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Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models.
On data banks and privacy homomorphisms
Ronald L Rivest, Len Adleman, Michael L Dertouzos, et al · 1978
Earlier work this paper cites.
How to share a secret
Adi Shamir · 1979
Earlier work this paper cites.
Protocols for secure computations
Andrew C Yao · 1982
Earlier work this paper cites.
A public key cryptosystem and a signature scheme based on discrete logarithms
Taher ElGamal · 1985
Earlier work this paper cites.
A more efficient cryptographic matchmaking protocol for use in the absence of a continuously available third party
Catherine A. Meadows · 1986
Earlier work this paper cites.
Efficient multiparty protocols using circuit randomization
Donald Beaver · 1992
Earlier work this paper cites.
Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
Earlier work this paper cites.
Enhancing privacy and trust in electronic communities
Bernardo A Huberman, Matt Franklin, and Tad Hogg · 1999
Earlier work this paper cites.
Similarity estimation techniques from rounding algorithms
Moses S Charikar · 2002
Earlier work this paper cites.
Cryptography and cryptographic protocols
Oded Goldreich · 2003
Earlier work this paper cites.
Efficient private matching and set intersection
Michael J Freedman, Kobbi Nissim, and Benny Pinkas · 2004
Earlier work this paper cites.
How to exchange secrets with oblivious transfer
Michael O Rabin · 2005
Earlier work this paper cites.
Privacy-preserving set operations
Lea Kissner and Dawn Song · 2005
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Efficiency tradeoffs for malicious two-party computation
Payman Mohassel and Matthew Franklin · 2006
Earlier work this paper cites.
Functional encryption: Definitions and challenges
Dan Boneh, Amit Sahai, and Brent Waters · 2011
Earlier work this paper cites.
Private set intersection: Are garbled circuits better than custom protocols?
Yan Huang, David Evans, and Jonathan Katz · 2012
Earlier work this paper cites.
Multiparty computation from somewhat homomorphic encryption
Ivan Damgård, Valerio Pastro, Nigel Smart, and Sarah Zakarias · 2012
Earlier work this paper cites.
When private set intersection meets big data: an efficient and scalable protocol
Changyu Dong, Liqun Chen, and Zikai Wen · 2013
Earlier work this paper cites.
When private set intersection meets big data: an efficient and scalable protocol
Changyu Dong, Liqun Chen, and Zikai Wen · 2013
Earlier work this paper cites.
Gradient boosting machines, a tutorial
Alexey Natekin and Alois Knoll · 2013
Earlier work this paper cites.
Faster private set intersection based on OT extension
Benny Pinkas, Thomas Schneider, and Michael Zohner · 2014
Earlier work this paper cites.
On the computational efficiency of training neural networks
Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir · 2014
Earlier work this paper cites.
Mechanism design in large games: Incentives and privacy
Michael Kearns, Mallesh Pai, Aaron Roth, and Jonathan Ullman · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Aby-a framework for efficient mixed-protocol secure two-party computation
Daniel Demmler, Thomas Schneider, and Michael Zohner · 2015
Earlier work this paper cites.
Xgboost: extreme gradient boosting
Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, Rory Mitchell, Ignacio Cano, Tianyi Zhou, et al · 2015
Earlier work this paper cites.
Simple functional encryption schemes for inner products
Michel Abdalla, Florian Bourse, Angelo De Caro, and David Pointcheval · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Earlier work this paper cites.
Federated learning of deep networks using model averaging
H Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
Earlier work this paper cites.
Efficient batched oblivious PRF with applications to private set intersection
Vladimir Kolesnikov, Ranjit Kumaresan, Mike Rosulek, and Ni Trieu · 2016
Earlier work this paper cites.
Privacy-preserving distributed linear regression on high-dimensional data
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2016
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Using randomized response for differential privacy preserving data collection
Yue Wang, Xintao Wu, and Donghui Hu · 2016
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Improved private set intersection against malicious adversaries
Peter Rindal and Mike Rosulek · 2017
Earlier work this paper cites.
Malicious-secure private set intersection via dual execution
Peter Rindal and Mike Rosulek · 2017
Earlier work this paper cites.
Practical multi-party private set intersection from symmetric-key techniques
Vladimir Kolesnikov, Naor Matania, Benny Pinkas, Mike Rosulek, and Ni Trieu · 2017
Earlier work this paper cites.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
Earlier work this paper cites.
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Earlier work this paper cites.
Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
Earlier work this paper cites.
Comprehensive privacy analysis of deep learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
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Property inference attacks on fully connected neural networks using permutation invariant representations
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov · 2018
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Scalable private set intersection based on OT extension
Benny Pinkas, Thomas Schneider, and Michael Zohner · 2018
Earlier work this paper cites.
Efficient scalable multiparty private set-intersection via garbled bloom filters
Roi Inbar, Eran Omri, and Benny Pinkas · 2018
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Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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Privacy-preserving ridge regression with only linearly-homomorphic encryption
Irene Giacomelli, Somesh Jha, Marc Joye, C David Page, and Kyonghwan Yoon · 2018
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Multi-input functional encryption for inner products: Function-hiding realizations and constructions without pairings
Michel Abdalla, Dario Catalano, Dario Fiore, Romain Gay, and Bogdan Ursu · 2018
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Aby3: A mixed protocol framework for machine learning
Payman Mohassel and Peter Rindal · 2018
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Privacy preserving group nearest neighbor search
Yuncheng Wu, Ke Wang, Zhilin Zhang, Weipeng Lin, Hong Chen, and Cuiping Li · 2018
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Earlier work this paper cites.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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How to play any mental game, or a completeness theorem for protocols with honest majority
Oded Goldreich, Silvio Micali, and Avi Wigderson · 2019
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Efficient circuit-based PSI with linear communication
Benny Pinkas, Thomas Schneider, Oleksandr Tkachenko, and Avishay Yanai · 2019
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An algebraic approach to maliciously secure private set intersection
Satrajit Ghosh and Tobias Nilges · 2019
Earlier work this paper cites.
Efficient multi-party private set intersection against malicious adversaries
En Zhang, Feng-Hao Liu, Qiqi Lai, Ganggang Jin, and Yu Li · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Shengwen Yang, Bing Ren, Xuhui Zhou, and Liping Liu · 2019
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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
Earlier work this paper cites.
Securegbm: Secure multi-party gradient boosting
Zhi Feng, Haoyi Xiong, Chuanyuan Song, Sijia Yang, Baoxin Zhao, Licheng Wang, Zeyu Chen, Shengwen Yang, Liping Liu, and Jun Huan · 2019
Earlier work this paper cites.
Secure and efficient federated transfer learning
Shreya Sharma, Chaoping Xing, Yang Liu, and Yan Kang · 2019
Earlier work this paper cites.
Lagrange coded computing: Optimal design for resiliency, security, and privacy
Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, and Salman A Avestimehr · 2019
Earlier work this paper cites.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Federated learning for privacy-preserving ai
Yong Cheng, Yang Liu, Tianjian Chen, and Qiang Yang · 2020
Cited alongside, same era.
Threats to federated learning: A survey, 2020
Lingjuan Lyu, Han Yu, and Qiang Yang · 2020
Cited alongside, same era.
Local differential privacy and its applications: A comprehensive survey
Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, and Kwok-Yan Lam · 2020
Cited alongside, same era.
Multi-party private set intersection in vertical federated learning
Linpeng Lu and Ning Ding · 2020
Cited alongside, same era.
Federated learning for privacy-preserving ai
Yong Cheng, Yang Liu, Tianjian Chen, and Qiang Yang · 2020
Vertical federated learning: A structured literature review
Afsana Khan, Marijn ten Thij, and Anna Wilbik · 2022
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Vertical federated learning: Challenges, methodologies and experiments
Kang Wei, Jun Li, Chuan Ma, Ming Ding, Sha Wei, Fan Wu, Guihai Chen, and Thilina Ranbaduge · 2022
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Splitfed: When federated learning meets split learning
Chandra Thapa, Pathum Chamikara Mahawaga Arachchige, Seyit Camtepe, and Lichao Sun · 2022
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Defending batch-level label inference and replacement attacks in vertical federated learning
Tianyuan Zou, Yang Liu, Yan Kang, Wenhan Liu, Yuanqin He, Zhihao Yi, Qiang Yang, and Ya-Qin Zhang · 2022
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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 · 2022
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Cited alongside, same era.
Psi from paxos: fast, malicious private set intersection
Benny Pinkas, Mike Rosulek, Ni Trieu, and Avishay Yanai · 2020
Cited alongside, same era.
Asymmetrical vertical federated learning
Yang Liu, Xiong Zhang, and Libin Wang · 2020
Cited alongside, same era.
Inverting gradients-how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
Cited alongside, same era.
Privacy leakage of real-world vertical federated learning
Haiqin Weng, Juntao Zhang, Feng Xue, Tao Wei, Shouling Ji, and Zhiyuan Zong · 2020
Cited alongside, same era.
Mitigating leakage in federated learning with trusted hardware
Javad Ghareh Chamani and Dimitrios Papadopoulos · 2020
Cited alongside, same era.
Yifei Zhang and Hao Zhu · 2020
Cited alongside, same era.
Later among the works it cites.
Label leakage and protection from forward embedding in vertical federated learning
Jiankai Sun, Xin Yang, Yuanshun Yao, and Chong Wang · 2022
Later among the works it cites.
Psi from ring-ole
Wutichai Chongchitmate, Yuval Ishai, Steve Lu, and Rafail Ostrovsky · 2022
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DP-PSI: private and secure set intersection
Jian Du, Tianxi Ji, Jamie Cui, Lei Zhang, Yufei Lu, and Pu Duan · 2022
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Clustering label inference attack against practical split learning
Junlin Liu and Xinchen Lyu · 2022
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Unsplit: Data-oblivious model inversion, model stealing, and label inference attacks against split learning
Ege Erdoğan, Alptekin Küpçü, and A Ercüment Çiçek · 2022
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Residue-based label protection mechanisms in vertical logistic regression
Juntao Tan, Lan Zhang, Yang Liu, Anran Li, and Ye Wu · 2022
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Feature reconstruction attacks and countermeasures of dnn training in vertical federated learning
Peng Ye, Zhifeng Jiang, Wei Wang, Bo Li, and Baochun Li · 2022
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Privacy-preserving federated adversarial domain adaptation over feature groups for interpretability
Yan Kang, Yuanqin He, Jiahuan Luo, Tao Fan, Yang Liu, and Qiang Yang · 2022
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Multi-input quadratic functional encryption: Stronger security, broader functionality
Shweta Agrawal, Rishab Goyal, and Junichi Tomida · 2022
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An efficient learning framework for federated xgboost using secret sharing and distributed optimization
Lunchen Xie, Jiaqi Liu, Songtao Lu, Tsung-Hui Chang, and Qingjiang Shi · 2022
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Towards end-to-end secure and efficient federated learning for xgboost
Chao Jin, Jun Wang, Sin G Teo, Le Zhang, C Chan, Qibin Hou, and Khin Mi Mi Aung · 2022
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Blindfl: Vertical federated machine learning without peeking into your data
Fangcheng Fu, Huanran Xue, Yong Cheng, Yangyu Tao, and Bin Cui · 2022
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A hybrid secure two-party protocol for vertical federated learning
Wenti Yang, Zhaoyang He, Yalei Li, Haiyan Zhang, and Zhitao Guan · 2022
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Feverless: Fast and secure vertical federated learning based on xgboost for decentralized labels
Rui Wang, Oğuzhan Ersoy, Hangyu Zhu, Yaochu Jin, and Kaitai Liang · 2022
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Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen, Zhezhi He, Deliang Fan, and Chaitali Chakrabarti · 2022
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Differentially private vertical federated clustering
Zitao Li, Tianhao Wang, and Ninghui Li · 2022
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Google. differentially private k-means clustering (experimental). https://github. com/google/differential-privacy/tree/main/learning/clustering, 2022
2022
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Opboost: a vertical federated tree boosting framework based on order-preserving desensitization
Xiaochen Li, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, and Zhan Qin · 2022
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Differentially private label protection in split learning
Xin Yang, Jiankai Sun, Yuanshun Yao, Junyuan Xie, and Chong Wang · 2022
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Differentially private vertical federated learning
Thilina Ranbaduge and Ming Ding · 2022
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Differentially private cutmix for split learning with vision transformer
Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, and Seong-Lyun Kim · 2022
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Secure split learning against property inference and data reconstruction attacks
Yunlong Mao, Zexi Xin, Zhenyu Li, Jue Hong, Yang Qingyou, and Sheng Zhong · 2022
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The poisson binomial mechanism for unbiased federated learning with secure aggregation
Wei-Ning Chen, Ayfer Ozgur, and Peter Kairouz · 2022
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Making split learning resilient to label leakage by potential energy loss
Fei Zheng, Chaochao Chen, Binhui Yao, and Xiaolin Zheng · 2022
Later among the works it cites.
Comprehensive analysis of privacy leakage in vertical federated learning during prediction
Xue Jiang, Xuebing Zhou, and Jens Grossklags · 2022
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Feature space hijacking attacks against differentially private split learning
Grzegorz Gawron and Philip Stubbings · 2022
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Your labels are selling you out: Relation leaks in vertical federated learning
Pengyu Qiu, Xuhong Zhang, Shouling Ji, Tianyu Du, Yuwen Pu, Jun Zhou, and Ting Wang · 2022
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Cheetah: Lean and fast secure { \{ Two-Party } \} deep neural network inference
Zhicong Huang, Wen-jie Lu, Cheng Hong, and Jiansheng Ding · 2022
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Self-supervised vertical federated learning
Timothy Castiglia, Shiqiang Wang, and Stacy Patterson · 2022
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Split learning based on self-supervised learning
Shaojie Yang, Hao Chen, Jianping Huang, Yong Yan, Jiewei Chen, and Ao Xiong · 2022
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Auditing privacy defenses in federated learning via generative gradient leakage
Zhuohang Li, Jiaxin Zhang, Luyang Liu, and Jian Liu · 2022
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A survey on federated learning systems: Vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He · 2023
Later among the works it cites.
Vertical federated learning: Concepts, advances and challenges
Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang, and Qiang Yang · 2023
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A survey on vertical federated learning: From a layered perspective
Liu Yang, Di Chai, Junxue Zhang, Yilun Jin, Leye Wang, Hao Liu, Han Tian, Qian Xu, and Kai Chen · 2023
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Client-specific property inference against secure aggregation in federated learning
Raouf Kerkouche, Gergely Ács, and Mario Fritz · 2023
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PCAT: Functionality and data stealing from split learning by Pseudo-Client attack
Xinben Gao and Lan Zhang · 2023
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Exploit: Extracting private labels in split learning
Sanjay Kariyappa and Moinuddin K Qureshi · 2023
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Exact: Extensive attack for split learning
Xinchi Qiu, Ilias Leontiadis, Luca Melis, Alex Sablayrolles, and Pierre Stock · 2023
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Eliminating label leakage in tree-based vertical federated learning
Hideaki Takahashi, Jingjing Liu, and Yang Liu · 2023
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Model extraction attacks on split federated learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen, Li Yang, Zhezhi He, Deliang Fan, and Chaitali Chakrabarti · 2023
Later among the works it cites.
Vf-cart: A communication-efficient vertical federated framework for the cart algorithm
Yang Xu, Xuexian Hu, Jianghong Wei, Hongjian Yang, and Kejia Li · 2023
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An embedded vertical-federated feature selection algorithm based on particle swarm optimisation
Yong Zhang, Ying Hu, Xiaozhi Gao, Dunwei Gong, Yinan Guo, Kaizhou Gao, and Wanqiu Zhang · 2023
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Securesl: A privacy-preserving vertical cooperative learning scheme for web 3.0
Wenti Yang, Xiaodong Wang, Zhitao Guan, Longfei Wu, Xiaojiang Du, and Mohsen Guizani · 2023
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Split ways: Privacy-preserving training of encrypted data using split learning
Tanveer Khan, Khoa Nguyen, and Antonis Michalas · 2023
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Quadratic functional encryption for secure training in vertical federated learning
Shuangyi Chen, Anuja Modi, Shweta Agrawal, and Ashish Khisti · 2023
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Vertices: Efficient two-party vertical federated linear model with ttp-aided secret sharing
Mingxuan Fan, Yilun Jin, Liu Yang, Zhenghang Ren, and Kai Chen · 2023
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Squirrel: A scalable secure Two-Party computation framework for training gradient boosting decision tree
Wen jie Lu, Zhicong Huang, Qizhi Zhang, Yuchen Wang, and Cheng Hong · 2023
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Privet: A privacy-preserving vertical federated learning service for gradient boosted decision tables
Yifeng Zheng, Shuangqing Xu, Songlei Wang, Yansong Gao, and Zhongyun Hua · 2023
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Fedvs: Straggler-resilient and privacy-preserving vertical federated learning for split models
Songze Li, Duanyi Yao, and Jin Liu · 2023
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Fedpass: Privacy-preserving vertical federated deep learning with adaptive obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang, Lixin Fan, and Qiang Yang · 2023
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vfedsec: Efficient secure aggregation for vertical federated learning via secure layer
Xinchi Qiu, Heng Pan, Wanru Zhao, Chenyang Ma, Pedro PB Gusmao, and Nicholas D Lane · 2023
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Privacy and efficiency of communications in federated split learning
Zongshun Zhang, Andrea Pinto, Valeria Turina, Flavio Esposito, and Ibrahim Matta · 2023
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Gan-based federated learning for label protection in binary classification
Yujin Han and Leying Guan · 2023
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Privacy tradeoffs in vertical federated learning
Linh Tran, Timothy Castiglia, Stacy Patterson, and Ana Milanova · 2023
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Robust and ip-protecting vertical federated learning against unexpected quitting of parties
Jingwei Sun, Zhixu Du, Anna Dai, Saleh Baghersalimi, Alireza Amirshahi, David Atienza, and Yiran Chen · 2023
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Practical feature inference attack in vertical federated learning during prediction in artificial internet of things
Ruikang Yang, Jianfeng Ma, Junying Zhang, Saru Kumari, Sachin Kumar, and Joel JPC Rodrigues · 2023
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One-shot empirical privacy estimation for federated learning
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H Brendan McMahan, and Vinith Suriyakumar · 2023
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