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-PI：Vitaly Shmatikov（德克萨斯大学奥斯汀分校）Co-PI：Joan Feigenbaum（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，防止未经授权的搜索、滥用和误用。 传统的解释隐私的机密性和/或不可访问的任何单独的一块信息是不够的，在协作环境中，一些合作者可能会被合法地允许访问部分的联合数据集，并没有信任的假设，可以对他们的计算平台。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收集的安全性。 技术包括可证明安全的数据转换，其在响应于合法请求而发布信息之后确保全局和个人隐私属性。 另一个研究目标是明确纳入信息价值的经济措施的隐私理论。 为了执行全球隐私政策，该项目将开发新的加密技术，用于数据集混淆和清理，确保在数据集传输给合作者后，只能对数据集进行符合政策的查询。主要目标是设计可证明安全的隐私保护数据转换，而无需对“防篡改”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，从多机构临床试验中的患者数据联合分析到执法机构的交易监控，这些都符合公民的第四修正案权利，以防止不合理的搜索。 项目网址：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. 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/