CAREER: Towards Efficient and Scalable Zero-Knowledge Proofs

职业：迈向高效且可扩展的零知识证明

• 批准号: 2144625
• 负责人: Yupeng Zhang
• 金额: $ 50万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144625&HistoricalAwards=false
• 关键词: CAREER; Towards; Efficient; Scalable; Zero

The rise of digital platforms, such as cloud computing, blockchains, and machine learning services, is leading to numerous new applications and transforming daily life. However, users lack knowledge of other participants and it is challenging to establish trust on these platforms. A key research question is determining how users can protect the privacy of their data, and ensure that the computations performed by others are valid. The focus of this project is on developing efficient and scalable zero-knowledge proof schemes, an important cryptographic primitive to ensure data privacy and computation integrity simultaneously. The project advances three aspects of the zero-knowledge proof schemes: theory, application and system level. On the theory side, new practical schemes with linear running time in the size of the computation are constructed based on error-correcting codes and expander graphs. On the application side, the project investigates machine learning algorithms and graph algorithms and develops efficient zero-knowledge proofs tailored for these applications. On the system side, the project initiates the study of memory-efficient and distributed algorithms for zero-knowledge proofs. The project will bring the efficiency and scalability of zero-knowledge proof to the next level, making it applicable and accessible to the broader community of engineers and developers in the industry. The results will enable new applications of privacy-preserving and verifiable data mining on digital platforms to protect users’ data privacy. The project also develops new course materials for undergraduate and graduate cybersecurity education, and for broadening the participation in computing of underrepresented groups and K-12 students.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.

云计算、区块链和机器学习服务等数字平台的兴起正在催生大量新应用并改变日常生活，但用户缺乏对其他参与者的了解，在这些平台上建立信任具有挑战性。研究问题是确定用户如何保护其数据的隐私，并确保其他人执行的计算是有效的。该项目的重点是开发高效且可扩展的零知识证明方案，这是确保数据隐私的重要密码原语。该项目同时推进了三个方面。零知识证明方案：理论、应用和系统层面 在理论方面，基于纠错码和扩展图构建了具有计算规模的线性运行时间的新实用方案。在系统方面，该项目启动了零知识证明的内存高效和分布式算法的研究，该项目将带来零知识证明的效率和可扩展性。知识证明该项目还开发了新的隐私保护和可验证数据挖掘应用程序，以保护用户的数据隐私。用于本科生和研究生网络安全教育的新课程材料，以及扩大代表性不足群体和 K-12 学生对计算的参与。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

Orion: Zero Knowledge Proof with Linear Prover Time

• DOI: 10.1007/978-3-031-15985-5_11
• 发表时间: 2022
• 作者: Tiancheng Xie;Yupeng Zhang;D. Song

zkBridge: Trustless Cross-chain Bridges Made Practical

• DOI: 10.1145/3548606.3560652
• 发表时间: 2022-10
• 期刊: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Tiancheng Xie;Jiaheng Zhang;Zerui Cheng;Fan Zhang;Yupeng Zhang;Yongzheng Jia;D. Boneh;D. Song