CAREER: Exact Optimal and Data-Adaptive Algorithms and Tools for Differential Privacy

这项计划的动机是公众对隐私问题的关注日益增加，新的立法和对隐私增强技术的高需求，例如私营和公共部门应用中的差分隐私（DP）。该项目的首要主题是解决随着差异隐私从理论结构转变为实际技术而出现的紧迫新挑战。该项目推进了DP领域的最新研究，并为隐私教育做出了贡献。在研究方面，该项目开发了新的算法和分析工具，以实现更精确的隐私会计和更高的DP效用。在教育方面，该项目包括培训DP领域的未来领导者，创建教育材料和扩展一个名为autodp的开源软件库，该软件库使最先进的差异私人计算更容易获得。总的来说，综合的研究和教育活动有助于正在进行的合作努力，建立差异化隐私的创新应用。该项目在应用启发基础研究中有三个主要组成部分。第一个组件统一了DP的最新突破，如Renyi DP，矩会计，f-DP，并产生一个中间函数表示，允许这些表示之间的无损转换。第二个部分侧重于调查实际数据结构所允许的更强的隐私属性，并解决在平均情况数据上解释最坏情况隐私的困境。第三部分侧重于使用公共数据集“去噪”私有数据发布或促进私有机器学习。研究成果将透过整合于autodp图书馆而广泛分享，并将整合于课程中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project is motivated by the increasing public concerns on privacy issues, new legislations and the high demand for privacy enhancing technologies such as differential privacy (DP) in applications from both private and public sectors. The overarching theme of the project is to address the pressing new challenges that arise as differential privacy transforms from a theoretical construct into a practical technology. The project advances the state-of-the-art of research in the area of DP, and contributes to privacy education. On the research front, the project develops new algorithms and analytical tools that enable more precise privacy accounting and higher utility in DP. On the education front, the project involves training future leaders in DP areas, creating educational materials and expanding an open-source software library called autodp that makes state-of-the-art differentially private computation more accessible. Collectively, the integrated research and educational activities contribute to ongoing collaborative efforts in building innovative applications of differential privacy. The project has three main components in use-inspired fundamental research. The first component unifies the recent breakthroughs in DP, such as, Renyi DP, moments accountant, f-DP and produce an intermediate functional representation that allows lossless conversions among these representations. The second component focuses on investigating the stronger privacy properties permitted by the structures of the actual data, and addressing the dilemma of interpreting worst-case privacy on average-case data. The third component focuses on using a public dataset to ``denoise'' the private data releases or to facilitate private machine learning. The outputs of the research will be broadly shared through integration in autodp library, and will be integrated in courses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.