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Artificial Intelligence systems require a through assessment of different pillars of trust, namely, fairness, interpretability, data and model privacy, reliability (safety) and robustness against against adversarial attacks.
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharyya, Michael Backes, Mario Fritz, and Yang Zhang. 2019a · 1904
Earlier work this paper cites.
Evaluating Differentially Private Machine Learning in Practice. In 28th USENIX Security Symposium (USENIX Security 19) . USENIX Association, Santa Clara, CA, 1895–1912
Bargav Jayaraman and David Evans. 2019 · 1912
Earlier work this paper cites.
Using additive noise in back-propagation training
L. Holmstrom and P. Koistinen. 1992 · 1992
Earlier work this paper cites.
Noise injection into inputs in back-propagation learning
K. Matsuoka. 1992 · 1992
Earlier work this paper cites.
Maximally fault tolerant neural networks
C. Neti, M. H. Schneider, and E. D. Young. 1992 · 1992
Earlier work this paper cites.
Training with Noise is Equivalent to Tikhonov Regularization
Chris M. Bishop. 1995 · 1995
Earlier work this paper cites.
Complete and partial fault tolerance of feedforward neural nets
D. S. Phatak and I. Koren. 1995 · 1995
Earlier work this paper cites.
Synthesis of fault-tolerant feedforward neural networks using minimax optimization
D. Deodhare, M. Vidyasagar, and S. Sathiya Keethi. 1998 · 1998
Earlier work this paper cites.
A Quantitative Study of Fault Tolerance, Noise Immunity, and Generalization Ability of MLPs
Jose Bernier, Julio Ortega, Eduardo Vidal, Ignacio Rojas, and Alberto Prieto. 2001 · 2001
Earlier work this paper cites.
Differential Privacy: A Survey of Results. In Theory and Applications of Models of Computation , Manindra Agrawal, Dingzhu Du, Zhenhua Duan, and Angsheng Li (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1–19
Cynthia Dwork. 2008 · 2008
Earlier work this paper cites.
Convergence and Objective Functions of Some Fault/Noise-Injection-Based Online Learning Algorithms for RBF Networks
K. I. . Ho, C. Leung, and J. Sum. 2010 · 2010
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth. 2014 · 2014
Earlier work this paper cites.
Applications of Fuzzy Integrals for Predicting Software Fault-prone
Cong Jin and Shu-Wei Jin. 2014 · 2014
Earlier work this paper cites.
Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures. In Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security (CCS ’15) . ACM, New York, NY, USA, 1322–1333
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
Cited alongside, same era.
Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS ’16) . ACM, New York, NY, USA, 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
Yu-Xiang Wang, Jing Lei, and Stephen E. Fienberg. 2016 · 2016
Cited alongside, same era.
Transport reliability on axonal cytoskeleton. In 2017 14th International Conference on Engineering of Modern Electric Systems (EMES) . 160–163
V. Beiu, N. C. Rohatinovici, L. Dăuş, and V. E. Balas. 2017 · 2017
Cited alongside, same era.
Scalable Private Learning with PATE. In International Conference on Learning Representations
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson. 2018 · 2018
Later among the works it cites.
Certifiable Distributional Robustness with Principled Adversarial Training. In International Conference on Learning Representations
Aman Sinha, Hongseok Namkoong, and John Duchi. 2018 · 2018
Later among the works it cites.
Weight Noise Injection-Based MLPs With Group Lasso Penalty: Asymptotic Convergence and Application to Node Pruning
J. Wang, Q. Chang, Q. Chang, Y. Liu, and N. R. Pal. 2018 · 2018
Later among the works it cites.
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In 28th USENIX Security Symposium (USENIX Security 19) . USENIX Association, Santa Clara, CA, 267–284
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
Closest in time.
Adversarial Fault Tolerant Training for Deep Neural Networks
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Regularizing Multilayer Perceptron for Robustness
P. Dey, K. Nag, T. Pal, and N. R. Pal. 2018 · 2017
Cited alongside, same era.
Rényi Differential Privacy. In 2017 IEEE 30th Computer Security Foundations Symposium (CSF) . 263–275
I. Mironov. 2017 · 2017
Cited alongside, same era.
Membership Inference Attacks Against Machine Learning Models. In 2017 IEEE Symposium on Security and Privacy (SP) . 3–18
R. Shokri, M. Stronati, C. Song, and V. Shmatikov. 2017 · 2017
Cited alongside, same era.
On the Exact Reliability Enhancements of Small Hammock Networks
S. R. Cowell, V. Beiu, L. Dăuş, and P. Poulin. 2018 · 2018
Cited alongside, same era.
A Survey of Adversarial Machine Learning in Cyber Warfare
Vasisht Duddu. 2018 · 2018
Cited alongside, same era.
Property Inference Attacks on Fully Connected Neural Networks Using Permutation Invariant Representations. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS ’18) . ACM, New York, NY, USA, 619–633
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov. 2018 · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Cited alongside, same era.
Differentiable Abstract Interpretation for Provably Robust Neural Networks. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Jennifer Dy and Andreas Krause (Eds.), Vol. 80. PMLR, Stockholmsmässan, Stockholm Sweden, 3578–3586
Matthew Mirman, Timon Gehr, and Martin Vechev. 2018 · 2018
Cited alongside, same era.
Vasisht Duddu, D Vijay Rao, and Valentina E Balas. 2019 · 2019
Closest in time.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, California, USA, 1823–1832
Christian Etmann, Sebastian Lunz, Peter Maass, and Carola Schoenlieb. 2019 · 2019
Closest in time.
Differentially Private Fair Learning. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, California, USA, 3000–3008
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi Malvajerdi, and Jonathan Ullman. 2019 · 2019
Closest in time.
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong. 2019 · 2019
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Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, and Michael Backes. 2019b · 2019
Closest in time.
Privacy Risks of Explaining Machine Learning Models
Reza Shokri, Martin Strobel, and Yair Zick. 2019 · 2019
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Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Liwei Song, Reza Shokri, and Prateek Mittal. 2019 · 2019
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Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019 · 2019
Closest in time.