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Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy.
”privacy-preserving inter-database operations”
G. Liang and S. S.Chawathe · 2004
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How to break anonymity of the netflix prize dataset
A. Narayanan and Vitaly Shmatikov · 2006
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Privacy-preserving analysis of vertically partitioned data using secure matrix products
Alan F. Karr, Xiaodong Lin, Jerome P. Reiter, and Ashish P. Sanil · 2007
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Mcrank: Learning to rank using multiple classification and gradient boosting
Ping Li, Qiang Wu, and Christopher Burges · 2008
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Parallel boosted regression trees for web search ranking
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, and Jennifer Paykin · 2011
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Local privacy and minimax bounds: Sharp rates for probability estimation
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response, 2014
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Secure multiparty computation and secret sharing an information theoretic approach for the internet of things, 2015
Ronald Cramer, Ivan Damgard, and Jesper Buus Nielsen · 2015
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Scalable and secure logistic regression via homomorphic encryption
Yoshinori Aono, Takuya Hayashi, Trieu Phong Le, and Lihua Wang · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Optimal quantile approximation in streams
Zohar Karnin, Kevin Lang, and Edo Liberty · 2016
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Secureboost: A lossless federated learning framework
K. Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang · 2019
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Practical federated gradient boosting decision trees, 2019
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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FFD: A Federated Learning Based Method for Credit Card Fraud Detection, pages 18–32
Wensi Yang, Yuhang Zhang, Kejiang Ye, Li Li, and Cheng-Zhong Xu · 2019
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A hybrid-domain framework for secure gradient tree boosting, 05 2020
Wenjing Fang, Chaochao Chen, Jin Tan, Chaofan Yu, Yufei Lu, Li Wang, Lei Wang, Jun Zhou, and Alex X · 2020
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A hybrid-domain framework for secure gradient tree boosting, 2020
Wenjing Fang, Chaochao Chen, Jin Tan, Chaofan Yu, Yufei Lu, Li Wang, Lei Wang, Jun Zhou, and Alex X · 2020
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Boosting privately: Federated extreme gradient boosting for mobile crowdsensing
Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert. H Deng, and Kui Ren · 2020
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Qinbin Li, Zeyi Wen, and Bingsheng He · 2019
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Collecting and analyzing multidimensional data with local differential privacy, 2019
Ning Wang, Xiaokui Xiao, Yin Yang, Jun Zhao, Siu Cheung Hui, Hyejin Shin, Junbum Shin, and Ge Yu · 2019
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Federated learning for healthcare informatics
Jie Xu and Fei Wang · 2019
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Geet Shingi · 2020
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Privacy-preserving gradient boosting decision trees, 2021
Qinbin Li, Zhaomin Wu, Zeyi Wen, and Bingsheng He · 2021
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Xgboost algorithm under differential privacy protection
Yuanmin Shi, Siran Yin, Ze Chen, and Leiming Yan · 2021
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