Green Lighting ML: Confidentiality, Integrity, and Availability of
Machine Learning Systems in Deployment

Green Lighting ML: Confidentiality, Integrity, and Availability of Machine Learning Systems in Deployment

9 July 2020

Abhishek Gupta

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Papers citing "Green Lighting ML: Confidentiality, Integrity, and Availability of Machine Learning Systems in Deployment"

7 / 7 papers shown

Title
Adversarial Machine Learning -- Industry Perspectives Ramnath Kumar Magnus Nyström J. Lambert Andrew Marshall Mario Goertzel Andi Comissoneru Matt Swann Sharon Xia AAML SILM 89 236 0 04 Feb 2020
Model Cards for Model Reporting Margaret Mitchell Simone Wu Andrew Zaldivar Parker Barnes Lucy Vasserman Ben Hutchinson Elena Spitzer Inioluwa Deborah Raji Timnit Gebru 130 1,903 0 05 Oct 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos Basel Alomair 150 1,148 0 22 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 243 3,194 0 01 Feb 2018
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning Rock Stevens H. Aggarwal Himani Arora Sanghyun Hong M. Hicks Chetan Arora SILM AAML 51 18 0 17 Jan 2017
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 109 1,810 0 09 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 268 8,583 0 16 Aug 2016