CAREER: Trading Security for Efficiency in Secure Computation

职业：以安全换取安全计算的效率

• 批准号: 1942575
• 负责人: Samuel Gordon
• 金额: $ 51.42万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942575&HistoricalAwards=false
• 关键词: CAREER; Trading; Security; Efficiency; Secure

Secure computation allows multiple parties, each holding private data, to perform arbitrary computations on the joint data without revealing anything other than the desired output. In the last fifteen years, secure computation has moved from being a purely theoretical area of research, to one that is being actively deployed in a variety of settings. However, technical barriers to adoption still exist: the communication and computational costs are quite high. At the same time, improvements in the last fifteen years have brought us quite near to the limit of what is feasible under standard definitions of security. This project investigates new techniques for improving the efficiency of secure computation beyond the known theoretical limits.This project explores new security relaxations, and new methods of utilizing existing security relaxations, in order to help scale secure computation to larger volumes of data, and a larger number of participating parties. The investigators envision a fully decentralized data plane in which user data is accessible by service providers only at the discretion of the data owner. Computations performed on this data would be subject to appropriate user-specified controls, and the results of the computations would reveal only what is minimally necessary to meet the goals of the data owner. Service providers could still monetize the data, but without ever seeing it, and only with the explicit agreement of the owner. The project has outreach activities that introduce secure computation, privacy and cryptography to various communities in the DC area.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.

安全计算允许各自持有私有数据的多方对联合数据执行任意计算，而不会泄露除所需输出之外的任何内容。 在过去的15年里，安全计算已经从一个纯理论的研究领域转变为一个正在积极部署在各种环境中的领域。 然而，采用的技术障碍仍然存在：通信和计算成本相当高。 与此同时，过去15年的进步使我们接近标准安全定义下可行的极限。本项目研究超越已知理论极限的提高安全计算效率的新技术。本项目研究新的安全松弛以及利用现有安全松弛的新方法，以帮助将安全计算扩展到更大的数据量和更多的参与方。研究人员设想了一个完全分散的数据平面，其中服务提供商只能根据数据所有者的决定访问用户数据。 对这些数据执行的计算将受到用户指定的适当控制，计算结果将只显示满足数据所有者目标所需的最低限度。 服务提供商仍然可以将数据货币化，但永远不会看到它，并且只有在所有者的明确同意下。该项目有推广活动，介绍安全计算，隐私和密码学在DC地区的各个社区。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Spreading the Privacy Blanket: Differentially Oblivious Shuffling for Differential Privacy

• DOI: 10.1007/978-3-031-09234-3_25
• 发表时间: 2021
• 作者: Dov S. Gordon;Jonathan Katz;Mingyu Liang;Jiayu Xu

Fully Secure PSI via MPC-in-the-Head

通过 MPC-in-the-Head 完全保护 PSI

• 发表时间: 2022
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Gordon, S. Dov;Hazay, Carmit;Le, Phi Hung
• 通讯作者: Le, Phi Hung

(∈, δ)-Indistinguishable Mixing for Cryptocurrencies

(, δ) - 加密货币的不可区分混合

• DOI: 10.2478/popets-2022-0004
• 发表时间: 2021
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Liang, Mingyu;Karantaidou, Ioanna;Baldimtsi, Foteini;Gordon, S. Dov;Varia, Mayank
• 通讯作者: Varia, Mayank

Secure Sampling with Sublinear Communication

使用次线性通信进行安全采样

• 发表时间: 2022
• 期刊: Springer
• 作者: Choi, Seung Geol;Dachman-Soled, Dana;Gordon, S. Dov;Liu, Linsheng;Yerukhimovich, Arkady
• 通讯作者: Yerukhimovich, Arkady