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，可以弥合数据实用性和隐私性之间的鸿沟。MPC有助于在不泄露非必要信息的情况下对私有数据进行计算，满足隐私期望，并遵守监管隐私法。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。该项目旨在提高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自动化针对侧信道攻击的威胁评估产生重大的现实影响。像速率和容量这样的效率指标在加密上下文中是很自然的。定量调查MPC对侧信道攻击的鲁棒性和恢复也是很好的动机。这个项目建立了一个数学框架来确定最有效的MPC协议及其对侧信道攻击的鲁棒性。MPC速率、容量和鲁棒性的理论将建立在计算机科学、信息论和数学的交叉点上。该奖项反映了NSF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.