Contamination Attacks and Mitigation in Multi-Party Machine Learning

8 January 2019

Papers citing "Contamination Attacks and Mitigation in Multi-Party Machine Learning"

25 / 25 papers shown

Title
Support Vector Machines under Adversarial Label Contamination Huang Xiao Battista Biggio B. Nelson Han Xiao Claudia Eckert Fabio Roli AAML 36 231 0 01 Jun 2022
Machine Learning with Membership Privacy using Adversarial Regularization Milad Nasr Reza Shokri Amir Houmansadr FedML MIACV 35 468 0 16 Jul 2018
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors Joshua Allen Bolin Ding Janardhan Kulkarni Harsha Nori O. Ohrimenko Sergey Yekhanin SyDa FedML 89 32 0 02 Jul 2018
Is feature selection secure against training data poisoning? Huang Xiao Battista Biggio Gavin Brown Giorgio Fumera Claudia Eckert Fabio Roli AAML SILM 41 423 0 21 Apr 2018
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning Matthew Jagielski Alina Oprea Battista Biggio Chang-rui Liu Cristina Nita-Rotaru Yue Liu AAML 71 757 0 01 Apr 2018
Understanding Membership Inferences on Well-Generalized Learning Models Yunhui Long Vincent Bindschaedler Lei Wang Diyue Bu Xiaofeng Wang Haixu Tang Carl A. Gunter Kai Chen MIALM MIACV 29 224 0 13 Feb 2018
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning Xinyun Chen Chang-rui Liu Yue Liu Kimberly Lu D. Song AAML SILM 80 1,822 0 15 Dec 2017
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 106 279 0 02 Oct 2017
Machine Learning Models that Remember Too Much Congzheng Song Thomas Ristenpart Vitaly Shmatikov VLM 52 511 0 22 Sep 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 72 1,758 0 22 Aug 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 136 2,854 0 14 Mar 2017
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning Briland Hitaj G. Ateniese Fernando Perez-Cruz FedML 107 1,385 0 24 Feb 2017
Learning to Pivot with Adversarial Networks Gilles Louppe Michael Kagan Kyle Cranmer 46 227 0 03 Nov 2016
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 203 4,075 0 18 Oct 2016
Minimax Filter: Learning to Preserve Privacy from Inference Attacks Jihun Hamm 23 82 0 12 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 168 8,513 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 491 5,878 0 08 Jul 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 162 6,069 0 01 Jul 2016
Learning Privately from Multiparty Data Jihun Hamm Yingjun Cao M. Belkin FedML 31 165 0 10 Feb 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 66 3,947 0 24 Nov 2015
Censoring Representations with an Adversary Harrison Edwards Amos Storkey AAML FaML 47 504 0 18 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 95 4,878 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 163 18,922 0 20 Dec 2014
Convolutional Neural Networks for Sentence Classification Yoon Kim AILaw VLM 554 13,395 0 25 Aug 2014
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 89 1,580 0 27 Jun 2012