Nicolas Papernot

ORCID

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 187

Top Venues

Publications

Theresa Stadler, Bogdan Kulynych, Nicolas Papernot, Michael Gastpar, Carmela Troncoso
The Fundamental Limits of Least-Privilege Learning. CoRR (2024)
Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol A. Verma, Fahad Razak, Nicolas Papernot, Bo Wang
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data. CoRR (2024)
Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, Nicolas Papernot
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy. CoRR (2024)
Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic
LLM Dataset Inference: Did you train on my dataset? CoRR (2024)
Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
Regulation Games for Trustworthy Machine Learning. CoRR (2024)
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. Trans. Mach. Learn. Res. 2024 (2024)
Sierra Calanda Wyllie, Ilia Shumailov, Nicolas Papernot
Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias. FAccT (2024)
David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, Nicolas Papernot
A False Sense of Safety: Unsafe Information Leakage in 'Safe' AI Responses. CoRR (2024)
Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jiménez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI. CoRR (2024)
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot
From Differential Privacy to Bounds on Membership Inference: Less can be More. Trans. Mach. Learn. Res. 2024 (2024)
Ali Shahin Shamsabadi, Gefei Tan, Tudor Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller
Confidential-DPproof: Confidential Proof of Differentially Private Training. ICLR (2024)
Andrei Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot
Unlearnable Algorithms for In-context Learning. CoRR (2024)
Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR (2024)
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson, Yarin Gal
AI models collapse when trained on recursively generated data. Nat. 631 (8022) (2024)
Sierra Calanda Wyllie, Ilia Shumailov, Nicolas Papernot
Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias. CoRR (2024)
Anvith Thudi, Hengrui Jia, Casey Meehan, Ilia Shumailov, Nicolas Papernot
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. USENIX Security Symposium (2024)
Mingyue Yang, David Lie, Nicolas Papernot
Exploring Strategies for Guiding Symbolic Analysis with Machine Learning Prediction. SANER (2024)
Harry Langford, Ilia Shumailov, Yiren Zhao, Robert D. Mullins, Nicolas Papernot
Architectural Neural Backdoors from First Principles. CoRR (2024)
Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR (2024)
Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr
Auditing Private Prediction. CoRR (2024)
Tudor Cebere, Aurélien Bellet, Nicolas Papernot
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model. CoRR (2024)
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. CoRR (2023)
Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS (2023)
Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
Robust and Actively Secure Serverless Collaborative Learning. CoRR (2023)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
When the Curious Abandon Honesty: Federated Learning Is Not Private. EuroS&P (2023)
Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot
Differentially Private Speaker Anonymization. Proc. Priv. Enhancing Technol. 2023 (1) (2023)
Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility. CoRR (2023)
Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR (2023)
Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
Robust and Actively Secure Serverless Collaborative Learning. NeurIPS (2023)
Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith M. Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang
Private Multi-Winner Voting for Machine Learning. Proc. Priv. Enhancing Technol. 2023 (1) (2023)
Dudi Biton, Aditi Misra, Efrat Levy, Jaidip Kotak, Ron Bitton, Roei Schuster, Nicolas Papernot, Yuval Elovici, Ben Nassi
The Adversarial Implications of Variable-Time Inference. AISec@CCS (2023)
Nicholas Boucher, Jenny Blessing, Ilia Shumailov, Ross Anderson, Nicolas Papernot
When Vision Fails: Text Attacks Against ViT and OCR. CoRR (2023)
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR (2023)
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross J. Anderson
The Curse of Recursion: Training on Generated Data Makes Models Forget. CoRR (2023)
Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert D. Mullins, Nicolas Papernot
Architectural Backdoors in Neural Networks. CVPR (2023)
Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller
Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. ICLR (2023)
Anvith Thudi, Hengrui Jia, Casey Meehan, Ilia Shumailov, Nicolas Papernot
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. CoRR (2023)
Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz
Tubes Among Us: Analog Attack on Automatic Speaker Identification. USENIX Security Symposium (2023)
Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot
Proof-of-Learning is Currently More Broken Than You Think. EuroS&P (2023)
Avital Shafran, Ilia Shumailov, Murat A. Erdogdu, Nicolas Papernot
Beyond Labeling Oracles: What does it mean to steal ML models? CoRR (2023)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
Is Federated Learning a Practical PET Yet? CoRR (2023)
David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? CoRR (2023)
Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
Training Private Models That Know What They Don't Know. NeurIPS (2023)
Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
Training Private Models That Know What They Don't Know. CoRR (2023)
Ali Shahin Shamsabadi, Nicolas Papernot
Losing Less: A Loss for Differentially Private Deep Learning. Proc. Priv. Enhancing Technol. 2023 (3) (2023)
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS (2023)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. EuroS&P (2023)
Dudi Biton, Aditi Misra, Efrat Levy, Jaidip Kotak, Ron Bitton, Roei Schuster, Nicolas Papernot, Yuval Elovici, Ben Nassi
The Adversarial Implications of Variable-Time Inference. CoRR (2023)
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
Challenges towards the Next Frontier in Privacy. CoRR (2023)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. ICLR (2023)
Tejumade Afonja, Lucas Bourtoule, Varun Chandrasekaran, Sageev Oore, Nicolas Papernot
Generative Extraction of Audio Classifiers for Speaker Identification. CoRR (2022)
Jiaqi Wang, Roei Schuster, Ilia Shumailov, David Lie, Nicolas Papernot
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning. CoRR (2022)
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot
Bounding Membership Inference. CoRR (2022)
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. CoRR (2022)
Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot
p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. CoRR (2022)
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
The Privacy Onion Effect: Memorization is Relative. NeurIPS (2022)
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 (2022)
Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
Increasing the Cost of Model Extraction with Calibrated Proof of Work. CoRR (2022)
Maximilian Kaufmann, Yiren Zhao, Ilia Shumailov, Robert Mullins, Nicolas Papernot
Efficient Adversarial Training With Data Pruning. CoRR (2022)
Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz
Pipe Overflow: Smashing Voice Authentication for Fun and Profit. CoRR (2022)
Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. ICLR (2022)
Nicolas Papernot, Thomas Steinke
Hyperparameter Tuning with Renyi Differential Privacy. ICLR (2022)
Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot
Differentially Private Speaker Anonymization. CoRR (2022)
Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot
Architectural Backdoors in Neural Networks. CoRR (2022)
Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran, Esha Ghosh, Olga Ohrimenko, Nicolas Papernot
Verifiable and Provably Secure Machine Unlearning. CoRR (2022)
Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. USENIX Security Symposium (2022)
Yue Gao, Ilia Shumailov, Kassem Fawaz, Nicolas Papernot
On the Limitations of Stochastic Pre-processing Defenses. CoRR (2022)
Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. CoRR (2022)
Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot, Abhradeep Thakurta
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search. CoRR (2022)
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot
Unrolling SGD: Understanding Factors Influencing Machine Unlearning. EuroS&P (2022)
Shimaa Ahmed, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz
Towards More Robust Keyword Spotting for Voice Assistants. USENIX Security Symposium (2022)
Yue Gao, Ilia Shumailov, Kassem Fawaz, Nicolas Papernot
On the Limitations of Stochastic Pre-processing Defenses. NeurIPS (2022)
Ali Shahin Shamsabadi, Mohammad Yaghini, Natalie Dullerud, Sierra Wyllie, Ulrich Aïvodji, Aisha Alaagib, Sébastien Gambs, Nicolas Papernot
Washing The Unwashable : On The (Im)possibility of Fairwashing Detection. NeurIPS (2022)
Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang
Private Multi-Winner Voting for Machine Learning. CoRR (2022)
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. ICML (2022)
Nicholas Boucher, Ilia Shumailov, Ross Anderson, Nicolas Papernot
Bad Characters: Imperceptible NLP Attacks. IEEE Symposium on Security and Privacy (2022)
Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot
Selective Classification Via Neural Network Training Dynamics. CoRR (2022)
Roei Schuster, Jin Peng Zhou, Thorsten Eisenhofer, Paul Grubbs, Nicolas Papernot
Learned Systems Security. CoRR (2022)
Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR (2022)
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
Dataset Inference for Self-Supervised Models. NeurIPS (2022)
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
The Privacy Onion Effect: Memorization is Relative. CoRR (2022)
Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot
On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. CoRR (2022)
Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
Increasing the Cost of Model Extraction with Calibrated Proof of Work. ICLR (2022)
Jiaqi Wang, Roei Schuster, Ilia Shumailov, David Lie, Nicolas Papernot
In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning. NeurIPS (2022)
Hengrui Jia, Hongyu Chen, Jonas Guan, Ali Shahin Shamsabadi, Nicolas Papernot
A Zest of LIME: Towards Architecture-Independent Model Distances. ICLR (2022)
Nicolas Papernot
The Role of Randomization in Trustworthy Machine Learning. MTD@CCS (2022)
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
Dataset Inference for Self-Supervised Models. CoRR (2022)
Ryan Sheatsley, Nicolas Papernot, Michael J. Weisman, Gunjan Verma, Patrick D. McDaniel
Adversarial examples for network intrusion detection systems. J. Comput. Secur. 30 (5) (2022)
David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson
Markpainting: Adversarial Machine Learning meets Inpainting. ICML (2021)
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson
Manipulating SGD with Data Ordering Attacks. NeurIPS (2021)
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)
Nicolas Papernot, Thomas Steinke
Hyperparameter Tuning with Renyi Differential Privacy. CoRR (2021)
David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson
Markpainting: Adversarial Machine Learning meets Inpainting. CoRR (2021)
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. IEEE Symposium on Security and Privacy (2021)
Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. CoRR (2021)
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot
Unrolling SGD: Understanding Factors Influencing Machine Unlearning. CoRR (2021)
Adelin Travers, Lorna Licollari, Guanghan Wang, Varun Chandrasekaran, Adam Dziedzic, David Lie, Nicolas Papernot
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. CoRR (2021)
Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot
Label-Only Membership Inference Attacks. ICML (2021)
Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
CaPC Learning: Confidential and Private Collaborative Learning. CoRR (2021)
Nicholas Boucher, Ilia Shumailov, Ross J. Anderson, Nicolas Papernot
Bad Characters: Imperceptible NLP Attacks. CoRR (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Nicolas Papernot