数据租赁——数据流通的新方式

doi:10.11959/j.issn.2096-0271.2022071

大数据

• •

数据租赁——数据流通的新方式

阮雯强，徐铭辛，涂新宇，宋鲁杉，韩伟力

1.复旦大学数据分析与安全实验室
2.上海市数据科学重点实验室

作者简介: 阮雯强（1999-），男，复旦大学计算机科学技术学院博士研究生，主要研究方向为基于安全多方计算的隐私保护机器学习，差分隐私等。徐铭辛（1997-），男，复旦大学软件学院硕士研究生，主要研究方向为基于安全多方计算的隐私保护机器学习，差分隐私等。涂新宇（1999-），男，复旦大学软件学院硕士研究生，主要研究方向为基于安全多方计算的隐私保护机器学习，秘密共享等。宋鲁杉（1999-），女，复旦大学计算机科学技术学院博士研究生，主要研究方向为基于安全多方计算的隐私保护机器学习等。韩伟力（1975-），男，博士，复旦大学计算机科学技术学院教授，主要研究方向为数据安全、访问控制。

Data tenancy: a new paradigm for data circulation

RUAN Wenqiang, XU Mingxin, TU Xinyu, SONG Lushan, HAN Weili#br#

1. Laboratory for Data Analytics and Security, Fudan University, Shanghai 200438, China
2. Shanghai Key Laboratory of Data Science

摘要/Abstract

摘要： 数据正成为推动社会发展的新生产要素。以合规的、可审计的方式使数据在多方之间流通对于数据价值的形成至关重要。从隐私保护以及数据利用的角度，提出了一种新的数据流通方式：数据租赁。介绍了提出数据租赁的动机，并明确了数据租赁应当满足的五项需求。最后，提出了一项基于秘密共享的数据租赁技术。

关键词: 数据流通, 秘密共享, 数据租赁, 隐私保护 ,

Abstract: Data are becoming a new type of factor of production. How to compliantly and audibly circulate data among multiple parties is very important for data value formation. A novel data circulation paradigm, namely data tenancy, is proposed from the perspective of privacy preservation and data utilization. The motivation of data tenancy is discussed, and five requirements that data tenancy should satisfy are identified. Finally, a secret sharing-based data tenancy technique is proposed.

Key words: data circulation, secret sharing, data tenancy, privacy preservation

阮雯强, 徐铭辛, 涂新宇, 宋鲁杉, 韩伟力. 数据租赁——数据流通的新方式[J]. 大数据, doi: 10.11959/j.issn.2096-0271.2022071.

RUAN Wenqiang, XU Mingxin, TU Xinyu, SONG Lushan, HAN Weili. Data tenancy: a new paradigm for data circulation[J]. Big Data Research, doi: 10.11959/j.issn.2096-0271.2022071.

参考文献

[1] XU, X. Research prospect: data factor of production. Journal of Internet and Digital Economics, Vol. 1 No. 1, pp. 64-71.
[2] SONG L, WU H, RUAN W, HAN W: SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation[J]. CoRR abs/2012.03386 (2020).
[3] CRAMER R and IVAN B D. Secure multiparty computation[M]. Cambridge University Press, 2015.
[4] ODED G, SILVIO M, and AVI W. How to play ANY mental game. In Proceedings of the nineteenth annual ACM symposium on Theory of computing (STOC '87) [C]. Association for Computing Machinery, New York, NY, USA, 218–229.
[5] WIGDERSON A, OR M B, GOLDWASSER S. Completeness theorems for noncryptographic fault-tolerant distributed computations[C]//Proceedings of the 20th Annual Symposium on the Theory of Computing (STOC’88). 1988: 1-10.
[6] MOHASSEL P, ZHANG Y. Secureml: A system for scalable privacy-preserving machine learning[C]//2017 IEEE symposium on security and privacy (SP). IEEE, 2017: 19-38.
[7] MOHASSEL P, RINDAL P. ABY3: A mixed protocol framework for machine learning[C]//Proceedings of the 2018 ACM SIGSAC conference on computer and communications security. 2018: 35-52.
[8] DALSKOV A, ESCUDERO D, KELLER M. Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious Security[C]//30th USENIX Security Symposium (USENIX Security 21). 2021: 2183-2200.
[9] KOTI N, PANCHOLI M, PATRA A, et al. {SWIFT}: Super-fast and Robust {Privacy-Preserving} Machine Learning[C]//30th USENIX Security Symposium (USENIX Security 21). 2021: 2651-2668.
[10] PATRA A, SURESH A. BLAZE: blazing fast privacy-preserving machine learning[J]. arXiv preprint arXiv:2005.09042, 2020.
[11] TAN S, KNOTT B, TIAN Y, et al. CryptGPU: Fast privacy-preserving machine learning on the GPU[C]//2021 IEEE Symposium on Security and Privacy (SP). IEEE, 2021: 1021-1038.
[12] WAGH S, TOPLE S, BENHAMOUDA F, et al. Falcon: Honest-majority maliciously secure framework for private deep learning[J]. arXiv preprint arXiv:2004.02229, 2020.
[13] LI T, SAHU A K, TALWALKAR A, et al. Federated learning: Challenges, methods, and future directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50-60.
[14] YANG Q, LIU Y, CHEN T, et al. Federated machine learning: Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2019, 10(2): 1-19.
[15] 王健宗, 孔令炜, 黄章成, 陈霖捷, 刘懿, 卢春曦, 肖京. 联邦学习隐私保护研究进展[J]. 大数据, 2021, 7(3): 2021030.
[16] 周传鑫, 孙奕, 汪德刚, 葛桦玮. 联邦学习研究综述[J]. 网络与信息安全学报, 2021, 7(5): 77-92.
[17] ZHU L, LIU Z, HAN S. Deep leakage from gradients[J]. Advances in Neural Information Processing Systems, 2019, 32.
[18] JERE M S, FARNAN T, KOUSHANFAR F. A taxonomy of attacks on federated learning[J]. IEEE Security & Privacy, 2020, 19(2): 20-28.
[19] NIU, C., ZhENG, Z., WU, F., TANG, S., & CHEN, G. (2020). Online pricing with reserve price constraint for personal data markets[J]. IEEE Transactions on Knowledge and Data Engineering.
[20] ZHENG, Z., SONG, L., & HAN, Z. (2017, May). Bridging the gap between big data and game theory: A general hierarchical pricing framework[C]//In 2017 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
[21] GENTRY C. Fully homomorphic encryption using ideal lattices[C]//Proceedings of the forty-first annual ACM symposium on Theory of computing. 2009: 169-178.

数据租赁——数据流通的新方式

Data tenancy: a new paradigm for data circulation

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

Metrics

推荐阅读 0