CIF: Small: Efficiency and Robustness of Secure Computation

安全多方计算（MPC）是一种领先的隐私增强技术，它弥合了数据效用和隐私鸿沟。MPC促进了对私人数据的计算，而不会泄露不必要的信息，满足隐私期望，并遵守隐私法规。MPC在社会、科学、商业和安全领域创造了令人兴奋的合作机会。本项目旨在提高MPC的安全性和效率，从而提高其采用率。该项目的成果将通过指导计算机辅助安全协议设计和针对侧信道攻击的自动化威胁评估，对现实世界产生重大影响。表征MPC的效率和稳健性尚处于起步阶段。像速率和容量这样的效率指标在密码学环境中是很自然的。定量研究MPC对侧信道攻击的鲁棒性和从中恢复也是很有动机的。该项目建立了一个数学框架，以确定最有效的MPC协议及其对侧信道攻击的鲁棒性。这个关于MPC速率、容量和鲁棒性的理论将建立在计算机科学、信息理论和数学的交叉点上。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。

Secure multi-party computation (MPC) is a leading privacy-enhancing technology bridging the data utility and privacy chasm. MPC facilitates computing over private data without revealing non-essential information, meeting privacy expectations, and complying with regulatory privacy laws. MPC creates exciting collaboration opportunities in social, scientific, commercial, and security domains. This project aims to improve the security and efficiency of MPC, thereby increasing its adoption. The project's outcomes will have a significant real-world impact by guiding computer-assisted secure protocol design and automating threat assessment against side-channel attacks.Characterizing MPC's efficiency and robustness is in its infancy. Efficiency metrics like rate and capacity are natural in the cryptographic context. Quantitative investigations into the robustness of MPC against side-channel attacks and recovery from them are also well-motivated. This project builds a mathematical framework to determine the most efficient MPC protocols and their robustness to side-channel attacks. This theory for MPC rate, capacity, and robustness will be built at the intersection of computer science, information theory, and mathematics.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.