HNDS-I: Bringing Differential Privacy to Social Science Data Repositories

要解决人类社会的许多最重要和最麻烦的问题，需要有能力解释从世界各地数十亿人那里收集的非常庞大、详细和高度信息量的数据集。这些数据来自手机记录、保险记录、医疗记录、社交媒体和网络流量等来源。然而，这些数据包含识别信息，如果向公众提供，可能会对涉案人员造成危险或尴尬。因此，重要的是找到方法，让科学家能够进行必要的研究，以了解是什么导致了社会的弊病，以及如何在不侵犯任何人的个人隐私的情况下解决这些问题。该项目构建基于社区的开源软件工具，使科学家能够安全地访问、分析和共享敏感数据集，并从数学上保证这些数据集中可能代表的个人的隐私。该项目基于差异隐私的数学理论，该理论以隐藏个人级别信息的方式改变数据。即使个人层面的信息是隐藏的，差异隐私仍然允许研究人员研究数据中的群体层面模式。该项目构建了用户友好的、公开可用的交互式差异隐私工具，可以与广泛使用的数据存储库集成。这些工具甚至可以被那些在差异隐私方面没有专业知识的人有效地使用。该项目还创建了新的统计方法，以确保这些工具对社会、行为和经济研究有用。该项目创建的基础设施将允许私人公司、政府机构和其他组织与研究人员共享数据，同时保持对不会发生侵犯隐私行为的信心。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Solving many of the most important and troubling problems of human society will require the ability to interpret the very large, detailed, and highly informative sets of data collected from billions of people around the world. These data come from sources such as cell phone records, insurance records, medical records, social media, and web traffic. However, these data contain identifying information that, if made available to the public, could be dangerous or embarrassing to the people involved. It is important, therefore, to find ways that scientists can conduct the research necessary to learn about what causes society’s ills and how to fix them without violating anyone’s individual privacy. This project builds open source, community-based software tools that will let scientists safely access, analyze, and share sensitive datasets, with mathematical guarantees for the privacy of the individuals who may be represented in those datasets. The project is based on the mathematical theory of differential privacy, which changes the data in a way that hides individual-level information. Even though the individual-level information is hidden, differential privacy still allows researchers to study the population-level patterns in the data. The project builds user-friendly, publicly available interactive differential privacy tools that can integrate with widely used data repositories. These tools can be effectively employed even by those without expertise in differential privacy. The project also creates new statistical methods that ensure that the tools are useful for social, behavioral, and economic research. The infrastructure created by this project will allow private companies, government agencies, and other organizations to share data with researchers while remaining confident that privacy violations cannot occur.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.