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The rise of deep learning technique has raised new privacy concerns about the training data and test data.
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2014
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M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in Computer Vision - {
2014
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M. Fredrikson, S. Jha, and T. Ristenpart, “Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures,” in Proceedings of the 22nd {
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A. Mahendran and A. Vedaldi, “Understanding deep image representations by inverting them,” in {
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R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership Inference Attacks Against Machine Learning Models,” in 2017 {
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Cited alongside, same era.
R. K. Srivastava, K. Greff, and J. Schmidhuber, “Training very deep networks,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 , ser. NIPS’15. Cambridge, MA, USA: MIT Press, 2015, pp. 2377–2385. [Online]. Available: http://dl.acm.org/citation.cfm?id=2969442.2969505
2015
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Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , 2015
2015
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J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” in Deep Learning Workshop, ICML , 2015
2015
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Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of International Conference on Computer Vision (ICCV) , 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in IEEE International Conference on Computer Vision (ICCV) , 2015
2015
Cited alongside, same era.
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici, “Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers,” International Journal of Security and Networks , vol. 10, no. 3, pp. 137–150, 2015
2015
Cited alongside, same era.
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici, “Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers,” International Journal of Security and Networks , vol. 10, no. 3, pp. 137–150, sep 2015. [Online]. Available: http://dx.doi.org/10.1504/IJSN.2015.071829
2015
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C. Song, T. Ristenpart, and V. Shmatikov, “Machine Learning Models that Remember Too Much,” in Proceedings of the 2017 {
2017
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H. Dang, Y. Huang, and E.-C. Chang, “Evading classifiers by morphing in the dark,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017
2017
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2017
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N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in ASIACCS , 2017
2017
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2017
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K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy preserving machine learning.” IACR Cryptology ePrint Archive , vol. 2017, p. 281, 2017
2017
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A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse, “The Reversible Residual Network : Backpropagation Without Storing Activations,” in Advances in Neural Information Processing Systems , vol. 1, no. Nips, 2017, pp. 1–11
2017
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2018
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J. Jia and N. Z. Gong, “AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning,” in 27th {
2018
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2018
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Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning attack on neural networks,” in 25nd Annual Network and Distributed System Security Symposium (NDSS) , 2018
2018
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C. Dwork and V. Feldman, “Privacy-preserving Prediction,” in Proceedings of the 31st Conference On Learning Theory , ser. Proceedings of Machine Learning Research, S. Bubeck, V. Perchet, and P. Rigollet, Eds., vol. 75. PMLR, 2018, pp. 1693–1702. [Online]. Available: http://proceedings.mlr.press/v75/dwork18a.html
2018
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2018
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L. T. Phong, Y. Aono, T. Hayashi, L. Wang, and S. Moriai, “Privacy-Preserving Deep Learning via Additively Homomorphic Encryption,” IEEE Transactions on Information Forensics and Security , vol. 13, no. 5, pp. 1333–1345, 2018
2018
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2018
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2018
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M. Du, N. Liu, Q. Song, and X. Hu, “Towards explanation of dnn-based prediction with guided feature inversion,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , ser. KDD ’18. New York, NY, USA: ACM, 2018, pp. 1358–1367. [Online]. Available: http://doi.acm.org/10.1145/3219819.3220099
2018
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J.-H. Jacobsen, A. W. Smeulders, and E. Oyallon, “i-revnet: Deep invertible networks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=HJsjkMb0Z
2018
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Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi, “Beyond Inferring Class Representatives : User-Level Privacy Leakage From Federated Learning,” in The 38th Annual IEEE International Conference on Computer Communications (INFOCOM 2019) , 2019
2019
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