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2020 – today
- 2023
[i49]Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace:
Extracting Training Data from Diffusion Models. CoRR abs/2301.13188 (2023)- [i48]Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis:
Tight Auditing of Differentially Private Machine Learning. CoRR abs/2302.07956 (2023) - [i47]Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr:
Poisoning Web-Scale Training Datasets is Practical. CoRR abs/2302.10149 (2023) - [i46]Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini:
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators. CoRR abs/2302.13464 (2023) - 2022
- [c31]Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini:
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CCS 2022: 2779-2792 - [c30]Ambra Demontis, Xinyun Chen, Florian Tramèr:
AISec '22: 15th ACM Workshop on Artificial Intelligence and Security. CCS 2022: 3549-3551 - [c29]Hannah Brown
, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, Florian Tramèr:
What Does it Mean for a Language Model to Preserve Privacy? FAccT 2022: 2280-2292 - [c28]Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto:
Large Language Models Can Be Strong Differentially Private Learners. ICLR 2022 - [c27]Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramèr:
Data Poisoning Won't Save You From Facial Recognition. ICLR 2022 - [c26]Florian Tramèr:
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them. ICML 2022: 21692-21702 - [c25]Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr:
Membership Inference Attacks From First Principles. IEEE Symposium on Security and Privacy 2022: 1897-1914 - [e1]Ambra Demontis, Xinyun Chen, Florian Tramèr:
Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security, AISec 2022, Los Angeles, CA, USA, 11 November 2022. ACM 2022, ISBN 978-1-4503-9880-0 [contents] - [i45]Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, Florian Tramèr:
What Does it Mean for a Language Model to Preserve Privacy? CoRR abs/2202.05520 (2022) - [i44]Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang:
Quantifying Memorization Across Neural Language Models. CoRR abs/2202.07646 (2022) - [i43]Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini:
Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR abs/2202.12219 (2022) - [i42]Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini:
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CoRR abs/2204.00032 (2022) - [i41]Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr:
The Privacy Onion Effect: Memorization is Relative. CoRR abs/2206.10469 (2022) - [i40]Nicholas Carlini, Florian Tramèr, Krishnamurthy Dvijotham, J. Zico Kolter:
(Certified!!) Adversarial Robustness for Free! CoRR abs/2206.10550 (2022) - [i39]Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini:
Increasing Confidence in Adversarial Robustness Evaluations. CoRR abs/2206.13991 (2022) - [i38]Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. CoRR abs/2207.00099 (2022) - [i37]Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR abs/2208.12348 (2022) - [i36]Chawin Sitawarin, Florian Tramèr, Nicholas Carlini:
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. CoRR abs/2210.03297 (2022) - [i35]Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, Florian Tramèr:
Red-Teaming the Stable Diffusion Safety Filter. CoRR abs/2210.04610 (2022) - [i34]Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini:
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. CoRR abs/2210.17546 (2022) - [i33]Florian Tramèr, Gautam Kamath, Nicholas Carlini:
Considerations for Differentially Private Learning with Large-Scale Public Pretraining. CoRR abs/2212.06470 (2022) - 2021
- [b1]Florian Tramèr:
Measuring and enhancing the security of machine learning. Stanford University, USA, 2021 - [j5]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [c24]Hui Xu, Guanpeng Li, Homa Alemzadeh, Rakesh Bobba, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramèr:
Fourth International Workshop on Dependable and Secure Machine Learning - DSML 2021. DSN Workshops 2021: xvi - [c23]Florian Tramèr, Dan Boneh:
Differentially Private Learning Needs Better Features (or Much More Data). ICLR 2021 - [c22]Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot:
Label-Only Membership Inference Attacks. ICML 2021: 1964-1974 - [c21]Charlie Hou, Mingxun Zhou, Yan Ji, Phil Daian, Florian Tramèr, Giulia Fanti, Ari Juels:
SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning. NDSS 2021 - [c20]Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr:
Antipodes of Label Differential Privacy: PATE and ALIBI. NeurIPS 2021: 6934-6945 - [c19]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramèr:
Is Private Learning Possible with Instance Encoding? IEEE Symposium on Security and Privacy 2021: 410-427 - [c18]Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel:
Extracting Training Data from Large Language Models. USENIX Security Symposium 2021: 2633-2650 - [i32]Mani Malek, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr:
Antipodes of Label Differential Privacy: PATE and ALIBI. CoRR abs/2106.03408 (2021) - [i31]Evani Radiya-Dixit, Florian Tramèr:
Data Poisoning Won't Save You From Facial Recognition. CoRR abs/2106.14851 (2021) - [i30]Florian Tramèr:
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them. CoRR abs/2107.11630 (2021) - [i29]Nicholas Carlini, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Florian Tramèr:
NeuraCrypt is not private. CoRR abs/2108.07256 (2021) - [i28]Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto:
Large Language Models Can Be Strong Differentially Private Learners. CoRR abs/2110.05679 (2021) - [i27]Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr:
Membership Inference Attacks From First Principles. CoRR abs/2112.03570 (2021) - [i26]Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini:
Counterfactual Memorization in Neural Language Models. CoRR abs/2112.12938 (2021) - 2020
- [c17]Homa Alemzadeh, Rakesh Bobba, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramèr:
Third International Workshop on Dependable and Secure Machine Learning - DSML 2020. DSN Workshops 2020: x - [c16]Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML 2020: 9561-9571 - [c15]Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry:
On Adaptive Attacks to Adversarial Example Defenses. NeurIPS 2020 - [c14]Edward Chou, Florian Tramèr, Giancarlo Pellegrino:
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems. SP Workshops 2020: 48-54 - [c13]Florian Tramèr, Dan Boneh, Kenny Paterson:
Remote Side-Channel Attacks on Anonymous Transactions. USENIX Security Symposium 2020: 2739-2756 - [i25]Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. CoRR abs/2002.04599 (2020) - [i24]Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry:
On Adaptive Attacks to Adversarial Example Defenses. CoRR abs/2002.08347 (2020) - [i23]Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot:
Label-Only Membership Inference Attacks. CoRR abs/2007.14321 (2020) - [i22]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr:
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR abs/2011.05315 (2020) - [i21]Florian Tramèr, Dan Boneh:
Differentially Private Learning Needs Better Features (or Much More Data). CoRR abs/2011.11660 (2020) - [i20]Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel:
Extracting Training Data from Large Language Models. CoRR abs/2012.07805 (2020) - [i19]Florian Tramèr, Dan Boneh, Kenneth G. Paterson:
Remote Side-Channel Attacks on Anonymous Transactions. IACR Cryptol. ePrint Arch. 2020: 220 (2020)
2010 – 2019
- 2019
- [j4]Lorenz Breidenbach, Philip Daian, Florian Tramèr, Ari Juels:
The Hydra Framework for Principled, Automated Bug Bounties. IEEE Secur. Priv. 17(4): 53-61 (2019) - [c12]Florian Tramèr, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino, Dan Boneh:
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning. CCS 2019: 2005-2021 - [c11]Florian Tramèr, Dan Boneh:
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware. ICLR 2019 - [c10]Florian Tramèr, Dan Boneh:
Adversarial Training and Robustness for Multiple Perturbations. NeurIPS 2019: 5858-5868 - [i18]Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot:
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. CoRR abs/1903.10484 (2019) - [i17]Florian Tramèr, Dan Boneh:
Adversarial Training and Robustness for Multiple Perturbations. CoRR abs/1904.13000 (2019) - [i16]Charlie Hou, Mingxun Zhou, Yan Ji, Phil Daian, Florian Tramèr, Giulia Fanti, Ari Juels:
SquirRL: Automating Attack Discovery on Blockchain Incentive Mechanisms with Deep Reinforcement Learning. CoRR abs/1912.01798 (2019) - [i15]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [c9]Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel:
Ensemble Adversarial Training: Attacks and Defenses. ICLR (Poster) 2018 - [c8]Lorenz Breidenbach, Philip Daian, Florian Tramèr, Ari Juels:
Enter the Hydra: Towards Principled Bug Bounties and Exploit-Resistant Smart Contracts. USENIX Security Symposium 2018: 1335-1352 - [c7]Dawn Song, Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramèr, Atul Prakash, Tadayoshi Kohno:
Physical Adversarial Examples for Object Detectors. WOOT @ USENIX Security Symposium 2018 - [i14]Florian Tramèr, Dan Boneh:
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware. CoRR abs/1806.03287 (2018) - [i13]Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramèr, Atul Prakash, Tadayoshi Kohno, Dawn Song:
Physical Adversarial Examples for Object Detectors. CoRR abs/1807.07769 (2018) - [i12]Florian Tramèr, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino, Dan Boneh:
Ad-versarial: Defeating Perceptual Ad-Blocking. CoRR abs/1811.03194 (2018) - [i11]Edward Chou, Florian Tramèr, Giancarlo Pellegrino, Dan Boneh:
SentiNet: Detecting Physical Attacks Against Deep Learning Systems. CoRR abs/1812.00292 (2018) - 2017
- [j3]Jean Louis Raisaro, Florian Tramèr, Zhanglong Ji, Diyue Bu, Yongan Zhao, W. Knox Carey, David D. Lloyd, Heidi Sofia, Dixie Baker, Paul Flicek, Suyash S. Shringarpure, Carlos D. Bustamante, Shuang Wang, Xiaoqian Jiang, Lucila Ohno-Machado, Haixu Tang, XiaoFeng Wang, Jean-Pierre Hubaux:
Addressing Beacon re-identification attacks: quantification and mitigation of privacy risks. J. Am. Medical Informatics Assoc. 24(4): 799-805 (2017) - [j2]Anh Pham, Italo Dacosta, Bastien Jacot-Guillarmod, Kévin Huguenin, Taha Hajar, Florian Tramèr, Virgil D. Gligor, Jean-Pierre Hubaux:
PrivateRide: A Privacy-Enhanced Ride-Hailing Service. Proc. Priv. Enhancing Technol. 2017(2): 38-56 (2017) - [c6]Rafael Pass, Elaine Shi, Florian Tramèr:
Formal Abstractions for Attested Execution Secure Processors. EUROCRYPT (1) 2017: 260-289 - [c5]Florian Tramèr, Fan Zhang, Huang Lin, Jean-Pierre Hubaux, Ari Juels, Elaine Shi:
Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge. EuroS&P 2017: 19-34 - [c4]Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, Huang Lin:
FairTest: Discovering Unwarranted Associations in Data-Driven Applications. EuroS&P 2017: 401-416 - [i10]Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel:
The Space of Transferable Adversarial Examples. CoRR abs/1704.03453 (2017) - [i9]Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, Patrick D. McDaniel:
Ensemble Adversarial Training: Attacks and Defenses. CoRR abs/1705.07204 (2017) - [i8]Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Dawn Song, Tadayoshi Kohno, Amir Rahmati, Atul Prakash, Florian Tramèr:
Note on Attacking Object Detectors with Adversarial Stickers. CoRR abs/1712.08062 (2017) - [i7]Lorenz Breidenbach, Philip Daian, Florian Tramèr, Ari Juels:
Enter the Hydra: Towards Principled Bug Bounties and Exploit-Resistant Smart Contracts. IACR Cryptol. ePrint Arch. 2017: 1090 (2017) - 2016
- [j1]Sonia Bogos, Florian Tramèr, Serge Vaudenay:
On solving L P N using B K W and variants - Implementation and analysis. Cryptogr. Commun. 8(3): 331-369 (2016) - [c3]Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, Thomas Ristenpart:
Stealing Machine Learning Models via Prediction APIs. USENIX Security Symposium 2016: 601-618 - [i6]Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, Thomas Ristenpart:
Stealing Machine Learning Models via Prediction APIs. CoRR abs/1609.02943 (2016) - [i5]Florian Tramèr, Fan Zhang, Huang Lin, Jean-Pierre Hubaux, Ari Juels, Elaine Shi:
Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge. IACR Cryptol. ePrint Arch. 2016: 635 (2016) - [i4]Rafael Pass, Elaine Shi, Florian Tramèr:
Formal Abstractions for Attested Execution Secure Processors. IACR Cryptol. ePrint Arch. 2016: 1027 (2016) - 2015
- [c2]Florian Tramèr, Zhicong Huang, Jean-Pierre Hubaux, Erman Ayday:
Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies. CCS 2015: 1286-1297 - [c1]Alexandre Duc, Florian Tramèr, Serge Vaudenay:
Better Algorithms for LWE and LWR. EUROCRYPT (1) 2015: 173-202 - [i3]Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, Huang Lin:
Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit. CoRR abs/1510.02377 (2015) - [i2]Sonia Bogos, Florian Tramèr, Serge Vaudenay:
On Solving Lpn using BKW and Variants. IACR Cryptol. ePrint Arch. 2015: 49 (2015) - [i1]Alexandre Duc, Florian Tramèr, Serge Vaudenay:
Better Algorithms for LWE and LWR. IACR Cryptol. ePrint Arch. 2015: 56 (2015)
Coauthor Index
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