CAREER: An Axiomatic Basis for Statistical Privacy

职业：统计隐私的公理基础

• 批准号: 1054389
• 负责人: Daniel Kifer
• 金额: $ 42.95万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054389&HistoricalAwards=false
• 关键词: CAREER; Axiomatic; Basis; Statistical; Privacy

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

统计隐私是释放数据集的艺术，这些数据集提供了有关人口趋势的有用信息，而无需透露有关任何个人的私人信息。 AOL和Netflix发布的对数据集的最新备受瞩目的攻击表明，需要进行严格的应用程序特定的隐私定义来指导数据的匿名化。该项目的目的是开发模块化组件，称为隐私公理，可以将其链接在一起以创建自定义的隐私定义和匿名数据，以用于统计隐私应用程序。这种模块化可以使数据策展人无需在统计隐私方面具有广泛的专业知识来释放匿名数据，同时提供更容易解释和可靠的隐私保证。IntellectualFure：该项目旨在为统计隐私提供统一的框架，以使对隐私问题有更深入的了解，并为安全的匿名和释放灵敏数据提供指导。除了理论发展外，该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程序。Boader的影响：该项目追求的系统性隐私方法可以使您可以访问和分析在域中访问数据的匿名数据，否则可以限制数据访问数据。该项目旨在通过参与拟议研究的本科生来建立研究者先前在外展计划（例如夏季研究机会计划（SROP））等外展计划的经验。为了使学生准备未来的工作，需要分析匿名数据，这项研究也已集成到宾夕法尼亚州立大学的机器学习课程中。有关更多信息，请参见url的项目网站：http：//www.cse.psu.edu.edu/~dkifer/~dkifer/axiomatizationprivacyprivacy.html

项目成果

Detecting Outliers in Data with Correlated Measures

• DOI: 10.1145/3269206.3271798
• 发表时间: 2018-08
• 期刊: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
• 作者: Yu-Hsuan Kuo;Z. Li;Daniel Kifer

Differentially Private Hierarchical Count-of-Counts Histograms

• DOI: 10.14778/3236187.3236202
• 发表时间: 2018-04
• 期刊: Proc. VLDB Endow.
• 作者: Yu-Hsuan Kuo;Cho-Chun Chiu;Daniel Kifer;Michael Hay;Ashwin Machanavajjhala