Security and Privacy for Artificial Intelligence: Opportunities and Challenges

9 February 2021

Papers citing "Security and Privacy for Artificial Intelligence: Opportunities and Challenges"

24 / 74 papers shown

Title
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 280 4,160 0 18 Oct 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 312 4,657 0 18 Oct 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 145 1,909 0 08 Oct 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 109 1,811 0 09 Sep 2016
Data Poisoning Attacks on Factorization-Based Collaborative Filtering Bo Li Yining Wang Aarti Singh Yevgeniy Vorobeychik AAML 89 346 0 29 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 547 5,912 0 08 Jul 2016
Adversarial Perturbations Against Deep Neural Networks for Malware Classification Kathrin Grosse Nicolas Papernot Praveen Manoharan Michael Backes Patrick McDaniel AAML 79 418 0 14 Jun 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 116 1,742 0 24 May 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 408 17,615 0 17 Feb 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 117 3,968 0 24 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 154 4,905 0 14 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 118 3,077 0 14 Nov 2015
A review of homomorphic encryption and software tools for encrypted statistical machine learning L. Aslett P. Esperança Chris C. Holmes 52 72 0 26 Aug 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 367 19,745 0 09 Mar 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Crypto-Nets: Neural Networks over Encrypted Data P. Xie Mikhail Bilenko Tom Finley Ran Gilad-Bachrach Kristin E. Lauter M. Naehrig FedML 113 150 0 18 Dec 2014
Towards Deep Neural Network Architectures Robust to Adversarial Examples S. Gu Luca Rigazio AAML 78 844 0 11 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 174 3,275 0 05 Dec 2014
Security Evaluation of Support Vector Machines in Adversarial Environments Battista Biggio Igino Corona B. Nelson Benjamin I. P. Rubinstein Davide Maiorca Giorgio Fumera Giorgio Giacinto and Fabio Roli AAML 73 125 0 30 Jan 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 289 14,968 1 21 Dec 2013
Do Deep Nets Really Need to be Deep? Lei Jimmy Ba R. Caruana 188 2,120 0 21 Dec 2013
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 127 1,595 0 27 Jun 2012
Security Analysis of Online Centroid Anomaly Detection Marius Kloft Pavel Laskov 119 97 0 27 Feb 2010