Security and Privacy Issues of Federated Learning

B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” arXiv preprint arXiv:1206.6389 , 2012

Original

2012

Earlier work this paper cites.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014

2014

Earlier work this paper cites.

R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security , 2015, pp. 1310–1321

2015

Earlier work this paper cites.

B. 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 , 2017, pp. 603–618

2017

Earlier work this paper cites.

K. Liu, B. Dolan-Gavitt, and S. Garg, “Fine-pruning: Defending against backdooring attacks on deep neural networks,” in International symposium on research in attacks, intrusions, and defenses . Springer, 2018, pp. 273–294

2018

Earlier work this paper cites.

S. Truex, L. Liu, M. Emre Gursoy, L. Yu, and W. Wei, “Towards Demystifying Membership Inference Attacks,” arXiv e-prints , p. arXiv:1807.09173, June 2018

Original

2018

Earlier work this paper cites.

L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou, “Differentially private generative adversarial network,” arXiv preprint arXiv:1802.06739 , 2018

Original

2018

Earlier work this paper cites.

B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,” in 2019 IEEE Symposium on Security and Privacy (SP) , 2019, pp. 707–723

2019

Earlier work this paper cites.

J. Zhang, J. Chen, D. Wu, B. Chen, and S. Yu, “Poisoning attack in federated learning using generative adversarial nets,” in 2019 18th IEEE international conference on trust, security and privacy in computing and communications/13th IEEE international conference on big data science and engineering (TrustCom/BigDataSE) . IEEE, 2019, pp. 374–380

2019

Cited alongside, same era.

Z. Sun, P. Kairouz, A. T. Suresh, and H. B. McMahan, “Can you really backdoor federated learning?” arXiv preprint arXiv:1911.07963 , 2019

Original

2019

Cited alongside, same era.

L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning,” in 2019 IEEE Symposium on Security and Privacy (SP) , 2019, pp. 691–706

2019

Cited alongside, same era.

S. Augenstein, H. B. McMahan, D. Ramage, S. Ramaswamy, P. Kairouz, M. Chen, R. Mathews, et al. , “Generative models for effective ml on private, decentralized datasets,” arXiv preprint arXiv:1911.06679 , 2019

Original

M. Fang, X. Cao, J. Jia, and N. Gong, “Local model poisoning attacks to { \{ Byzantine-Robust } \} federated learning,” in 29th USENIX security symposium (USENIX Security 20) , 2020, pp. 1605–1622

2020

Later among the works it cites.

C. Xie, K. Huang, P.-Y. Chen, and B. Li, “Dba: Distributed backdoor attacks against federated learning,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=rkgyS0VFvr

2020

Later among the works it cites.

T. Zhu, D. Ye, W. Wang, W. Zhou, and S. Y. Philip, “More than privacy: Applying differential privacy in key areas of artificial intelligence,” IEEE Transactions on Knowledge and Data Engineering , vol. 34, no. 6, pp. 2824–2843, 2020

2020

Later among the works it cites.

V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Generation Computer Systems , vol. 115, pp. 619–640, 2021

2021