Florian Tramèr | Publications

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Conferences	SNAP: Efficient Extraction of Private Properties with Poisoning [arXiv] Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr and Jonathan Ullman IEEE S&P 2023 Measuring Forgetting of Memorized Training Examples [arXiv] Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot and Chiyuan Zhang ICLR 2023 (Certified!!) Adversarial Robustness for Free! [arXiv] Nicholas Carlini, Florian Tramèr*, Rice Leslie, Mingjie Sun, Krishnamurthy Dvijotham and J Zico Kolter ICLR 2023* Quantifying Memorization Across Neural Language Models [arXiv] Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr and Chiyuan Zhang (αβ-order) ICLR 2023 (Spotlight Presentation) Increasing Confidence in Adversarial Robustness Evaluations [arXiv] Roland S. Zimmermann, Wieland Brendel, Florian Tramèr and Nicholas Carlini NeurIPS 2022 The Privacy Onion Effect: Memorization is Relative [arXiv] Nicholas Carlini, Matthew Jagielski, Nicolas Papernot, Andreas Terzis, Florian Tramèr and Chiyuan Zhang (αβ-order) NeurIPS 2022 Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets [arXiv] Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong and Nicholas Carlini (βα-order) CCS 2022 Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them [arXiv] Florian Tramèr ICML 2022 (Long Presentation) What Does it Mean for a Language Model to Preserve Privacy? [arXiv] Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri and Florian Tramèr (αβ-order) FAccT 2022 Membership Inference Attacks From First Principles [arXiv] Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis and Florian Tramèr (αβ-order) IEEE S&P 2022 Large Language Models Can Be Strong Differentially Private Learners [arXiv] Xuechen Li, Florian Tramèr, Percy Liang and Tatsunori Hashimoto ICLR 2022 (Oral Presentation) Data Poisoning Won’t Save You From Facial Recognition [arXiv] Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini and Florian Tramèr ICLR 2022 Antipodes of Label Differential Privacy: PATE and ALIBI [arXiv] Mani Malek, Ilya Mironov, Karthik Prasad, Igor Shilov and Florian Tramèr (αβ-order) NeurIPS 2021 Extracting Training Data from Large Language Models [arXiv] Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea and Colin Raffel USENIX Security 2021 Label-Only Membership Inference Attacks [arXiv] Christopher A. Choquette Choo, Florian Tramèr, Nicholas Carlini and Nicolas Papernot ICML 2021 Differentially Private Learning Needs Better Features (or Much More Data) [arXiv] Florian Tramèr and Dan Boneh ICLR 2021 (Spotlight Presentation) Is Private Learning Possible with Instance Encoding? [arXiv] Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta and Florian Tramèr (αβ-order) IEEE S&P 2021 SquirRL: Automating Attack Discovery on Blockchain Incentive Mechanisms with Deep Reinforcement Learning [arXiv] Charlie Hou, Mingxun Zhou, Yan Ji, Phil Daian, Florian Tramèr, Giulia Fanti and Ari Juels NDSS 2021 On Adaptive Attacks to Adversarial Example Defenses [arXiv] Florian Tramèr, Nicholas Carlini, Wieland Brendel* and Aleksander Madry NeurIPS 2020 Remote Side-Channel Attacks on Anonymous Transactions [eprint] Florian Tramèr, Dan Boneh and Kenneth G. Paterson USENIX Security 2020 Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations [arXiv] Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot and Jörn-Henrik Jacobsen ICML 2020 Adversarial Training and Robustness for Multiple Perturbations [arXiv] Florian Tramèr and Dan Boneh NeurIPS 2019 (Spotlight Presentation) AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning [arXiv] Florian Tramèr, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino and Dan Boneh CCS 2019 Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware [arXiv] Florian Tramèr and Dan Boneh ICLR 2019 (Oral Presentation) Enter the Hydra: Towards Principled Bug Bounties and Exploit-Resistant Smart Contracts [eprint] Lorenz Breidenbach, Phil Daian, Florian Tramèr* and Ari Juels USENIX Security 2018 (Invited to appear in IEEE Security and Privacy Magazine) Ensemble Adversarial Training: Attacks and Defenses [arXiv] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh and Patrick McDaniel ICLR 2018 PrivateRide: A Privacy-Preserving and Secure Ride-Hailing Service [PDF] Anh Pham, Italo Dacosta, Bastien Jacot-Guillarmod, Kévin Huguenin, Taha Hajar, Florian Tramèr and Jean-Pierre Hubaux PETS 2017 Formal Abstractions for Attested Execution Secure Processors [eprint] Rafael Pass, Elaine Shi and Florian Tramèr (αβ-order) EUROCRYPT 2017 Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge [eprint] Florian Tramèr, Fan Zhang, Huang Lin, Jean-Pierre Hubaux, Ari Juels and Elaine Shi EuroS&P 2017 FairTest: Discovering Unwarranted Associations in Data-Driven Applications [arXiv] Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels and Huang Lin EuroS&P 2017 Stealing Machine Learning Models via Prediction APIs [arXiv] Florian Tramèr, Fan Zhang, Ari Juels, Michael Reiter and Thomas Ristenpart USENIX Security 2016 Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies [PDF] Florian Tramèr, Zhicong Huang, Jean-Pierre Hubaux and Erman Ayday CCS 2015 Better Algorithms for LWE and LWR [eprint] Alexandre Duc, Florian Tramèr and Serge Vaudenay (αβ-order) EUROCRYPT 2015
Journals	Advances and Open Problems in Federated Learning [arXiv] Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner and others Foundations and Trends in ML 2021 The Hydra Framework for Principled, Automated Bug Bounties [PDF] Lorenz Breidenbach, Phil Daian, Florian Tramèr* and Ari Juels IEEE Security & Privacy 2019 Addressing Beacon Re-Identification Attacks: Quantification and Mitigation of Privacy Risks [PDF] Jean Louis Raisaro, Florian Tramèr*, Zhanglong Ji, Diyue Bu, Yongan Zhao, Knox Carey and others JAMIA 2017* On Solving LPN using BKW and Variants [eprint] Sonia Bogos, Florian Tramèr and Serge Vaudenay (αβ-order) Cryptography and Communications 2016
Workshops	Red-Teaming the Stable Diffusion Safety Filter [arXiv] Javier Rando, Daniel Paleka, David Lindner, Lennard Heim and Florian Tramèr NeurIPS ML Safety Workshop 2022 (Best Paper Award) NeuraCrypt is not private [arXiv] Nicholas Carlini, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody and Florian Tramèr (αβ-order) PPML Workshop 2021 SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems [arXiv] Edward Chou, Florian Tramèr and Giancarlo Pellegrino DLS Workshop 2020 Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness [arXiv] Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr and Nicolas Papernot ICLR SafeML Workshop 2019 Physical Adversarial Examples for Object Detectors [arXiv] Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramèr*, Atul Prakash, Tadayoshi Kohno and Dawn Song WOOT Workshop 2018*
Manuscripts	Poisoning Web-Scale Training Datasets is Practical [arXiv] Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas and Florian Tramèr Tight Auditing of Differentially Private Machine Learning [arXiv] Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini and Andreas Terzis Extracting Training Data from Diffusion Models [arXiv] Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito and Eric Wallace Considerations for Differentially Private Learning with Large-Scale Public Pretraining [arXiv] Florian Tramèr, Gautam Kamath and Nicholas Carlini Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy [arXiv] Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A Choquette-Choo and Nicholas Carlini Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems [arXiv] Chawin Sitawarin, Florian Tramèr and Nicholas Carlini Debugging Differential Privacy: A Case Study for Privacy Auditing [arXiv] Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski and Nicholas Carlini (βα-order) Counterfactual Memorization in Neural Language Models [arXiv] Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr and Nicholas Carlini On the Opportunities and Risks of Foundation Models [arXiv] Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch and others The Space of Transferable Adversarial Examples [arXiv] Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh and Patrick McDaniel
PhD Thesis	Measuring and Enhancing the Security of Machine Learning [PDF] Florian Tramèr PhD Thesis 2021
Master Thesis	Algorithmic Fairness Revisited [PDF] Florian Tramèr Master Thesis 2015