Papernot, Nicolas

59 publications

ICLR 2025 Breach by a Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, Nicolas Papernot

ICML 2025 Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot

ICML 2025 Fast Exact Unlearning for In-Context Learning Data for LLMs Andrei Ioan Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot

ICML 2025 Language Models May Verbatim Complete Text They Were Not Explicitly Trained on Ken Liu, Christopher A. Choquette-Choo, Matthew Jagielski, Peter Kairouz, Sanmi Koyejo, Percy Liang, Nicolas Papernot

TMLR 2025 Learned-Database Systems Security Roei Schuster, Jin Peng Zhou, Thorsten Eisenhofer, Paul Grubbs, Nicolas Papernot

ICML 2025 Leveraging Per-Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite

NeurIPS 2025 Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Eleni Triantafillou, Peter Kairouz, Nicole Elyse Mitchell, Niloofar Mireshghallah, Abigail Z. Jacobs, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Ilia Shumailov, Andreas Terzis, Solon Barocas, Jennifer Wortman Vaughan, Danah Boyd, Yejin Choi, Sanmi Koyejo, Fernando Delgado, Percy Liang, Daniel E. Ho, Pamela Samuelson, Miles Brundage, David Bau, Seth Neel, Hanna Wallach, Amy B. Cyphert, Mark Lemley, Nicolas Papernot, Katherine Lee

TMLR 2025 Selective Prediction via Training Dynamics Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Sokun, Nicolas Papernot

ICLRW 2025 Societal Alignment Frameworks Can Improve LLM Alignment Karolina Stanczak, Nicholas Meade, Mehar Bhatia, Hattie Zhou, Konstantin Böttinger, Jeremy Barnes, Jason Stanley, Jessica Montgomery, Richard Zemel, Nicolas Papernot, Nicolas Chapados, Denis Therien, Timothy P Lillicrap, Ana Marasovic, Sylvie Delacroix, Gillian K Hadfield, Siva Reddy

ICML 2025 Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot

ICLR 2025 Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model Tudor Ioan Cebere, Aurélien Bellet, Nicolas Papernot

NeurIPS 2025 What Does It Take to Build a Performant Selective Classifier? Stephan Rabanser, Nicolas Papernot

ICML 2024 Auditing Private Prediction Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr

TMLR 2024 Augment Then Smooth: Reconciling Differential Privacy with Certified Robustness Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot

TMLR 2024 Beyond Labeling Oracles - What Does It Mean to Steal ML Models? Avital Shafran, Ilia Shumailov, Murat A Erdogdu, Nicolas Papernot

ICLRW 2024 Confidential-DPproof : Confidential Proof of Differentially Private Training Ali Shahin Shamsabadi, Gefei Tan, Tudor Ioan Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller

ICLR 2024 Confidential-DPproof: Confidential Proof of Differentially Private Training Ali Shahin Shamsabadi, Gefei Tan, Tudor Ioan Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller

TMLR 2024 From Differential Privacy to Bounds on Membership Inference: Less Can Be More Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot

NeurIPS 2024 LLM Dataset Inference: Did You Train on My Dataset? Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic

ICLR 2024 Memorization in Self-Supervised Learning Improves Downstream Generalization Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch

ICML 2024 Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan

NeurIPS 2024 Temporal-Difference Learning Using Distributed Error Signals Jonas Guan, Shon Eduard Verch, Claas Voelcker, Ethan C. Jackson, Nicolas Papernot, William A. Cunningham

ICML 2024 The Fundamental Limits of Least-Privilege Learning Theresa Stadler, Bogdan Kulynych, Michael Gastpar, Nicolas Papernot, Carmela Troncoso

CVPR 2023 Architectural Backdoors in Neural Networks Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot

ICLR 2023 Confidential-PROFITT: Confidential PROof of FaIr Training of Trees Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller

NeurIPS 2023 Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch

NeurIPS 2023 Have It Your Way: Individualized Privacy Assignment for DP-SGD Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot

NeurIPSW 2023 Learning to Walk Impartially on the Pareto Frontier of Fairness, Privacy, and Utility Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot

ICLR 2023 Measuring Forgetting of Memorized Training Examples Matthew Jagielski, Om Thakkar, Florian Tramer, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang

NeurIPSW 2023 Regulation Games for Trustworthy Machine Learning Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot

NeurIPS 2023 Robust and Actively Secure Serverless Collaborative Learning Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang

ICLRW 2023 Sentence Embedding Encoders Are Easy to Steal but Hard to Defend Adam Dziedzic, Franziska Boenisch, Mingjian Jiang, Haonan Duan, Nicolas Papernot

NeurIPS 2023 Training Private Models That Know What They Don’t Know Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot

ICLR 2022 A Zest of LIME: Towards Architecture-Independent Model Distances Hengrui Jia, Hongyu Chen, Jonas Guan, Ali Shahin Shamsabadi, Nicolas Papernot

NeurIPS 2022 Dataset Inference for Self-Supervised Models Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot

ICLR 2022 Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot, Thomas Steinke

NeurIPS 2022 In Differential Privacy, There Is Truth: On Vote-Histogram Leakage in Ensemble Private Learning Jiaqi Wang, Roei Schuster, I Shumailov, David Lie, Nicolas Papernot

ICLR 2022 Increasing the Cost of Model Extraction with Calibrated Proof of Work Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot

ICLR 2022 Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi

ICML 2022 On the Difficulty of Defending Self-Supervised Learning Against Model Extraction Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot

NeurIPS 2022 On the Limitations of Stochastic Pre-Processing Defenses Yue Gao, I Shumailov, Kassem Fawaz, Nicolas Papernot

NeurIPS 2022 The Privacy Onion Effect: Memorization Is Relative Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramer

NeurIPS 2022 Washing the Unwashable : On the (Im)possibility of Fairwashing Detection Ali Shahin Shamsabadi, Mohammad Yaghini, Natalie Dullerud, Sierra Wyllie, Ulrich Aïvodji, Aisha Alaagib, Sébastien Gambs, Nicolas Papernot

ICLR 2021 CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang

CVPR 2021 Data-Free Model Extraction Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, Nicolas Papernot

ICLR 2021 Dataset Inference: Ownership Resolution in Machine Learning Pratyush Maini, Mohammad Yaghini, Nicolas Papernot

ICML 2021 Label-Only Membership Inference Attacks Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, Nicolas Papernot

NeurIPS 2021 Manipulating SGD with Data Ordering Attacks I Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A Erdogdu, Ross J Anderson

ICML 2021 Markpainting: Adversarial Machine Learning Meets Inpainting David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross Anderson

AAAI 2021 Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson

ICML 2020 Fundamental Tradeoffs Between Invariance and Sensitivity to Adversarial Perturbations Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Joern-Henrik Jacobsen

ICLR 2020 Thieves on Sesame Street! Model Extraction of BERT-Based APIs Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer

ICML 2019 Analyzing and Improving Representations with the Soft Nearest Neighbor Loss Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton

NeurIPS 2019 MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin A Raffel

NeurIPS 2018 Adversarial Examples That Fool Both Computer Vision and Time-Limited Humans Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein

ICLR 2018 Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

ICLR 2018 Scalable Private Learning with PATE Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar Erlingsson

ICLR 2017 Adversarial Attacks on Neural Network Policies Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel

ICLR 2017 Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar