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We introduce PyVertical, a framework supporting vertical federated learning using split neural networks.
Privacy-preserving cooperative statistical analysis
Wenliang Du and Mikhail J Atallah · 2001
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Privacy preserving association rule mining in vertically partitioned data
Jaideep Vaidya and Chris Clifton · 2002
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Privacy-preserving multivariate statistical analysis: Linear regression and classification
Wenliang Du, Yunghsiang S Han, and Shigang Chen · 2004
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Efficient private matching and set intersection
Michael J Freedman, Kobbi Nissim, and Benny Pinkas · 2004
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Privacy-preserving distributed mining of association rules on horizontally partitioned data
Murat Kantarcioglu and Chris Clifton · 2004
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Building predictors from vertically distributed data
Sabine McConnell and David B Skillicorn · 2004
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Privacy preserving regression modelling via distributed computation
Ashish P Sanil, Alan F Karr, Xiaodong Lin, and Jerome P Reiter · 2004
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Privacy-preservation for gradient descent methods
Li Wan, Wee Keong Ng, Shuguo Han, and Vincent CS Lee · 2007
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Efficient robust private set intersection
Dana Dachman-Soled, Tal Malkin, Mariana Raykova, and Moti Yung · 2009
Earlier work this paper cites.
Privacy-preserving analysis of vertically partitioned data using secure matrix products
Alan F Karr, Xiaodong Lin, Ashish P Sanil, and Jerome P Reiter · 2009
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Practical private set intersection protocols with linear complexity
Emiliano De Cristofaro and Gene Tsudik · 2010
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Private set intersection: Are garbled circuits better than custom protocols?
Yan Huang, David Evans, and Jonathan Katz · 2012
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Cited alongside, same era.
Privacy-preserving distributed linear regression on high-dimensional data
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2017
Cited alongside, same era.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
Cited alongside, same era.
Scalable multi-party private set-intersection
A distributed trust framework for privacy-preserving machine learning
Will Abramson, Adam James Hall, Pavlos Papadopoulos, Nikolaos Pitropakis, and William J. Buchanan · 2020
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PSI Source Code, 2020
Nick Angelou, Ayoud Benaissa, Bogdan Cebere, Will Clark, Phillipp Schoppmann, Rutuja Surve, Daniel Liu, and Ben Szymbow · 2020
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Nick Angelou, Ayoub Benaissa, Bogdan Cebere, William Clark, Adam James Hall, Michael A Hoeh, Daniel Liu, Pavlos Papadopoulos, Robin Roehm, Robert Sandmann, Phillipp Schoppmann, and Tom Titcombe · 2020
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Private matching for compute
Prasad Buddhavarapu, Andrew Knox, Payman Mohassel, Shubho Sengupta, Erik Taubeneck, and Vlad Vlaskin · 2020
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Splitnn-driven vertical partitioning, 2020
Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, and Ramesh Raskar · 2020
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Carmit Hazay and Muthuramakrishnan Venkitasubramaniam · 2017
Cited alongside, same era.
Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage · 2017
Cited alongside, same era.
Pir-psi: Scaling private contact discovery
Daniel Demmler, Peter Rindal, Mike Rosulek, and Ni Trieu · 2018
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Scalable private set intersection based on ot extension
Benny Pinkas, Thomas Schneider, and Michael Zohner · 2018
Cited alongside, same era.
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
Cited alongside, same era.
Private set intersection in the internet setting from lightweight oblivious prf
Melissa Chase and Peihan Miao · 2020
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Multi-participant multi-class vertical federated learning
Siwei Feng and Han Yu · 2020
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Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, et al · 2020
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On deploying secure computing: Private intersection-sum-with-cardinality
Mihaela Ion, Ben Kreuter, Ahmet Erhan Nergiz, Sarvar Patel, Shobhit Saxena, Karn Seth, Mariana Raykova, David Shanahan, and Moti Yung · 2020
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Asymmetrically vertical federated learning
Yang Liu, Xiong Zhang, and Libin Wang · 2020
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Splitfed: When federated learning meets split learning, 2020
Chandra Thapa, M. A. P. Chamikara, and Seyit Camtepe · 2020
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Privacy and trust redefined in federated machine learning
Pavlos Papadopoulos, Will Abramson, Adam J. Hall, Nikolaos Pitropakis, and William J. Buchanan · 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
Closest in time.