CAREER: An Axiomatic Basis for Statistical Privacy

统计隐私是发布数据集的艺术，这些数据集提供有关人口趋势的有用信息，而不泄露任何个人的私人信息。最近AOL和Netflix发布的数据集受到了高调的攻击，这表明需要严格的特定于应用程序的隐私定义来指导数据的匿名化。该项目的目标是开发称为隐私公理的模块化组件，这些组件可以链接在一起，为统计隐私应用程序创建定制的隐私定义和匿名数据。这种模块化可以使在统计隐私方面没有丰富专业知识的数据管理员发布匿名数据，同时提供更可解释和可靠的隐私保证。智力优势：该项目旨在为统计隐私提供一个统一的框架，可以更深入地了解隐私问题，并为敏感数据的安全匿名化和发布提供指导。除了理论发展之外，这项研究计划还针对宾夕法尼亚州立大学和美国人口普查局的特定现有应用。更广泛的影响：该项目所追求的系统隐私方法可以在数据访问受到严格限制的领域访问和分析匿名数据。该项目旨在通过让本科生参与拟议的研究，建立在研究者之前的外展项目（如夏季研究机会计划（SROP））经验的基础上。为了让学生为未来需要分析匿名数据的工作做好准备，这项研究也被整合到宾夕法尼亚州立大学的机器学习课程中。欲了解更多信息，请参阅项目网站的URL:http://www.cse.psu.edu/~dkifer/axiomatizingprivacy.html

Statistical privacy is the art of releasing the datasets that provide useful information about population trends without revealing private information about any individual. Recent high-profile attacks on datasets released by AOL and Netflix demonstrate the need for rigorous application-specific privacy definitions to guide the anonymization of data. The goal of this project is to develop modular components, called privacy axioms, that can be chained together to create customized privacy definitions and anonymized data for statistical privacy applications. Such modularity can enable data curators without extensive expertise in statistical privacy to release anonymized data while providing privacy guarantees that are more interpretable and reliable.Intellectual merit: this project is designed to provide a unifying framework for statistical privacy that can bring about a deeper understanding of privacy issues and provide guidance for the safe anonymization and release of sensitive data. In addition to theoretical developments, this research plan also targets specific existing applications at Penn State and the U.S. Census Bureau.Broader impact: the systematic approach to privacy pursued by this project can enable access to and analysis of anonymized data in domains where access to data is otherwise heavily restricted. This project aims to build upon the investigator's prior experience with outreach programs such as the Summer Research Opportunities Program (SROP) by involving undergraduates in the proposed research. To prepare students for future work that requires analysis of anonymized data, this research is also being integrated into machine learning courses at Penn State.For further information see the project web site at the URL:http://www.cse.psu.edu/~dkifer/axiomatizingprivacy.html