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In this work, we demonstrate universal multi-party poisoning attacks that adapt and apply to any multi-party learning process with arbitrary interaction pattern between the parties.
Learning disjunctions of conjunctions
Leslie G. Valiant · 1985
Earlier work this paper cites.
Collective coin flipping
M. Ben-Or and N. Linial · 1989
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Learning in the Presence of Malicious Errors
Michael J. Kearns and Ming Li · 1993
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Adaptively secure multi-party computation
Ran Canetti, Uri Feige, Oded Goldreich, and Moni Naor · 1996
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Nader H. Bshouty, Nadav Eiron, and Eyal Kushilevitz · 2002
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Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar · 2006
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Maria-Florina Balcan, Avrim Blum, and Santosh Vempala · 2008
Earlier work this paper cites.
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Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Coin flipping with constant bias implies one-way functions
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Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Agnostic estimation of mean and covariance
Kevin A Lai, Anup B Rao, and Santosh Vempala · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Earlier work this paper cites.
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Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
Earlier work this paper cites.
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Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
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Statistical query lower bounds for robust estimation of high-dimensional Gaussians and Gaussian mixtures
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Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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