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（得克萨斯大学奥斯汀分校）联合PI: Joan Feigenbaum（耶鲁大学）当协作涉及个人和组织的敏感数据共享时，必须保护这些数据免受未经授权的搜索、滥用和误用。在协作环境中，将隐私解释为任何单独信息的保密性和/或不可访问性的传统解释是不充分的，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内容，并且无法对其计算平台做出信任假设。该项目的目标是为协作环境中的隐私开发新的概念和框架，重点关注联合数据集的全局属性，例如防止不合理搜索和滥用信息收集的安全性。技术包括可证明的安全数据转换，在响应合法请求发布信息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经济衡量标准的隐私理论。为了执行全球隐私政策，该项目将开发用于数据集混淆和清理的新加密技术，确保在数据集传输给协作者后，只能在数据集上计算符合策略的查询。主要目标是设计可证明是安全的保护隐私的数据转换，而不需要对“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使重要的协作应用成为可能，从多机构临床试验中对患者数据的联合分析，到执法机构对交易的监控，这些都符合公民的第四修正案权利，即免受不合理的搜查。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/