Collaborative Research: New Privacy Frameworks for Collaborative Information Sharing

协作研究：协作信息共享的新隐私框架

• 批准号: 0534198
• 负责人: Vitaly Shmatikov
• 金额: $ 15.15万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534198&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Privacy; Frameworks

Collaborative Research:New Privacy Frameworks for Collaborative Information Sharing------------------------------------------------------------PI: Vitaly Shmatikov (The University of Texas at Austin)Co-PI: Joan Feigenbaum (Yale University)When collaborations involve sharing of sensitive data about individuals and organizations, these data must be protected from unauthorized searches, abuse, and misuse. Conventional interpretations of privacy as confidentiality and/or inaccessibility of any individual piece of information are inadequate in collaborative environments, where some collaborators may legitimately be allowed to access parts of the joint dataset, and no trust assumptions can be made about their computing platforms.The goal of this project is to develop new concepts and frameworks for privacy in collaborative environments, focusing on global properties of the joint dataset such as security against unreasonable searches and abusive information harvesting. Techniques include provably secure data transformations that assure global and individual privacy properties after information has been released in response to a legitimate request. Another research objective is a theory of privacy that explicitly incorporates economic measures of information value. To enforce global privacy policies, this project will develop new cryptographic techniques for dataset obfuscation and sanitization, ensuring that only policy-compliant queries can be computed on the dataset after it has been transferred to the collaborators.The main objective is to design privacy-preserving data transformations that are provably secure without unrealistic assumptions about "tamper-proof" software or hardware. Privacy technologies developed in the course of the project will enable important collaborative applications, ranging from joint analysis of patient data in multi-institution clinical trials to transaction monitoring by law enforcement agencies that complies with the citizens' Fourth Amendment right to be secure against unreasonable searches.Project URL: http://www.cs.utexas.edu/~shmat/privacyframeworks/

协作研究：协作信息的新隐私框架Sharing------------------------------------------------------------PI：维塔利·什马提科夫(德克萨斯大学奥斯汀分校)合作：琼·费根鲍姆(耶鲁大学)当协作涉及共享有关个人和组织的敏感数据时，必须保护这些数据不受未经授权的搜索、滥用和误用。在协作环境中，传统的隐私解释为任何单个信息的机密性和/或不可访问性是不充分的，在协作环境中，一些合作者可能被合法地允许访问联合数据集的部分，并且不能对他们的计算平台做出信任假设。本项目的目标是开发协作环境中的隐私的新概念和框架，重点关注联合数据集的全局属性，例如针对不合理搜索和滥用信息获取的安全性。技术包括可证明安全的数据转换，在响应合法请求发布信息后，确保全球和个人隐私属性。另一个研究目标是一种隐私理论，它明确地纳入了信息价值的经济衡量标准。为了执行全球隐私政策，该项目将开发用于数据集混淆和净化的新密码技术，确保在将数据集传输给合作者后，只能计算符合策略的查询。主要目标是设计可证明安全的隐私保护数据转换，而不会对软件或硬件进行不切实际的假设。在项目过程中开发的隐私技术将实现重要的协作应用，范围从多机构临床试验中对患者数据的联合分析到执法机构的交易监控，这符合公民免受不合理搜索的第四修正案的权利。项目网址：http://www.cs.utexas.edu/~shmat/privacyframeworks/