共 66 条
- [1] DOWLIN N, GILAD-BACHRACH R, LAINE K, Et al., Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy, Proceedings of the 33rd International Conference on International Conference on Machine Learning, pp. 19-24, (2016)
- [2] WU Y, CAI S, XIAO X, Et al., Privacy preserving vertical federated learning for tree-based models, VLDB Endow⁃ ment, 13, 11, pp. 2090-2103, (2020)
- [3] YAO A C., Protocols for secure computations, Proceed⁃ ings of the 23rd Annual Symposium on Foundations of Computer Science, pp. 160-164, (1982)
- [4] GOLDREICH O, MICALI S, WIGDERSON A., How to play any mental game, Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing, pp. 218-229, (1987)
- [5] DAVID E, VLADIMIR K, MIKE R., A pragmatic introduc⁃ tion to secure multi-party computation, Foundations and Trends in Privacy and Security, 2, 2-3, pp. 70-246, (2018)
- [6] JI S L, DU T Y, LI J F, Et al., Security and pdvacy of ma⁃ chine learning models: A survey, Journal of Software, 32, 1, pp. 41-67, (2021)
- [7] TAN Z W, ZHANG L F., Survey on privacy preserving techniques for machine learning, Journal of Software, 31, 7, pp. 2127-2156, (2020)
- [8] LIU R X, CHEN H, GUO R Y, Et al., Survey on privacy at⁃ tacks and defenses in machine learning, Joumal of Soft⁃ ware, 31, 3, pp. 866-892, (2020)
- [9] AL-RUBAIE M, CHANG J M., Privacy-preserving ma⁃ chine learning: Threats and solutions, IEEE Security Pri⁃ vacy, 17, 2, pp. 49-58, (2019)
- [10] TANUWIDJAJA H C, CHOI R, KIM K., A survey on deep learning techniques for privacy-preserving, Pro⁃ ceedings of the Second International Conference on Ma⁃ chine Learning for Cyber Security, pp. 29-46, (2019)