CAREER: Privacy Foundations for Practice and Policy

有关个人的高度个人信息被以前所未有的规模收集和分析。从这种分析中获得的见解有可能改变医学、社会科学和技术。然而，当数据的保管人没有工具来分析数据，同时保护个人隐私时，这种潜力往往无法实现。差分隐私为保证强大的个人隐私提供了一个框架，同时使尊重隐私的算法的系统设计成为可能。由于这些原因，它在工业和政府中得到越来越广泛的采用。这项研究面临着三大类挑战，这些挑战将使更广泛、更安全地采用不同的私人技术。首先是了解哪些统计推断和学习任务允许不同的私有解决方案，以及在计算资源方面的成本是多少。本研究利用隐私、通信复杂性和在线学习之间的联系，对何时可以实现隐私给出统一的表征。第二是基于计算启发式和回归算法开发新的算法范式，这将导致高维统计问题的实际解决方案。最终目标是开发新的数学工具，以更精确地理解差异隐私的保证并评估其下游影响。这些工具将构成科学界必须向决策者提供的指导的关键部分。这项研究与教育计划相结合，包括课程开发，研究生和本科生的研究培训，以及围绕敏感调查数据隐私的新的K-12体验活动。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。

Highly personal information about individuals is collected and analyzed at an unprecedented scale. The insights to be gained from such analyses have the potential to transform medicine, social science, and technology. However, this potential often goes unrealized when the custodians of data do not have the tools to analyze it while safeguarding individual privacy. Differential privacy provides a framework for guaranteeing strong individual privacy while enabling the systematic design of privacy-respecting algorithms. For these reasons, it is enjoying increasingly widespread adoption in both industry and government.This research confronts three broad classes of challenges which will enable the wider and safer adoption of differentially private technologies. The first is to understand which statistical inference and learning tasks admit differentially private solutions, and at what cost in computational resources. This research uses connections between privacy, communication complexity, and online learning to give a unified characterization of when privacy is achievable. The second is to develop new algorithmic paradigms, based on computational heuristics and regression algorithms, that will lead to practical solutions to high-dimensional statistical problems. The final objective is to develop new mathematical tools for more precisely understanding the guarantees of differential privacy and assessing its downstream impacts. Such tools will form a critical part of the guidance that the scientific community must provide to policymakers. This research is integrated with an educational plan that includes course development, research training for graduate and undergraduate students, and new K-12 experiential activities around privacy for sensitive survey data.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.