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

SaTC：核心：媒介：协作：以用户为中心的差异隐私部署

• 批准号: 1931443
• 负责人: Ninghui Li
• 金额: $ 34.31万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931443&HistoricalAwards=false
• 关键词: SaTC; CORE; Medium; Collaborative; User

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.

差分隐私（DP）已被研究界和其他领域接受为数据隐私的事实标准。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，以便更广泛地部署私人数据共享。 该项目的新奇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，以开发一种混合方法来共享私人数据，以实现更好的隐私-效用权衡。 该项目的影响是在推进国家的最先进的DP部署，特别是在一般的隐私保护。 更具体地说，该项目确定了DP数据共享的工作流程，提高了对DP通信的理解，并开发了新的算法，隐私概念和隐私机制，以支持DP的部署。 该项目有四项任务，将促进对以用户为中心的DP的理解，并为其部署奠定基础。(1)考察个人用户对DP的概念和保证以及隐私参数的影响的感知、理解和接受，并研究有效的方式来传达这些概念。 (2)从人为因素和人机交互领域实施方法，以确定私有数据共享中的任务、目标和工作流。 (3)为私有数据共享的混合方法开发关键算法和软件。 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有概要。 从这些边缘，可以合成新的数据集，或直接使用最大熵原则下的推理来回答查询。 混合方法增强了这与交互式查询回答，使信息提取不包括低度边缘。 (4)开发技术，以进一步改善私人数据共享中的隐私-效用权衡，包括在可识别信息下的差异隐私理论。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

MGD: A Utility Metric for Private Data Publication

• DOI: 10.1145/3491371.3491385
• 发表时间: 2021-12
• 期刊: Proceedings of the 8th International Conference on Networking, Systems and Security
• 作者: Zitao Li;Trung Dang;Tianhao Wang;Ninghui Li

Locally Decodable/Correctable Codes for Insertions and Deletions

用于插入和删除的本地可解码/可纠正代码

• DOI: 10.4230/lipics.fsttcs.2020.16
• 发表时间: 2020
• 期刊: 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science
• 作者: Block, A;Blocki, J;Grigorescu, E;Kulkarni, S;Zhu, M.
• 通讯作者: Zhu, M.

Towards Effective Differential Privacy Communication for Users’ Data Sharing Decision and Comprehension

• DOI: 10.1109/sp40000.2020.00088
• 发表时间: 2020-03
• 期刊: 2020 IEEE Symposium on Security and Privacy (SP)
• 作者: Aiping Xiong;Tianhao Wang;Ninghui Li;S. Jha

Computationally Relaxed Locally Decodable Codes, Revisited

重新审视计算宽松的本地可解码代码

• DOI: 10.1109/isit54713.2023.10206655
• 发表时间: 2023
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT
• 作者: Block, Alexander R.;Blocki, Jeremiah
• 通讯作者: Blocki, Jeremiah

Federated Matrix Factorization with Privacy Guarantee

• DOI: 10.14778/3503585.3503598
• 发表时间: 2021-12
• 期刊: Proc. VLDB Endow.
• 作者: Zitao Li;Bolin Ding;Ce Zhang;Ninghui Li;Jingren Zhou