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.

数字平台（例如云计算，区块链和机器学习服务）的兴起正在导致许多新的应用程序并改变日常生活。但是，用户缺乏对其他参与者的了解，在这些平台上建立信任是挑战。一个关键的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，这是一个重要的加密原始性，可简单地确保数据隐私和计算完整性。该项目推进了零知识证明方案的三个方面：理论，应用和系统级别。在理论方面，基于错误校正的代码和扩展器图构建了具有线性运行时间的新实用方案。在应用程序方面，该项目研究了机器学习算法和图形算法，并开发了针对这些应用程序量身定制的有效零知识证明。在系统方面，该项目启动了用于零知识证明的记忆效率和分布式算法的研究。该项目将使零知识证明的效率和可扩展性提高到一个新的水平，使其适用于行业中更广泛的工程师和开发人员社区。结果将使在数字平台上提供隐私保护和可验证数据挖掘的新应用程序，以保护用户的数据隐私。该项目还为本科和研究生网络安全教育开发了新的课程材料，并为扩大了代表性不足的群体和K-12学生的计算的参与。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。

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