Funder
Innovation Capability Support Program of Shaanxi
the Shaanxi Special Support Program Youth Top-notch Talent Program
the Natural Science Basic Research Plan in Shaanxi Province of China
Publisher
Springer Science and Business Media LLC
Reference41 articles.
1. Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS’17, pp 1175–1191, New York, NY, USA. Association for Computing Machinery
2. Chen Y, Luo F, Li T, Xiang T, Liu Z, Li J (2020) A training-integrity privacy-preserving federated learning scheme with trusted execution environment. Inf Sci 522:69–79
3. Cong W, Chow Sherman SM, Qian W, Kui R, Wenjing L (2011) Privacy-preserving public auditing for secure cloud storage. IEEE Trans Comput 62(2):362–375
4. Diffie W, Hellman Martin E (1976) New directions in cryptography. IEEE Trans Inf Theory 22(6):644–654
5. Dong Z (2019) Federal learning: the second goal of ai security after deep learning.https://www.leiphone.com/news/201911/ziMkFyZXf1ERiniG.html
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献