A novel secure deep ensemble learning protocol based on Conjugacy search problem homomorphic encryption scheme
Tóm tắt
Từ khóa
#Học sâu #Học máy có đảm bảo tính riêng tư #Tính toán bảo mật nhiều thành viênTài liệu tham khảo
C. Aggarwal. Neural Networks and Deep Learning. Springer, Cham, 2018..
C. C. Aggarwal and P. S. Yu, editors. Privacy-Preserving Data Mining - Models and Algorithms, volume 34 of Advances in Database Systems. Springer, 2008
U. M. A¨ıvodji, S. Gambs, and A. Martin. Iotfla: A secured and privacy-preserving smart home architecture implementing federated learning. In 2019 IEEE Security and Privacy Workshops (SPW), pages 175–180. IEEE, 2019.
M. Al-Rubaie and J. M. Chang. Privacy-preserving machine learning: Threats and solutions. IEEE Security Privacy, 17(2):49–58, 2019.
Y. Bengio, I. Goodfellow, and A. Courville. Deep learning, volume 1. MIT press Massachusetts, USA:, 2017.
Boles and P. Rad. Voice biometrics: Deep learning-based voiceprint authentication system. In 2017 12th System of Systems Engineering Conference (SoSE), pages 1–6. IEEE, 2017.
Bu, Y. Ma, Z. Chen, and H. Xu. Privacy preserving backpropagation based on bgv on cloud. In 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pages 1791–1795, 2015.
J. Chen, X. Pan, R. Monga, S. Bengio, and R. Jozefowicz. Revisiting distributed synchronous sgd. arXiv preprint arXiv:1604.00981, 2016.
Guo and N. Zhang. A survey on deep learning based face recognition. Computer vision and image understanding, 189:102805, 2019.
Gupta and R. Raskar. Distributed learning of deep neural network over multiple agents. Journal of Network and Computer Applications, 116:1 – 8, 2018.
Hard, C. M. Kiddon, D. Ramage, F. Beaufays, H. Eichner, K. Rao, R. Mathews, and S. Augenstein. Federated learning for mobile keyboard prediction, 2018.
Hitaj, G. Ateniese, and F. Perez-Cruz. Deep models under the gan: Information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS ’17, page 603–618, New York, NY, USA, 2017. Association for Computing Machinery.
P. Li, J. Li, Z. Huang, T. Li, C.-Z. Gao, S.-M. Yiu, and K. Chen. Multi-key privacy-preserving deep learning in cloud computing. Future Generation Computer Systems, 74:76 – 85, 2017.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3):50–60, 2020.
L. Lyu, X. He, Y. W. Law, and M. Palaniswami. Privacypreserving collaborative deep learning with application to human activity recognition. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, page 1219–1228, New York, NY, USA, 2017. Association for Computing Machinery.
P. Mohassel and Y. Zhang. Secureml: A system for scalable privacy-preserving machine learning. In 2017 IEEE Symposium on Security and Privacy (SP), pages 19–38, 2017.
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar. Semi-supervised knowledge transfer for deep learning from private training data. arXiv preprint arXiv:1610.05755, 2016.
L. T. Phong, Y. Aono, T. Hayashi, L. Wang, and
S. Moriai. Privacy-preserving deep learning via additively homomorphic encryption. Trans. Info. For. Sec., 13(5):1333–1345, May 2018.
M. I. Razzak, S. Naz, and A. Zaib. Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps, pages 323–350, 2018.
L. Rokach. Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition). World Scientific Publishing Co Pte Ltd, Singapore, 2nd edition, 2019.
R. Shokri and V. Shmatikov. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference computer and communications security, pages 1310–1321, 2015.