| 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, 506-519, 2017 | 719* | 2017 |
| Extracting training data from diffusion models N Carlini, J Hayes, M Nasr, M Jagielski, V Sehwag, F Tramer, B Balle, ... 32nd USENIX Security Symposium (USENIX Security 23), 5253-5270, 2023 | 531 | 2023 |
| k-fingerprinting: A robust scalable website fingerprinting technique J Hayes, G Danezis 25th USENIX Security Symposium (USENIX Security 16), 1187-1203, 2016 | 489 | 2016 |
| Generating steganographic images via adversarial training J Hayes, G Danezis Advances in neural information processing systems 30, 2017 | 353 | 2017 |
| The loopix anonymity system AM Piotrowska, J Hayes, T Elahi, S Meiser, G Danezis 26th usenix security symposium (usenix security 17), 1199-1216, 2017 | 236 | 2017 |
| Unlocking high-accuracy differentially private image classification through scale S De, L Berrada, J Hayes, SL Smith, B Balle arXiv preprint arXiv:2204.13650, 2022 | 187 | 2022 |
| Learning universal adversarial perturbations with generative models J Hayes, G Danezis 2018 IEEE Security and Privacy Workshops (SPW), 43-49, 2018 | 182 | 2018 |
| Local and central differential privacy for robustness and privacy in federated learning M Naseri, J Hayes, E De Cristofaro arXiv preprint arXiv:2009.03561, 2020 | 167 | 2020 |
| Reconstructing training data with informed adversaries B Balle, G Cherubin, J Hayes 2022 IEEE Symposium on Security and Privacy (SP), 1138-1156, 2022 | 151 | 2022 |
| On visible adversarial perturbations & digital watermarking J Hayes Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 136 | 2018 |
| Towards unbounded machine unlearning M Kurmanji, P Triantafillou, J Hayes, E Triantafillou Advances in neural information processing systems 36, 2024 | 120 | 2024 |
| Website fingerprinting defenses at the application layer G Cherubin, J Hayes, M Juárez Proceedings on Privacy Enhancing Technologies 2017 (2), 168-185, 2017 | 110 | 2017 |
| Contamination attacks and mitigation in multi-party machine learning J Hayes, O Ohrimenko Advances in neural information processing systems 31, 2018 | 102 | 2018 |
| 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 | 95 | 2020 |
| Tight auditing of differentially private machine learning M Nasr, J Hayes, T Steinke, B Balle, F Tramèr, M Jagielski, N Carlini, ... 32nd USENIX Security Symposium (USENIX Security 23), 1631-1648, 2023 | 62 | 2023 |
| 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, 2020 | 60 | 2020 |
| Differentially private diffusion models generate useful synthetic images S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes, S De, ... arXiv preprint arXiv:2302.13861, 2023 | 59 | 2023 |
| Guard Sets for Onion Routing J Hayes, G Danezis Proceedings on Privacy Enhancing Technologies 1 (2), Pages 65–80, 2015 | 38* | 2015 |
| Bounding training data reconstruction in dp-sgd J Hayes, B Balle, S Mahloujifar Advances in Neural Information Processing Systems 36, 2024 | 33 | 2024 |
| Extensions and limitations of randomized smoothing for robustness guarantees J Hayes Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 26 | 2020 |
George DanezisMysten Labs & University College LondonVerified email at ucl.ac.uk