准确的模型建立之间进行权衡，结果和工具将通过出版物和该项目的网站(www.db.cs.witc.edu/DBPrivacy)广泛传播。