SaTC: CORE: Medium: Collaborative: User-Centered Deployment of Differential Privacy

差分隐私 (DP) 已被研究界内外视为数据隐私的事实上的标准。公司和政府机构都在尝试部署 DP 技术。 然而，DP 技术的更广泛部署面临着挑战。 该项目旨在了解不同利益相关者在数据隐私方面的需求，并开发算法和软件以实现更广泛的隐私数据共享部署。 该项目的新颖之处在于，将社会科学研究人员的专业知识与具有 DP 相关理论和系统研究经验的计算机科学家的专业知识相结合，开发一种私人数据共享的混合方法，以实现更好的隐私与效用权衡。 该项目的影响在于推进了 DP 部署（特别是 DP 部署）和一般隐私保护方面的最先进技术。 更具体地说，该项目确定了 DP 数据共享的工作流程，提高对 DP 通信的理解，并开发新的算法、隐私概念和隐私机制来支持 DP 的部署。 该项目有四项任务，将加深对以用户为中心的DP的理解，并为其部署奠定基础。 （1）考察人类个体用户对DP的概念和保证以及隐私参数的效果的感知、理解和接受，并研究传达这些概念的有效方式。 （2）实施人为因素和人机交互领域的方法来确定私有数据共享中的任务、目标和工作流程。 (3) 开发私有数据共享混合方法的关键算法和软件。 在混合方法中，首先使用精心选择的低度边缘发布数据集的私人概要。 根据这些边际数据，人们可以合成新的数据集，或者使用最大熵原理下的推理直接回答查询。 混合方法通过交互式查询应答增强了这一点，从而能够提取低度边缘未覆盖的信息。 (4) 开发技术以进一步改善私人数据共享中的隐私与效用权衡，包括可发布信息下的差异隐私理论。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

Differential privacy (DP) has been accepted as the de facto standard for data privacy in the research community and beyond. Both companies and government agencies are trying to deploy DP technologies. Broader deployments of DP technology, however, face challenges. This project aims to understand the needs of different stakeholders in data privacy, and to develop algorithms and software to enable broader deployment of private data sharing. The project's novelty is combining the expertise of social science researchers with that of computer scientists who have both theoretical and system research experiences related to DP to develop a hybrid approach to private data sharing to achieve better privacy-utility tradeoff. The project's impacts are in advancing the state-of-the-art with regard to DP deployment in particular and privacy protection in general. More specifically the project identifies the workflow of DP data sharing, improve understanding of DP communication, and develop new algorithms, privacy concepts, and privacy mechanisms to support deployment of DP. The project has four tasks that will advance the understanding of user-centered DP and lay a foundation for its deployment. (1) Examine individual human users' perception, comprehension and acceptance of the concept and guarantee of DP and the effect of privacy parameter, and to investigate effective ways to communicate those concepts. (2) Implement methods from the domains of human factors and human-computer interaction to identify tasks, goals, and workflow in private data sharing. (3) Develop key algorithms and software for a hybrid approach of private data sharing. In the hybrid approach, one first publishes a private synopsis of dataset using carefully selected low-degree marginals. From these marginals, one can either synthesize new datasets, or answer queries directly using inference under the maximum entropy principle. The hybrid approach enhances this with interactive query answering, enabling extraction of information not covered by low-degree marginals. (4) Develop techniques to further improve the privacy-utility tradeoff in private data sharing, including a theory of differential privacy under publishable information.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.