TWC: Large: Collaborative: Computing Over Distributed Sensitive Data

有关个人的信息由各种组织收集，包括政府机构、银行、医院、研究机构和私人公司。在许多情况下，在组织之间共享这些数据可以在社会、科学、商业和安全领域带来好处，因为收集的信息具有相似的性质，属于相似的人群。然而，这些收集的数据大多是敏感的，因为它包含个人信息，或者可能损害组织声誉或竞争力的信息。因此，出于道德、法律或商业原因，数据共享经常受到限制。该项目开发了一系列工具，可以在不让数据所有者共享数据的情况下实现数据共享的好处。开发的技术尊重数据所有权和隐私要求的原则，并借鉴了隐私、密码学、机器学习、计算统计、程序验证和系统安全方面的最新科学发展。在这个项目中开发的工具将有助于现有的研究和商业基础设施，从而能够以新的方式创造信息的价值，否则这些信息的使用将受到限制。该项目支持开发新的课程材料，并培训新一代研究人员和公民，他们具有解决围绕数据隐私的复杂问题所需的多学科视角。

Information about individuals is collected by a variety of organizations including government agencies, banks, hospitals, research institutions, and private companies. In many cases, sharing this data among organizations can bring benefits in social, scientific, business, and security domains, as the collected information is of similar nature, of about similar populations. However, much of this collected data is sensitive as it contains personal information, or information that could damage an organization's reputation or competitiveness. Sharing of data is hence often curbed for ethical, legal, or business reasons. This project develops a collection of tools that will enable the benefits of data sharing without having the data owners share the data. The techniques developed respect principles of data ownership and privacy requirement, and draw on recent scientific developments in privacy, cryptography, machine learning, computational statistics, program verification, and system security. The tools developed in this project will contribute to the existing research and business infrastructure, and hence enable new ways to create value in information whose use would have been otherwise restricted. The project supports the development of new curricula material and train a new generation of researchers and citizens with the multidisciplinary perspectives required to address the complex issues surrounding data privacy.