| Ensemble Adversarial Training: Attacks and Defenses F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel International Conference on Learning Representations (ICLR), 2018 | 1551 | 2018 |
| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14 (1), 2019 | 1004 | 2019 |
| Stealing machine learning models via prediction apis F Tramèr, F Zhang, A Juels, MK Reiter, T Ristenpart 25th {USENIX} Security Symposium ({USENIX} Security 16), 601-618, 2016 | 979 | 2016 |
| On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 408 | 2019 |
| The space of transferable adversarial examples F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1704.03453, 2017 | 360 | 2017 |
| On adaptive attacks to adversarial example defenses F Tramèr, N Carlini, W Brendel, A Madry In 2020 Conference on Neural Information Processing Systems (NeurIPS) (Vol. 33), 2020 | 235 | 2020 |
| Physical adversarial examples for object detectors D Song, K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, F Tramèr, ... 12th {USENIX} Workshop on Offensive Technologies ({WOOT} 18), 2018 | 225 | 2018 |
| Slalom: Fast, verifiable and private execution of neural networks in trusted hardware F Tramèr, D Boneh International Conference on Learning Representations (ICLR), 2019 | 159 | 2019 |
| Adversarial training and robustness for multiple perturbations F Tramèr, D Boneh In 2019 Conference on Neural Information Processing Systems (NeurIPS) (Vol. 32), 2019 | 154 | 2019 |
| Extracting Training Data from Large Language Models N Carlini, F Tramèr, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ... arXiv preprint arXiv:2012.07805, 2020 | 109 | 2020 |
| FairTest: Discovering unwarranted associations in data-driven applications F Tramèr, V Atlidakis, R Geambasu, D Hsu, JP Hubaux, M Humbert, ... 2017 IEEE European Symposium on Security and Privacy (EuroS&P), 401-416, 2017 | 94 | 2017 |
| Sealed-glass proofs: Using transparent enclaves to prove and sell knowledge F Tramèr, F Zhang, H Lin, JP Hubaux, A Juels, E Shi 2017 IEEE European Symposium on Security and Privacy (EuroS&P), 19-34, 2017 | 84 | 2017 |
| Differential privacy with bounded priors: reconciling utility and privacy in genome-wide association studies F Tramèr, Z Huang, JP Hubaux, E Ayday Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications …, 2015 | 82 | 2015 |
| Formal abstractions for attested execution secure processors R Pass, E Shi, F Tramèr Annual International Conference on the Theory and Applications of …, 2017 | 78 | 2017 |
| Sentinet: Detecting localized universal attacks against deep learning systems E Chou, F Tramer, G Pellegrino 2020 IEEE Security and Privacy Workshops (SPW), 48-54, 2020 | 73* | 2020 |
| Privateride: A privacy-enhanced ride-hailing service A Pham, I Dacosta, B Jacot-Guillarmod, K Huguenin, T Hajar, F Tramèr, ... Proceedings on Privacy Enhancing Technologies 2017 (2), 38–56, 2017 | 65 | 2017 |
| Addressing Beacon re-identification attacks: quantification and mitigation of privacy risks JL Raisaro, F Tramèr, Z Ji, D Bu, Y Zhao, K Carey, D Lloyd, H Sofia, ... Journal of the American Medical Informatics Association 24 (4), 799-805, 2017 | 59 | 2017 |
| Better algorithms for LWE and LWR A Duc, F Tramèr, S Vaudenay Annual International Conference on the Theory and Applications of …, 2015 | 57 | 2015 |
| Enter the hydra: Towards principled bug bounties and exploit-resistant smart contracts L Breidenbach, P Daian, F Tramèr, A Juels 27th {USENIX} Security Symposium ({USENIX} Security 18), 1335-1352, 2018 | 53 | 2018 |
| Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations F Tramèr, J Behrmann, N Carlini, N Papernot, JH Jacobsen International Conference on Machine Learning, 9561-9571, 2020 | 51* | 2020 |
Dan BonehProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
