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Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud.
R. Agrawal and R. Srikant, “Privacy-preserving data mining,” in ACM Sigmod Record , vol. 29, no. 2. ACM, 2000, pp. 439–450
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R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’15. New York, NY, USA: ACM, 2015, pp. 1310–1321. [Online]. Available: http://doi.acm.org/10.1145/2810103.2813687
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2014
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J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li, “Deep learning for content-based image retrieval: A comprehensive study,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 157–166
2014
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J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems , 2014, pp. 3320–3328
2014
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2014
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K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the devil in the details: Delving deep into convolutional nets,” in British Machine Vision Conference , 2014
2014
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2014
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N. D. Lane and P. Georgiev, “Can deep learning revolutionize mobile sensing?” in Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications . ACM, 2015, pp. 117–122
2015
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N. D. Lane, P. Georgiev, C. Mascolo, and Y. Gao, “Zoe: A cloud-less dialog-enabled continuous sensing wearable exploiting heterogeneous computation,” in Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services . ACM, 2015, pp. 273–286
2015
Cited alongside, same era.
2015
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J. R. Padilla-López, A. A. Chaaraoui, and F. Flórez-Revuelta, “Visual privacy protection methods: A survey,” Expert Systems with Applications , vol. 42, no. 9, pp. 4177–4195, 2015
2015
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L. Pournajaf, D. A. Garcia-Ulloa, L. Xiong, and V. Sunderam, “Participant privacy in mobile crowd sensing task management: A survey of methods and challenges,” ACM SIGMOD Record , vol. 44, no. 4, pp. 23–34, 2016
2016
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J. Rich, H. Haddadi, and T. M. Hospedales, “Towards bottom-up analysis of social food,” in Proceedings of the 6th International Conference on Digital Health Conference , ser. DH ’16. New York, NY, USA: ACM, 2016, pp. 111–120. [Online]. Available: http://doi.acm.org/10.1145/2896338.2897734
2016
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P. N. Druzhkov and V. D. Kustikova, “A survey of deep learning methods and software tools for image classification and object detection,” Pattern Recognition and Image Analysis , vol. 26, no. 1, pp. 9–15, 2016. [Online]. Available: http://dx.doi.org/10.1134/S1054661816010065
2016
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A. Dosovitskiy and T. Brox, “Inverting visual representations with convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4829–4837
2016
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A. Mollahosseini, D. Chan, and M. H. Mahoor, “Going deeper in facial expression recognition using deep neural networks,” in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 2016, pp. 1–10
2016
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M. Abadi, A. Chu, I. Goodfellow, H. Brendan McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep Learning with Differential Privacy,” ArXiv e-prints , Jul. 2016
2016
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R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing, “Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy,” in Proceedings of The 33rd International Conference on Machine Learning , 2016, pp. 201–210
2016
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S. Bhattacharya and N. D. Lane, “Sparsification and separation of deep learning layers for constrained resource inference on wearables,” in Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM . ACM, 2016, pp. 176–189
2016
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2016
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2017
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2017
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P. Mohassel and Y. Zhang, “Secureml: A system for scalable privacy-preserving machine learning.” IACR Cryptology ePrint Archive , vol. 2017, p. 396, 2017
2017
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