Florian Tramèr
Florian Tramèr
Assistant Professor of Computer Science, ETH Zurich
Zweryfikowany adres z inf.ethz.ch - Strona główna
Cytowane przez
Cytowane przez
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
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
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
On evaluating adversarial robustness
N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ...
arXiv preprint arXiv:1902.06705, 2019
The space of transferable adversarial examples
F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel
arXiv preprint arXiv:1704.03453, 2017
On adaptive attacks to adversarial example defenses
F Tramèr, N Carlini, W Brendel, A Madry
Conference on Neural Information Processing Systems (NeurIPS) 33, 2020
Extracting Training Data from Large Language Models
N Carlini, F Tramèr, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ...
30th USENIX Security Symposium (USENIX Security 21), 2633--2650, 2021
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
Physical adversarial examples for object detectors
K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, F Tramèr, A Prakash, ...
12th USENIX Workshop on Offensive Technologies (WOOT 18), 2018
Slalom: Fast, verifiable and private execution of neural networks in trusted hardware
F Tramèr, D Boneh
International Conference on Learning Representations (ICLR), 2019
Adversarial training and robustness for multiple perturbations
F Tramèr, D Boneh
Conference on Neural Information Processing Systems (NeurIPS) 32, 2019
Fairtest: Discovering unwarranted associations in data-driven applications
F Tramer, V Atlidakis, R Geambasu, D Hsu, JP Hubaux, M Humbert, ...
IEEE European Symposium on Security and Privacy (EuroS&P), 401-416, 2017
Sentinet: Detecting localized universal attacks against deep learning systems
E Chou, F Tramèr, G Pellegrino
IEEE Security and Privacy Workshops (SPW), 48-54, 2020
Label-Only Membership Inference Attacks
CAC Choo, F Tramèr, N Carlini, N Papernot
International Conference on Machine Learning (ICML), 1964--1974, 2021
Sealed-glass proofs: Using transparent enclaves to prove and sell knowledge
F Tramèr, F Zhang, H Lin, JP Hubaux, A Juels, E Shi
IEEE European Symposium on Security and Privacy (EuroS&P), 19-34, 2017
Differential privacy with bounded priors: reconciling utility and privacy in genome-wide association studies
F Tramèr, Z Huang, JP Hubaux, E Ayday
22nd ACM SIGSAC Conference on Computer and Communications Security (CCS …, 2015
Formal abstractions for attested execution secure processors
R Pass, E Shi, F Tramèr
Annual International Conference on the Theory and Applications of …, 2017
Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations
F Tramèr, J Behrmann, N Carlini, N Papernot, JH Jacobsen
International Conference on Machine Learning (ICML), 9561-9571, 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
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
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