Nicolas Papernot

According to our database1, Nicolas Papernot authored at least 19 papers between 2014 and 2018.

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Bibliography

2018
Making machine learning robust against adversarial inputs.
Commun. ACM, 2018

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Ensemble Adversarial Training: Attacks and Defenses.
Proceedings of the 6th International Conference on Learning Representations, 2018

Scalable Private Learning with PATE.
Proceedings of the 6th International Conference on Learning Representations, 2018

SoK: Security and Privacy in Machine Learning.
Proceedings of the 2018 IEEE European Symposium on Security and Privacy, 2018

A Marauder's Map of Security and Privacy in Machine Learning: An overview of current and future research directions for making machine learning secure and private.
Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security, 2018

Detection under Privileged Information.
Proceedings of the 2018 on Asia Conference on Computer and Communications Security, 2018

2017
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data.
Proceedings of the 5th International Conference on Learning Representations, 2017

Adversarial Attacks on Neural Network Policies.
Proceedings of the 5th International Conference on Learning Representations, 2017

Adversarial Examples for Malware Detection.
Proceedings of the Computer Security - ESORICS 2017, 2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approches.
Proceedings of the 30th IEEE Computer Security Foundations Symposium, 2017

Practical Black-Box Attacks against Machine Learning.
Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 2017

2016
Machine Learning in Adversarial Settings.
IEEE Security & Privacy, 2016

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks.
Proceedings of the IEEE Symposium on Security and Privacy, 2016

Crafting adversarial input sequences for recurrent neural networks.
Proceedings of the 2016 IEEE Military Communications Conference, 2016

Mapping sample scenarios to operational models.
Proceedings of the 2016 IEEE Military Communications Conference, 2016

The Limitations of Deep Learning in Adversarial Settings.
Proceedings of the IEEE European Symposium on Security and Privacy, 2016

2015
Enforcing agile access control policies in relational databases using views.
Proceedings of the 34th IEEE Military Communications Conference, 2015

2014
Security and Science of Agility.
Proceedings of the First ACM Workshop on Moving Target Defense, 2014


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