Conducted a mini survey on design of federated learning systems and how people solve privacy issues.
Rank 6 / 132
Yang, Qiang, et al. “Federated machine learning: Concept and applications.” ACM Transactions on Intelligent Systems and Technology (TIST) 10.2 (2019): 1-19.
Domingo-Ferrer, Josep, et al. “Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges.” Computer Communications 140 (2019): 38-60.
Li, Qinbin, Zeyi Wen, and Bingsheng He. “Federated learning systems: Vision, hype and reality for data privacy and protection.” arXiv preprint arXiv:1907.09693 (2019).
Fontaine, Caroline, and Fabien Galand. “A survey of homomorphic encryption for nonspecialists.” EURASIP Journal on Information Security 2007 (2007): 1-10.
Shokri, Reza, and Vitaly Shmatikov. “Privacy-preserving deep learning.” Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 2015.
Aono, Yoshinori, et al. “Privacy-preserving deep learning via additively homomorphic encryption.” IEEE Transactions on Information Forensics and Security 13.5 (2017): 1333-1345.
Bonawitz, Keith, et al. “Practical secure aggregation for privacy-preserving machine learning.” Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017.