Performing Co-Membership Attacks Against Deep Generative Models

C. E. Shannon, “Communication theory of secrecy systems,” Bell Labs Technical Journal , vol. 28, no. 4, pp. 656–715, 1949

1949

Earlier work this paper cites.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998

1998

Earlier work this paper cites.

L. v. d. Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res. , vol. 9, no. Nov, pp. 2579–2605, 2008

2008

Earlier work this paper cites.

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013

Original

2013

Earlier work this paper cites.

D. P. Kingma and M. Welling, “Auto-Encoding variational bayes,” ICLR 2014 , Dec. 2013

2013

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,” in NIPS , 2014, pp. 2672–2680

2014

Earlier work this paper cites.

M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart, “Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing.” in USENIX Security Symposium , 2014

2014

Earlier work this paper cites.

M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security , 2015

2015

Cited alongside, same era.

A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434 , 2015

Original

2015

Cited alongside, same era.

M. Abadi and D. G. Andersen, “Learning to protect communications with adversarial neural cryptography,” arXiv preprint arXiv:1610.06918 , 2016

Original

2016

Cited alongside, same era.

R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” Oct. 2016

2016

Cited alongside, same era.

B. McMahan and D. Ramage, “Federated learning: Collaborative machine learning without centralized training data,” Technical report, Google, Tech. Rep., 2017

2017

Later among the works it cites.

J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “Logan: Evaluating privacy leakage of generative models using generative adversarial networks,” arXiv preprint arXiv:1705.07663 , 2017

Original

2017

Later among the works it cites.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875 , 2017

Original

2017

Later among the works it cites.

P. Samangouei, M. Kabkab, and R. Chellappa, “Defense-GAN: protecting classifiers against adversarial attacks using generative models,” in Proceedings of the Sixth International Conference on Learning Representations (ICLR) , 2018

2018

Closest in time.