CAREER: Towards Privacy and Confidentiality Preserving Databases

is - 0546027xintao Wu xwu@uncc.eduUniversity， North Carolina at charlottec职业：迈向隐私和保密保护数据库许多来自政府、商业和非营利组织的数据库维护着大量敏感或机密信息的数据，如收入和医疗记录。因此，保护这类数据库的隐私和机密性是首要问题。当数据库所有者导出或发布其敏感或机密数据时，通常采用数据扰动方法。然而，在实践中，由于用于披露分析的数据空间几乎是无限的，因此很难量化和评估数据效用与披露风险之间的权衡。本项目开发了一种新颖的基于模型的披露分析方法，该方法首先建立统计模型，并在模型的参数水平上分析潜在的披露。由于参数的搜索空间比数据的搜索空间小得多，并且攻击者可以获得的所有信息都包含在这些参数中，因此该方法更加有效和高效。本项目还对基于摄动的方法进行了理论研究，针对以往的研究仅进行了经验评价，建立了各种重建方法的施工精度与添加噪声之间的显式形式。该项目的成果将提供一个原型系统，该系统可以使用基于模型和基于随机化的方法充分进行披露分析，以满足用户复杂的隐私和保密规范。该系统旨在为当地工业伙伴和其他组织使用。该项目的教育影响将包括研究生和本科生的参与，并将研究项目纳入与数据库安全和隐私相关的课程。为提高国家信息安全能力，将培养2名博士研究生。所有结果，包括出版物、实证研究和软件将通过项目网站（http://www.cs.uncc.edu/~xwu/career）传播。

IIS-0546027Xintao Wu xwu@uncc.eduUniversity of North Carolina at CharlotteCAREER: Towards Privacy and Confidentiality Preserving DatabasesMany databases from government, commercial and non-profit organizations maintain a huge amount of data on sensitive or confidential information such as income and medical records. As a result, protecting the privacy and confidentiality of such databases is of primary concern. Data perturbation approach is often adopted when database owners export or publish their sensitive or confidential data. However, it is very hard to quantify and evaluate the tradeoffs between the data utility and the disclosure risk in practice since the data space which is used for disclosure analysis is almost infinite. This project develops a novel model-based disclosure analysis approach which builds statistical models first and analyzes potential disclosure at the models' parameter level. Since the search space of parameters is much smaller than that of data and all information which attackers can derive is contained in those parameters, this approach is more effective and efficient. This project also conducts the theoretical study of perturbation based approach by developing the explicit form between construction accuracy and noise added for various reconstruction methods since previous research only conducted empirical evaluations. The results of this project will provide a prototype system which can fully conduct disclosure analysis using both model based and randomization based approaches to satisfy users' complex privacy and confidentiality specifications. The system aims to be used by local industry partners and other organizations. Education impacts of this project will include involvement of graduate and undergraduate students and incorporation of research projects into courses related to database security and privacy. Two Ph.D. graduate students will be produced to enhance the nation's capability in information security. All results including publications, empirical studies and software will be disseminated via the project web site (http://www.cs.uncc.edu/~xwu/career).