Jamie Hayes
Titel
Zitiert von
Zitiert von
Jahr
LOGAN: evaluating privacy leakage of generative models using generative adversarial networks
J Hayes, L Melis, G Danezis, E De Cristofaro
arXiv preprint arXiv:1705.07663, 2017
150*2017
k-fingerprinting: A robust scalable website fingerprinting technique
J Hayes, G Danezis
25th {USENIX} Security Symposium ({USENIX} Security 16), 1187-1203, 2016
1442016
Generating steganographic images via adversarial training
J Hayes, G Danezis
Advances in Neural Information Processing Systems, 1954-1963, 2017
892017
The loopix anonymity system
AM Piotrowska, J Hayes, T Elahi, S Meiser, G Danezis
26th {USENIX} Security Symposium ({USENIX} Security 17), 1199-1216, 2017
782017
Website fingerprinting defenses at the application layer
G Cherubin, J Hayes, M Juarez
Proceedings on Privacy Enhancing Technologies 2017 (2), 186-203, 2017
372017
Learning universal adversarial perturbations with generative models
J Hayes, G Danezis
2018 IEEE Security and Privacy Workshops (SPW), 43-49, 2018
312018
Contamination attacks and mitigation in multi-party machine learning
J Hayes, O Ohrimenko
Advances in Neural Information Processing Systems, 6604-6615, 2018
272018
Guard Sets for Onion Routing
J Hayes, G Danezis
Proceedings on Privacy Enhancing Technologies 1 (2), Pages 65–80, 2015
24*2015
On visible adversarial perturbations & digital watermarking
J Hayes
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
142018
AnNotify: A private notification service
AM Piotrowska, J Hayes, N Gelernter, G Danezis, A Herzberg
Proceedings of the 2017 on Workshop on Privacy in the Electronic Society, 5-15, 2017
112017
Evading classifiers in discrete domains with provable optimality guarantees
B Kulynych, J Hayes, N Samarin, C Troncoso
arXiv preprint arXiv:1810.10939, 2018
102018
A framework for robustness certification of smoothed classifiers using f-divergences
KD Dvijotham, J Hayes, B Balle, Z Kolter, C Qin, A Gyorgy, K Xiao, ...
International Conference on Learning Representations, 2019
92019
Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy
M Naseri, J Hayes, E De Cristofaro
arXiv preprint arXiv:2009.03561, 2020
22020
TASP: Towards anonymity sets that persist
J Hayes, C Troncoso, G Danezis
Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society …, 2016
22016
traffic confirmation attacks despite noise
J Hayes
arXiv preprint arXiv:1601.04893, 2016
22016
Towards transformation-resilient provenance detection of digital media
J Hayes, Y Chen, S Dieleman, P Kohli, N Casagrande
arXiv preprint arXiv:2011.07355, 2020
2020
Adaptive Traffic Fingerprinting: Large-scale Inference under Realistic Assumptions
V Mavroudis, J Hayes
arXiv preprint arXiv:2010.10294, 2020
2020
Provable trade-offs between private & robust machine learning
J Hayes
arXiv preprint arXiv:2006.04622, 2020
2020
Unique properties of adversarially trained linear classifiers on Gaussian data
J Hayes
arXiv preprint arXiv:2006.03873, 2020
2020
Extensions and limitations of randomized smoothing for robustness guarantees
J Hayes
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
2020
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20