Fetching the paper…
Reading the bibliography…
Today's AI still faces two major challenges.
On Data Banks and Privacy Homomorphisms
R L Rivest, L Adleman, and M L Dertouzos. 1978 · 1978
Earlier work this paper cites.
Protocols for Secure Computations. In
Andrew C. Yao. 1982 · 1982
Earlier work this paper cites.
How to Play ANY Mental Game. In
O. Goldreich, S. Micali, and A. Wigderson. 1987 · 1987
Earlier work this paper cites.
Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases
Amit P. Sheth and James A. Larson. 1990 · 1990
Earlier work this paper cites.
Privacy-preserving Data Mining. In
Rakesh Agrawal and Ramakrishnan Srikant. 2000 · 2000
Earlier work this paper cites.
Privacy-Preserving Cooperative Statistical Analysis. In
W. Du and M. Atallah. 2001 · 2001
Earlier work this paper cites.
Building Decision Tree Classifier on Private Data. In
Wenliang Du and Zhijun Zhan. 2002 · 2002
Earlier work this paper cites.
K-anonymity: A Model for Protecting Privacy
Latanya Sweeney. 2002 · 2002
Earlier work this paper cites.
Privacy Preserving Association Rule Mining in Vertically Partitioned Data. In
Jaideep Vaidya and Chris Clifton. 2002 · 2002
Earlier work this paper cites.
Privacy-preserving K-means Clustering over Vertically Partitioned Data. In
Jaideep Vaidya and Chris Clifton. 2003 · 2003
Earlier work this paper cites.
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification. In
Wenliang Du, Yunghsiang Sam Han, and Shigang Chen. 2004 · 2004
Earlier work this paper cites.
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
Murat Kantarcioglu and Chris Clifton. 2004 · 2004
Earlier work this paper cites.
Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products
Alan F. Karr, X. Sheldon Lin, Ashish P. Sanil, and Jerome P. Reiter. 2004 · 2004
Earlier work this paper cites.
Privacy-preserving inter-database operations. In
Gang Liang and Sudarshan S Chawathe. 2004 · 2004
Earlier work this paper cites.
Privacy Preserving Regression Modelling via Distributed Computation. In
Ashish P. Sanil, Alan F. Karr, Xiaodong Lin, and Jerome P. Reiter. 2004 · 2004
Earlier work this paper cites.
Privacy Preserving Naive Bayes Classifier for Vertically Partitioned Data. In
Jaideep Vaidya and Chris Clifton. [n. d.] · 2004
Earlier work this paper cites.
Privacy-Preserving Decision Trees over Vertically Partitioned Data. In
Jaideep Vaidya and Chris Clifton. 2005 · 2005
Earlier work this paper cites.
Privacy-preserving SVM Using Nonlinear Kernels on Horizontally Partitioned Data. In
Hwanjo Yu, Xiaoqian Jiang, and Jaideep Vaidya. 2006a · 2006
Earlier work this paper cites.
Privacy Preserving Schema and Data Matching. In
Monica Scannapieco, Ilya Figotin, Elisa Bertino, and Ahmed K. Elmagarmid. 2007 · 2007
Earlier work this paper cites.
Privacy-preservation for Gradient Descent Methods. In
Li Wan, Wee Keong Ng, Shuguo Han, and Vincent C. S. Lee. 2007 · 2007
Earlier work this paper cites.
Sharemind: A Framework for Fast Privacy-Preserving Computations. In
Dan Bogdanov, Sven Laur, and Jan Willemson. 2008 · 2008
Earlier work this paper cites.
Differential Privacy: A Survey of Results. In
Cynthia Dwork. 2008 · 2008
Earlier work this paper cites.
Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni. 2009 · 2009
Earlier work this paper cites.
A Survey on Transfer Learning
Sinno Jialin Pan and Qiang Yang. 2010 · 2009
Earlier work this paper cites.
Secure multiple linear regression based on homomorphic encryption
Rob Hall, Stephen E. Fienberg, and Yuval Nardi. 2011 · 2011
Earlier work this paper cites.
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server. In
Qirong Ho, James Cipar, Henggang Cui, Jin Kyu Kim, Seunghak Lee, Phillip B. Gibbons, Garth A. Gibson, Gregory R. Ganger, and Eric P. Xing. 2013 · 2013
Cited alongside, same era.
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records. In
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis, Marc Joye, Dan Boneh, and Nina Taft. 2013 · 2013
Cited alongside, same era.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate. 2013 · 2013
Cited alongside, same era.
Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing
Jiawei Yuan and Shucheng Yu. 2014 · 2013
Cited alongside, same era.
Fast and Secure Three-party Computation: The Garbled Circuit Approach. In
Payman Mohassel, Mike Rosulek, and Ye Zhang. 2015 · 2015
Cited alongside, same era.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017 · 2017
Later among the works it cites.
CryptoDL: Deep Neural Networks over Encrypted Data
Ehsan Hesamifard, Hassan Takabi, and Mehdi Ghasemi. 2017 · 2017
Later among the works it cites.
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz. 2017 · 2017
Later among the works it cites.
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
Cited alongside, same era.
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning
Qingchen Zhang, Laurence T. Yang, and Zhikui Chen. 2016 · 2015
Cited alongside, same era.
Deep Learning with Differential Privacy. In
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Scalable and Secure Logistic Regression via Homomorphic Encryption. In
Yoshinori Aono, Takuya Hayashi, Le Trieu Phong, and Lihua Wang. 2016 · 2016
Cited alongside, same era.
High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority. In
Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara. 2016 · 2016
Cited alongside, same era.
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016 · 2016
Cited alongside, same era.
REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
EU. 2016 · 2016
Cited alongside, same era.
Oblivious Neural Network Predictions via MiniONN Transformations. In
Jian Liu, Mika Juuti, Yao Lu, and N. Asokan. 2017 · 2017
Later among the works it cites.
Learning Differentially Private Language Models Without Losing Accuracy
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017 · 2017
Later among the works it cites.
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Payman Mohassel and Yupeng Zhang. 2017b · 2017
Later among the works it cites.
DeepSecure: Scalable Provably-Secure Deep Learning
Bita Darvish Rouhani, M. Sadegh Riazi, and Farinaz Koushanfar. 2017 · 2017
Later among the works it cites.
Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar. 2017 · 2017
Later among the works it cites.
A Survey on Homomorphic Encryption Schemes: Theory and Implementation
Abbas Acar, Hidayet Aksu, A. Selcuk Uluagac, and Mauro Conti. 2018 · 2018
Later among the works it cites.
How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2018 · 2018
Later among the works it cites.
Federated Meta-Learning for Recommendation
Fei Chen, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018 · 2018
Later among the works it cites.
Blind Justice: Fairness with Encrypted Sensitive Attributes. In
Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, and Adrian Weller. 2018 · 2018
Later among the works it cites.
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation
Miran Kim, Yongsoo Song, Shuang Wang, Yuhou Xia, and Xiaoqian Jiang. 2018b · 2018
Later among the works it cites.
Inference Attacks Against Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2018 · 2018
Later among the works it cites.
ABY3: A Mixed Protocol Framework for Machine Learning. In
Payman Mohassel and Peter Rindal. 2018 · 2018
Later among the works it cites.
Entity Resolution and Federated Learning get a Federated Resolution
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2018 · 2018
Later among the works it cites.
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai. 2018 · 2018
Later among the works it cites.
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim M. Songhori, Thomas Schneider, and Farinaz Koushanfar. 2018 · 2018
Later among the works it cites.
Securing Distributed Machine Learning in High Dimensions
Lili Su and Jiaming Xu. 2018 · 2018
Later among the works it cites.
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan. 2018 · 2018
Later among the works it cites.
https://en.wikipedia.org/wiki/Facebook-Cambridge_Analytica_data_scandal
Wikipedia. 2018 · 2018
Later among the works it cites.
Federated Learning
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2018 · 2018
Later among the works it cites.
Federated Learning with Non-IID Data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018 · 2018
Later among the works it cites.