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Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to the serious concerns over user privacy and data security.
On data banks and privacy homomorphisms
R L Rivest, L Adleman, and M L Dertouzos · 1978
Earlier work this paper cites.
How to play any mental game
O. Goldreich, S. Micali, and A. Wigderson · 1987
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl · 2001
Earlier work this paper cites.
Methods and metrics for cold-start recommendations
Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock · 2002
Earlier work this paper cites.
Amazon.com recommendations: item-to-item collaborative filtering
G. Linden, B. Smith, and J. York · 2003
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Improved neighborhood-based collaborative filtering
Robert M. Bell and Yehuda Koren · 2007
Earlier work this paper cites.
Personalized news recommendation based on click behavior
Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen · 2010
Earlier work this paper cites.
Learning deep structured semantic models for web search using clickthrough data
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
A multi-view deep learning approach for cross domain user modeling in recommendation systems
Ali Mamdouh Elkahky, Yang Song, and Xiaodong He · 2015
Cited alongside, same era.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Cited alongside, same era.
Federated collaborative filtering for privacy-preserving personalized recommendation system
Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan · 2019
Later among the works it cites.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe M Kiddon, Jakub Konecny, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
Later among the works it cites.
Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang · 2019
Later among the works it cites.
TensorFlow Federated Framework
Google Inc · 2019
Later among the works it cites.
Demystifying arm trustzone: A comprehensive survey
Sandro Pinto and Nuno Santos · 2019
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Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
Cited alongside, same era.
The EU General Data Protection Regulation (GDPR): A Practical Guide
Paul Voigt and Axel von dem Bussche · 2017
Cited alongside, same era.
LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Later among the works it cites.
Federated multi-view matrix factorization for personalized recommendations
Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A Khan, and Muhammad Ammad-Ud-Din · 2020
Closest in time.
Fedrec: Privacy-preserving news recommendation with federated learning
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, and Xing Xie · 2020
Closest in time.