Nicolas Papernot
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
53
Venues
10
Active years
2014–2026
Best venue rank
A*
Where they publish
Papers
53 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | NDSS | Prεεmpt: Sanitizing Sensitive Prompts for LLMs. | Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan, Prasad Chalasani, Nicolas Papernot, Somesh Jha, Mihir Bellare |
| 2025 | CCS | Secure Noise Sampling for Differentially Private Collaborative Learning. | Olive Franzese, Congyu Fang, Radhika Garg, Xiao Wang, Somesh Jha, Nicolas Papernot, Adam Dziedzic |
| 2025 | ICLR | Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model. | Tudor Ioan Cebere, Aurlien Bellet, Nicolas Papernot |
| 2025 | ICLR | Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses. | David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, Nicolas Papernot |
| 2025 | ICML | 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 |
| 2025 | ICML | Fast Exact Unlearning for In-Context Learning Data for LLMs. | Andrei Ioan Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot |
| 2025 | ICML | Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings. | Angline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot |
| 2025 | ICML | Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. | Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot |
| 2025 | ICML | 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 |
| 2025 | SP | Architectural Neural Backdoors from First Principles. | Harry Langford, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot |
| 2024 | ICLR | Confidential-DPproof: Confidential Proof of Differentially Private Training. | Ali Shahin Shamsabadi, Gefei Tan, Tudor Cebere, Aurlien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller |
| 2024 | ICLR | Memorization in Self-Supervised Learning Improves Downstream Generalization. | Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch |
| 2024 | ICML | Auditing Private Prediction. | Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr |
| 2024 | ICML | Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches. | David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan |
| 2024 | ICML | The Fundamental Limits of Least-Privilege Learning. | Theresa Stadler, Bogdan Kulynych, Michael Gastpar, Nicolas Papernot, Carmela Troncoso |
| 2024 | PIMRC | Privacy-Preserving Federated Learning for Coverage Prediction. | Congyu Fang, Akram Bin Sediq, Hamza Umit Sokun, Israfil Bahceci, A Ahmed Ibrahim, Nicolas Papernot |
| 2023 | CCS | The Adversarial Implications of Variable-Time Inference. | Dudi Biton, Aditi Misra, Efrat Levy, Jaidip Kotak, Ron Bitton, Roei Schuster, Nicolas Papernot, Yuval Elovici, Ben Nassi |
| 2023 | CVPR | Architectural Backdoors in Neural Networks. | Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot |
| 2023 | ICLR | Measuring Forgetting of Memorized Training Examples. | Matthew Jagielski, Om Thakkar, Florian Tramr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang |
| 2023 | ICLR | Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. | Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sbastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller |
| 2022 | CCS | The Role of Randomization in Trustworthy Machine Learning. | Nicolas Papernot |
| 2022 | ICLR | Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. | Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi |
| 2022 | ICLR | Increasing the Cost of Model Extraction with Calibrated Proof of Work. | Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot |
| 2022 | ICLR | A Zest of LIME: Towards Architecture-Independent Model Distances. | Hengrui Jia, Hongyu Chen, Jonas Guan, Ali Shahin Shamsabadi, Nicolas Papernot |
| 2022 | ICLR | Hyperparameter Tuning with Renyi Differential Privacy. | Nicolas Papernot, Thomas Steinke |
| 2022 | ICML | On the Difficulty of Defending Self-Supervised Learning against Model Extraction. | Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot |
| 2022 | SP | Bad Characters: Imperceptible NLP Attacks. | Nicholas Boucher, Ilia Shumailov, Ross Anderson, Nicolas Papernot |
| 2021 | AAAI | Tempered Sigmoid Activations for Deep Learning with Differential Privacy. | Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, lfar Erlingsson |
| 2021 | CVPR | Data-Free Model Extraction. | Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, Nicolas Papernot |
| 2021 | DSN | Fourth International Workshop on Dependable and Secure Machine Learning - DSML 2021. | Hui Xu, Guanpeng Li, Homa Alemzadeh, Rakesh Bobba, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramr |
| 2021 | ICLR | CaPC Learning: Confidential and Private Collaborative Learning. | Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang |
| 2021 | ICLR | Dataset Inference: Ownership Resolution in Machine Learning. | Pratyush Maini, Mohammad Yaghini, Nicolas Papernot |
| 2021 | ICML | Label-Only Membership Inference Attacks. | Christopher A. Choquette-Choo, Florian Tramr, Nicholas Carlini, Nicolas Papernot |
| 2021 | ICML | Markpainting: Adversarial Machine Learning meets Inpainting. | David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson |
| 2021 | SP | SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems. | Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, Patrick Traynor |
| 2021 | SP | Machine Unlearning. | Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot |
| 2021 | SP | Proof-of-Learning: Definitions and Practice. | Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot |
| 2021 | SP | Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. | Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini |
| 2020 | DSN | Third International Workshop on Dependable and Secure Machine Learning - DSML 2020. | Homa Alemzadeh, Rakesh Bobba, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramr |
| 2020 | ICLR | Thieves on Sesame Street! Model Extraction of BERT-based APIs. | Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer |
| 2020 | ICML | Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. | Florian Tramr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jrn-Henrik Jacobsen |
| 2020 | SP | On the Robustness of Cooperative Multi-Agent Reinforcement Learning. | Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot |
| 2019 | ICML | Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. | Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton |
| 2018 | CCS | Detection under Privileged Information. | Z. Berkay Celik, Patrick D. McDaniel, Rauf Izmailov, Nicolas Papernot, Ryan Sheatsley, Raquel Alvarez, Ananthram Swami |
| 2018 | CCS | A Marauder's Map of Security and Privacy in Machine Learning: An overview of current and future research directions for making machine learning secure and private. | Nicolas Papernot |
| 2018 | ICLR | Scalable Private Learning with PATE. | Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, lfar Erlingsson |
| 2018 | ICLR | Ensemble Adversarial Training: Attacks and Defenses. | Florian Tramr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel |
| 2017 | CCS | Practical Black-Box Attacks against Machine Learning. | Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami |
| 2017 | ESORICS | Adversarial Examples for Malware Detection. | Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel |
| 2017 | ICLR | Adversarial Attacks on Neural Network Policies. | Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel |
| 2017 | ICLR | Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. | Nicolas Papernot, Martn Abadi, lfar Erlingsson, Ian J. Goodfellow, Kunal Talwar |
| 2016 | SP | Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. | Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, Ananthram Swami |
| 2014 | CCS | Security and Science of Agility. | Patrick D. McDaniel, Trent Jaeger, Thomas F. La Porta, Nicolas Papernot, Robert J. Walls, Alexander Kott, Lisa M. Marvel, Ananthram Swami, Prasant Mohapatra, Srikanth V. Krishnamurthy, Iulian Neamtiu |