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In this work, we propose a novel framework for privacy-preserving client-distributed machine learning.
D. R. Cox, “The regression analysis of binary sequences (with discussion),” J Roy Stat Soc B , vol. 20, pp. 215–242, 1958
1958
Earlier work this paper cites.
F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychological Review , pp. 65–386, 1958
1958
Earlier work this paper cites.
J. A. Nelder and R. W. M. Wedderburn, “Generalized linear models,” Journal of the Royal Statistical Society, Series A, General , vol. 135, pp. 370–384, 1972
1972
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.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of Cryptography Conference (TCC) , 2006, pp. 265–284
2006
Earlier work this paper cites.
K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,” in Advances in Neural Information Processing Systems , 2009, pp. 289–296
2009
Earlier work this paper cites.
A. Smith, “Differential privacy and the secrecy of the sample,” https://adamdsmith.wordpress.com/2009/09/02/sample-secrecy/ , 2009
2009
Earlier work this paper cites.
C. Dwork, “A firm foundation for private data analysis,” Communications of the ACM , vol. 54, no. 1, pp. 86–95, 2011
2011
Earlier work this paper cites.
N. Li, W. Qardaji, and D. Su, “On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy,” in Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security , 2012, pp. 32–33
2012
Earlier work this paper cites.
K. Kenthapadi, A. Korolova, I. Mironov, and N. Mishra, “Privacy via the Johnson-Lindenstrauss transform,” Journal of Privacy and Confidentiality , vol. 5, pp. 39–71, 2013
2013
Earlier work this paper cites.
S. Song, K. Chaudhuri, and A. D. Sarwate, “Stochastic gradient descent with differentially private updates,” in Global Conference on Signal and Information Processing (GlobalSIP) . IEEE, 2013, pp. 245–248
2013
Earlier work this paper cites.
C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Earlier work this paper cites.
Ú. Erlingsson, V. Pihur, and A. Korolova, “RAPPOR: Randomized aggregatable privacy-preserving ordinal response,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’14, 2014, pp. 1054–1067
2014
Earlier work this paper cites.
P. Kairouz, S. Oh, and P. Viswanath, “Extremal mechanisms for local differential privacy,” in Advances in Neural Information Processing Systems , 2014, pp. 2879–2887
2014
Earlier work this paper cites.
R. Bassily and A. Smith, “Local, private, efficient protocols for succinct histograms,” in Proceedings of the forty-seventh annual ACM Symposium on Theory of Computing . ACM, 2015, pp. 127–135
2015
Earlier work this paper cites.
M. Madden and L. Rainie, “Americans’ attitudes about privacy, security and surveillance,” Pew Research Center. http://www.pewinternet.org/2015/05/20/americans-attitudes-about-privacy-security-and-surveillance/ , 2015
2015
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 . ACM, 2015, pp. 1310–1321
2015
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’16, 2016, pp. 308–318. [Online]. Available: http://doi.acm.org/10.1145/2976749.2978318
2016
Cited alongside, same era.
M. Bun and T. Steinke, “Concentrated differential privacy: Simplifications, extensions, and lower bounds,” in Theory of Cryptography Conference . Springer, 2016, pp. 635–658
2016
Cited alongside, same era.
G. Fanti, V. Pihur, and Ú. Erlingsson, “Building a RAPPOR with the unknown: Privacy-preserving learning of associations and data dictionaries,” Proceedings on Privacy Enhancing Technologies , vol. 2016, no. 3, pp. 41–61, 2016
2016
Cited alongside, same era.
——, “Apple’s differential privacy is about collecting your data – but not your data,” in Wired , June 13, 2016
2016
Cited alongside, same era.
A. Greenberg, “How one of Apple’s key privacy safeguards falls short,” Wired , 2017. [Online]. Available: https://www.wired.com/story/apple-differential-privacy-shortcomings/
2017
Later among the works it cites.
——, “The composition theorem for differential privacy,” IEEE Transactions on Information Theory , vol. 63, no. 6, pp. 4037–4049, 2017
2017
Later among the works it cites.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in AISTATS , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
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——, “Privacy and information sharing,” Pew Research Center. http://www.pewinternet.org/2016/01/14/privacy-and-information-sharing/ , 2016
2016
Cited alongside, same era.
N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar, “Semi-supervised knowledge transfer for deep learning from private training data,” 5th International Conference on Learning Representations , 2016
2016
Cited alongside, same era.
E. Portnoy, G. Gebhart, and S. Grant, in EFF DeepLinks Blog , Sep 27, 2016, www.eff.org/deeplinks/2016/09/facial-recognition-differential-privacy-and-trade-offs-apples-latest-os-releases
2016
Cited alongside, same era.
WWDC 2016, WWDC 2016 Keynote , June, 2016, https://www.apple.com/apple-events/june-2016/
2016
Cited alongside, same era.
Apple Differential Privacy Team, “Learning with privacy at scale.” Apple Machine Learning Journal, 2017, vol. 1. [Online]. Available: https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html
2017
Cited alongside, same era.
B. Avent, A. Korolova, D. Zeber, T. Hovden, and B. Livshits, “BLENDER: Enabling local search with a hybrid differential privacy model,” in 26th USENIX Security Symposium (USENIX Security 17) , 2017, pp. 747–764. [Online]. Available: https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/avent
2017
Cited alongside, same era.
R. Bassily, K. Nissim, U. Stemmer, and A. G. Thakurta, “Practical locally private heavy hitters,” in Advances in Neural Information Processing Systems , 2017, pp. 2285–2293
2017
Cited alongside, same era.
A. Bittau, U. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnes, and B. Seefeld, “PROCHLO: Strong privacy for analytics in the crowd,” in Proceedings of the 26th Symposium on Operating Systems Principles . ACM, 2017, pp. 441–459
2017
Cited alongside, same era.
2017
Later among the works it cites.
I. Mironov, “Renyi differential privacy,” in 2017 IEEE 30th Computer Security Foundations Symposium (CSF) , 2017, pp. 263–275
2017
Later among the works it cites.
2017
Later among the works it cites.
X. Wu, F. Li, A. Kumar, K. Chaudhuri, S. Jha, and J. Naughton, “Bolt-on differential privacy for scalable stochastic gradient descent-based analytics,” in Proceedings of the ACM International Conference on Management of Data , 2017, pp. 1307–1322
2017
Later among the works it cites.
V. Feldman, I. Mironov, K. Talwar, and A. Thakurta, “Privacy amplification by iteration,” ArXiv e-prints 1808.06651 , Aug. 2018
2018
Closest in time.
Google, “Google Cloud best practices,” https://cloud.google.com/datastore/docs/best-practices , 2018
2018
Closest in time.
——, “Google safe browsing,” https://safebrowsing.google.com/ , 2018
2018
Closest in time.
——, “Memcache overview,” https://cloud.google.com/appengine/docs/standard/python/memcache/#operations_per_second_by_item_size , 2018
2018
Closest in time.
——, “Sharding counters,” https://cloud.google.com/appengine/articles/sharding_counters , 2018
2018
Closest in time.
Interworx, “Locating performance-degrading hot keys in memcached,” https://www.interworx.com/community/locating-performance-degrading-hot-keys-in-memcached/ , 2018
2018
Closest in time.
2018
Closest in time.
K. Nissim, T. Steinke, A. Wood, M. Altman, A. Bembenek, M. Bun, M. Gaboardi, D. O’Brien, and S. Vadhan, “Differential privacy: A primer for a non-technical audience (preliminary version),” Vanderbilt Journal of Entertainment and Technology Law , 2018 Forthcoming
2018
Closest in time.
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson, “Scalable private learning with pate,” International Conference on Learning Representations (ICLR) , 2018
2018
Closest in time.