CT-T: Goal-Oriented Privacy-Preservation

医疗保健、生物医学信息学、社会学和国土安全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，随着更多的医疗数据被收集，问题只会变得更糟。该项目的主要重点是通过开发匿名化大型数据库的技术和工具来“解锁”此类关键数据集。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、州和联邦级的卫生部门，定期发布汇总形式的数据，因为它们可以用于回答对选定数据子集的统计查询。然而，现有的汇总方法有很大的弱点，拟议的改进措施规模不大。 此外，建立准确的预测模型的问题（例如，决策树）尚未被广泛研究。有很多机会从聚合数据中构建准确的预测模型以保护隐私;相反，构建这种模型的可能性表明了另一种方式，即使只发布聚合数据，敏感信息也可能会无意中“泄露”。该项目旨在调查隐私保障与发布数据用于特定分析任务之间的权衡，包括：（a）用于聚集大数据集的隐私保护算法，（B）用于使用聚集的数据构建预测模型的算法，以及（c）该项目小组包括国家首席流行病学家，威斯康星州，他是许多健康相关数据集的策展人，一位癌症研究员，他的研究项目部分依赖于该州策展的数据集，以及三位具有数据挖掘和管理专业知识的计算机科学家。该项目将在威斯康星州大学培训研究生和本科生，在隐私保护和准确模型构建之间进行权衡，成果和工具将通过出版物和项目网站（www-db.cs.wisc.edu/dbprivacy）广泛传播。

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).