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: 0534052
• 负责人: Joan Feigenbaum
• 金额: $ 21.02万
• 依托单位: Yale University
• 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=0534052&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/

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. 在协作环境中，可以允许某些合作者可以合法地访问联合数据集的一部分，并且可以对其计算平台做出任何信任假设，并且该项目的目标是为了开发新的概念和框架，在此概念上，在协作中，对任何单个信息的保密性解释是任何个人信息的机密性和/或无法访问的性能是不充分的，在此过程中，没有任何信任假设。搜索和滥用信息收集。 技术包括可证明的安全数据转换，这些数据转换可确保根据合法请求发布信息后全球和个人隐私属性。 另一个研究目标是隐私理论，该理论明确纳入了信息价值的经济措施。 为了执行全球隐私政策，该项目将开发用于数据集混淆和消毒的新的加密技术，以确保只能将其转移到合作者之后，在数据集中可以在数据集上计算出符合策略的查询。主要目标是设计隐私的数据转换，而无需设计不明智的假设，而无需设计隐私的数据转换。 在项目过程中开发的隐私技术将实现重要的协作应用程序，从对多机构临床试验中的患者数据进行联合分析到执法机构的交易监控，这些执法机构符合公民的第四修正案，以确保不合理的搜索。 项目URL：http：//www.cs.utexas.edu/~shmat/privacyframeworks/