权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Pushing the Practicality of Secure Multiparty Computation

职业：推动安全多方计算的实用性

基本信息

批准号：
2236819
负责人：
Xiao Wang
金额：
$ 57.89万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236819&HistoricalAwards=false
关键词：
CAREER Pushing Practicality Secure Multiparty

项目摘要

Advances in data-driven technologies offer benefits through the centralized collection of user data. However, data centralization and sharing also raise critical security and privacy needs. Secure multi-party computation (MPC) allows a set of parties to compute any function jointly and reveals nothing but the output. It is a crucial tool to enable private data computation without sharing it. This project focuses on further pushing the practicality of MPC protocols. The project's novelties are identifying the hurdles of deploying MPC protocols in real-world settings and designing efficient, robust, and scalable protocols to overcome these issues. The project's broader significance and importance are two-fold: 1) by pushing the use of MPC, the project enables new beneficial applications without sacrificing privacy; 2) the project involves educational and outreach activities to enhance MPC education for students at different stages, improve social welfare, and promote diversity of participation. This project focuses on three aspects of bringing MPC to practice. The first aspect is on secure two-party computation protocols in the malicious setting as efficient and scalable as their semi-honest counterparts. Second, the project investigates MPC efficiency when used for big data in application-specific settings and improved RAM-based MPC protocols for real-world workloads. Third, the project develops new classes of protocols that can scale MPC to hundreds of parties and compute huge statements even when the adversary can corrupt many participants. Finally, the research outcomes from this award are applied and evaluated via academic collaboration and industry deployment.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.

数据驱动技术的进步通过集中收集用户数据提供了好处。然而，数据集中和共享也提出了关键的安全和隐私需求。安全多方计算（MPC）允许一组各方共同计算任何函数，并且只显示输出。它是实现私有数据计算而不共享数据的关键工具。本项目旨在进一步推动MPC协议的实用化。该项目的创新之处在于确定了在现实环境中部署MPC协议的障碍，并设计了高效、健壮和可扩展的协议来克服这些问题。该项目更广泛的意义和重要性有两个方面：1)通过推动MPC的使用，该项目在不牺牲隐私的情况下实现了新的有益应用；2)透过教育及外展活动，加强不同阶段学生的MPC教育，改善社会福利，促进多元参与。本项目侧重于将MPC应用于实践的三个方面。第一个方面是在恶意设置下的安全的双方计算协议，其效率和可扩展性与半诚实的对等协议一样。其次，该项目研究了MPC在特定应用环境下用于大数据时的效率，并改进了基于ram的MPC协议，用于实际工作负载。第三，该项目开发了新的协议类别，可以将MPC扩展到数百个参与方，即使对手可以破坏许多参与方，也可以计算大量语句。最后，通过学术合作和行业部署，对该奖项的研究成果进行应用和评估。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。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Actively Secure Half-Gates with Minimum Overhead under Duplex Networks

DOI：
10.1007/978-3-031-30617-4_2
发表时间：
2023
期刊：
IACR Cryptol. ePrint Arch.
影响因子：
0
作者：
Hongrui Cui;Xiao Wang;Kang Yang;Yu Yu-Yu
通讯作者：
Hongrui Cui;Xiao Wang;Kang Yang;Yu Yu-Yu

Ou: Automating the Parallelization of Zero-Knowledge Protocols

Ou：零知识协议的自动化并行化

DOI：
10.1145/3576915.3616621
发表时间：
2023
期刊：
SIGSAC Conference on Computer and Communications Security
影响因子：
0
作者：
Sang, Yuyang;Luo, Ning;Judson, Samuel;Chaimberg, Ben;Antonopoulos, Timos;Wang, Xiao;Piskac, Ruzica;Shao, Zhong
通讯作者：
Shao, Zhong

Half-Tree: Halving the Cost of Tree Expansion in COT and DPF

半树：将 COT 和 DPF 中树扩展的成本减半