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The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy.
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2012
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2013
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2014
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2014
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2015
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2015
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2015
Cited alongside, same era.
2016
Later among the works it cites.
J. Hamm, Y. Cao, and M. Belkin, “Learning privately from multiparty data,” in Proceedings of the 33nd International Conference on Machine Learning, ICML 2016 , 2016, pp. 555–563. [Online]. Available: http://jmlr.org/proceedings/papers/v48/hamm16.html
2016
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2016
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2016
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R. Gilad-Bachrach, N. Dowlin, K. Laine, K. E. Lauter, M. Naehrig, and J. Wernsing, “CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy,” in Proceedings of the 33nd International Conference on Machine Learning, ICML 2016 , 2016, pp. 201–210. [Online]. Available: http://jmlr.org/proceedings/papers/v48/gilad-bachrach16.html
2016
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2016
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2017
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2017
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