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Split learning is a popular technique used for vertical federated learning (VFL), where the goal is to jointly train a model on the private input and label data held by two parties.
Protocols for secure computations
Andrew C Yao · 1982
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Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
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Yuval Ishai, Joe Kilian, Kobbi Nissim, and Erez Petrank · 2003
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A framework for efficient and composable oblivious transfer
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Image clustering using local discriminant models and global integration
Yi Yang, Dong Xu, Feiping Nie, Shuicheng Yan, and Yueting Zhuang · 2010
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More efficient oblivious transfer and extensions for faster secure computation
Gilad Asharov, Yehuda Lindell, Thomas Schneider, and Michael Zohner · 2013
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Innovative instructions and software model for isolated execution
Frank McKeen, Ilya Alexandrovich, Alex Berenzon, Carlos V Rozas, Hisham Shafi, Vedvyas Shanbhogue, and Uday R Savagaonkar · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Practical lessons from predicting clicks on ads at facebook
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, et al · 2014
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Aby-a framework for efficient mixed-protocol secure two-party computation
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Fast private set intersection from homomorphic encryption
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Meni Orenbach, Pavel Lifshits, Marina Minkin, and Mark Silberstein · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Can we use split learning on 1d cnn models for privacy preserving training?
Sharif Abuadbba, Kyuyeon Kim, Minki Kim, Chandra Thapa, Seyit A Camtepe, Yansong Gao, Hyoungshick Kim, and Surya Nepal · 2020
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Ginn: Fast gpu-tee based integrity for neural network training
Aref Asvadishirehjini, Murat Kantarcioglu, and Bradley A Malin · 2020
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A homomorphic-encryption-based vertical federated learning scheme for rick management
Wei Ou, Jianhuan Zeng, Zijun Guo, Wanqin Yan, Dingwan Liu, and Stelios Fuentes · 2020
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Conversion rate benchmarks: Find out how your conversion rate compares
Conor Bond · 2021
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
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Marcelo Tallis and Pranjul Yadav · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
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A quasi-newton method based vertical federated learning framework for logistic regression
Kai Yang, Tao Fan, Tianjian Chen, Yuanming Shi, and Qiang Yang · 2019
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Label inference attacks against vertical federated learning
Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X Liu, and Ting Wang
Cited in the paper.
Label leakage and protection in two-party split learning
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang
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Ege Erdogan, Alptekin Kupcu, and A Ercument Cicek · 2021
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On deep learning with label differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang · 2021
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Label leakage and protection in two-party split learning
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang · 2021
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Practical defences against model inversion attacks for split neural networks
Tom Titcombe, Adam J Hall, Pavlos Papadopoulos, and Daniele Romanini · 2021
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