基于TEE的多客户端函数加密方案研究
Research on Multi-Client Functional Encryption Scheme Based on TEE
摘要: 随着云计算的兴起，对加密数据进行联合计算变的越来越重要。近年来，多客户端函数加密的发展使用户能够在不需要任何交互的情况下对私有输入进行联合计算。在云计算中，确保安全性与可靠性是极为重要的需求。针对多客户端函数加密方案出现的隐私安全问题，本文提出了一种基于TEE技术的去中心化多客户端函数加密方案。利用Python语言、开发环境PyCharm 2020.3.0版本、以及Python的PyCryptodome V3.10.1密码学库下对本文方案的关键算法或协议进行了仿真实验。综上试验结果及分析表明本方案在保障隐私安全的同时，具有较好的计算性能。这为在云计算环境中处理敏感数据提供了一种高效且安全的解决方案，因此具有一定的实际应用意义。
Abstract: With the rise of cloud computing, joint computing of encrypted data is becoming increasingly important. In recent years, the development of multi-client function encryption has enabled users to perform joint computations on private inputs without any interaction. In cloud computing, ensuring security and reliability is an extremely important requirement. Aiming at the privacy and security problems of multi-client function encryption schemes, this paper proposes a decentralized multi-client function encryption scheme based on Intel SGX technology. Using Python language, development environment PyCharm 2020.3.0 version, and Python PyCryptodome V3.10.1 cryptography library, the key algorithms or protocols of this scheme are simulated. In summary, the experimental results and analysis show that the scheme has good computational performance while protecting privacy and security. This provides an efficient and secure solution for processing sensitive data in the cloud computing environment, so it has certain practical application significance.
文章引用：程钰雯, 岳笑含. 基于TEE的多客户端函数加密方案研究[J]. 计算机科学与应用, 2024, 14(6): 32-40. https://doi.org/10.12677/csa.2024.146139

参考文献

[1] Diffie, W. and Hellman, M.E. (2022) New Directions in Cryptography. In: Slayton, R., Ed., Democratizing Cryptography: The Work of Whitfield Diffie and Martin Hellman, Association for Computing Machinery, 365-390.
[2] 陈立全, 张林樾, 陈垚. 一种结合可验证数据库的属性可搜索加密方案[J]. 密码学报, 2022, 9(5): 910-922.
[3] Kim, S., Lewi, K., Mandal, A., et al. (2018) Function-Hiding Inner Product Encryption Is Practical. In: International Conference on Security and Cryptography for Networks, Springer International Publishing, 544-562.
[4] 张志强, 朱友文, 王箭, 等. 基于内积谓词的属性基隐私保护加密方案[J]. 电子与信息学报, 2023, 45(3): 828-835.
[5] Mehibel, N. and Hamadouche, M. (2019) A New Enhancement of Elliptic Curve Digital Signature Algorithm. Journal of Discrete Mathematical Sciences and Cryptography, 23, 743-757. [Google Scholar] [CrossRef] 
[6] Chandramouli, A., Choudhury, A. and Patra, A. (2022) A Survey on Perfectly Secure Verifiable Secret-Sharing. ACM Computing Surveys, 54, 1-36. [Google Scholar] [CrossRef] 
[7] 钱文君, 沈晴霓, 吴鹏飞, 等. 大数据计算环境下的隐私保护技术研究进展[J]. 计算机学报, 2022, 45(4): 669-701.
[8] Felsen, S., Kiss, Á., Schneider, T. and Weinert, C. (2019) Secure and Private Function Evaluation with Intel SGX. Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop, London, 11 November 2019, 165-181. [Google Scholar] [CrossRef] 
[9] Zheng, W., Wu, Y., Wu, X., Feng, C., Sui, Y., Luo, X., et al. (2020) A Survey of Intel SGX and Its Applications. Frontiers of Computer Science, 15, Article ID: 153808. [Google Scholar] [CrossRef] 
[10] Fisch, B., Vinayagamurthy, D., Boneh, D., et al. (2017) Iron: Functional Encryption Using Intel SGX. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, 30 October-3 November 2017, 765-782.
[11] Abdalla, M., Bourse, F., De Caro, A., et al. (2015) Simple Functional Encryption Schemes for Inner Products. In: IACR International Workshop on Public Key Cryptography, Springer, 733-751.
[12] Mera, J.M.B., Karmakar, A., Marc, T., et al. (2022) Efficient Lattice-Based Inner-Product Functional Encryption. In: IACR International Conference on Public-Key Cryptography, Springer International Publishing, 163-193.
[13] Agrawal, S., Libert, B., Maitra, M., et al. (2020) Adaptive Simulation Security for Inner Product Functional Encryption. In: IACR International Conference on Public-Key Cryptography, Springer International Publishing, 34-64.
[14] Shi, E., Chan, H.T.H., Rieffel, E., et al. (2011) Privacy-Preserving Aggregation of Time-Series Data. In: Annual Network & Distributed System Security Symposium (NDSS), Internet Society, 489-505.
[15] Benhamouda, F., Joye, M. and Libert, B. (2016) A New Framework for Privacy-Preserving Aggregation of Time-Series Data. ACM Transactions on Information and System Security, 18, 1-21. [Google Scholar] [CrossRef]