1. M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, L. Zhang, Deep learning with differential privacy, in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, New York, NY (ACM, New York, 2016), pp. 308–318
2. Y. Aono, T. Hayashi, L. Trieu Phong, L. Wang, Scalable and secure logistic regression via homomorphic encryption, in Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, CODASPY ’16, New York, NY (ACM, New York, 2016), pp. 142–144
3. Y. Aono, T. Hayashi, L. Trieu Phong, L. Wang, Privacy-preserving logistic regression with distributed data sources via homomorphic encryption. IEICE Trans. Inf. Syst. 99(8), 2079–2089 (2016)
4. A. Ben-David, N. Nisan, B. Pinkas, Fairplaymp: a system for secure multi-party computation, in Proceedings of the 15th ACM Conference on Computer and Communications Security, CCS ’08, New York, NY (ACM, New York, 2008), pp. 257–266
5. A. Blum, C. Dwork, F. McSherry, K. Nissim, Practical privacy: The sulq framework, in Proceedings of the Twenty-fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS ’05, New York, NY (ACM, New York, 2005), pp. 128–138