Nicolas Papernot

231 papers A* 57A 11B 1Journal 138Unranked 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents. CoRR Hanna Foerster, Robert Mullins, Tom Blanchard, Nicolas Papernot, Kristina Nikolic, Florian Tramèr, Ilia Shumailov, Cheng Zhang, Yiren Zhao
2026	J	`jnl`	Gauss-Newton Unlearning for the LLM Era. CoRR Lev McKinney, Anvith Thudi, Juhan Bae, Tara Rezaei, Nicolas Papernot, Sheila A. McIlraith, Roger B. Grosse
2025	A*	`conf`	Architectural Neural Backdoors from First Principles. SP Harry Langford, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot
2025	—	`conf`	Backdoor Detection Through Replicated Execution of Outsourced Training. SaTML Hengrui Jia, Sierra Calanda Wyllie, Akram Bin Sediq, Ahmed Ibrahim, Nicolas Papernot
2025	J	`jnl`	Backdoor Detection through Replicated Execution of Outsourced Training. CoRR Hengrui Jia, Sierra Calanda Wyllie, Akram Bin Sediq, Ahmed Ibrahim, Nicolas Papernot
2025	J	`jnl`	Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning. CoRR Roy Rinberg, Ilia Shumailov, Vikrant Singhal, Rachel Cummings, Nicolas Papernot
2025	A*	`conf`	Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses. ICLR David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, Nicolas Papernot
2025	A*	`conf`	Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. ICML Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot
2025	J	`jnl`	Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. CoRR Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot
2025	J	`jnl`	Efficient Public Verification of Private ML via Regularization. CoRR Zoë Ruha Bell, Anvith Thudi, Olive Franzese-McLaughlin, Nicolas Papernot, Shafi Goldwasser
2025	A*	`conf`	Fast Exact Unlearning for In-Context Learning Data for LLMs. ICML Andrei Ioan Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot
2025	—	`conf`	Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy. SaTML Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, Nicolas Papernot
2025	A*	`conf`	Language Models May Verbatim Complete Text They Were Not Explicitly Trained On. ICML Ken Liu, Christopher A. Choquette-Choo, Matthew Jagielski, Peter Kairouz, Sanmi Koyejo, Percy Liang, Nicolas Papernot
2025	J	`jnl`	Language Models May Verbatim Complete Text They Were Not Explicitly Trained On. CoRR Ken Ziyu Liu, Christopher A. Choquette-Choo, Matthew Jagielski, Peter Kairouz, Sanmi Koyejo, Percy Liang, Nicolas Papernot
2025	J	`jnl`	Learned-Database Systems Security. Trans. Mach. Learn. Res. Roei Schuster, Jin Peng Zhou, Thorsten Eisenhofer, Paul Grubbs, Nicolas Papernot
2025	A*	`conf`	Leveraging Per-Instance Privacy for Machine Unlearning. ICML Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite
2025	J	`jnl`	Leveraging Per-Instance Privacy for Machine Unlearning. CoRR Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite
2025	J	`jnl`	Private Rate-Constrained Optimization with Applications to Fair Learning. CoRR Mohammad Yaghini, Tudor Cebere, Michael Menart, Aurélien Bellet, Nicolas Papernot
2025	J	`jnl`	Prεεmpt: Sanitizing Sensitive Prompts for LLMs. CoRR Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan, Prasad Chalasani, Nicolas Papernot, Somesh Jha, Mihir Bellare
2025	A*	`conf`	Secure Noise Sampling for Differentially Private Collaborative Learning. CCS Olive Franzese, Congyu Fang, Radhika Garg, Xiao Wang, Somesh Jha, Nicolas Papernot, Adam Dziedzic
2025	J	`jnl`	Secure Noise Sampling for Differentially Private Collaborative Learning. IACR Cryptol. ePrint Arch. Olive Franzese, Congyu Fang, Radhika Garg, Somesh Jha, Nicolas Papernot, Xiao Wang, Adam Dziedzic
2025	J	`jnl`	Selective Prediction via Training Dynamics. Trans. Mach. Learn. Res. Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Umit Sokun, Nicolas Papernot
2025	J	`jnl`	Societal Alignment Frameworks Can Improve LLM Alignment. CoRR Karolina Stanczak, Nicholas Meade, Mehar Bhatia, Hattie Zhou, Konstantin Böttinger, Jeremy Barnes, Jason Stanley, Jessica Montgomery, Richard S. Zemel, Nicolas Papernot, Nicolas Chapados, Denis Therien, Timothy P. Lillicrap, Ana Marasovic, Sylvie Delacroix, Gillian K. Hadfield, Siva Reddy
2025	A*	`conf`	Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings. ICML Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot
2025	J	`jnl`	Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings. CoRR Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot
2025	A*	`conf`	Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model. ICLR Tudor Ioan Cebere, Aurélien Bellet, Nicolas Papernot
2025	—	`conf`	Trustworthy ML Regulation as a Principal-Agent Problem. FAccT Mohammad Yaghini, Patty Liu, Andrew Magnuson, Natalie Dullerud, Nicolas Papernot
2025	—	`conf`	Verifiable and Provably Secure Machine Unlearning. SaTML Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran, Esha Ghosh, Olga Ohrimenko, Nicolas Papernot
2025	J	`jnl`	What Does It Take to Build a Performant Selective Classifier? CoRR Stephan Rabanser, Nicolas Papernot
2025	—	`conf`	When Vision Fails: Text Attacks Against ViT and OCR. LAMPS@CCS Nicholas Boucher, Jenny Blessing, Ilia Shumailov, Ross J. Anderson, Nicolas Papernot
2024	J	`jnl`	A False Sense of Safety: Unsafe Information Leakage in 'Safe' AI Responses. CoRR David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, Nicolas Papernot
2024	J	`jnl`	AI models collapse when trained on recursively generated data. Nat. Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson, Yarin Gal
2024	J	`jnl`	Architectural Neural Backdoors from First Principles. CoRR Harry Langford, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot
2024	A*	`conf`	Auditing Private Prediction. ICML Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr
2024	J	`jnl`	Auditing Private Prediction. CoRR Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr
2024	J	`jnl`	Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. Trans. Mach. Learn. Res. Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
2024	J	`jnl`	Beyond Labeling Oracles - What does it mean to steal ML models? Trans. Mach. Learn. Res. Avital Shafran, Ilia Shumailov, Murat A. Erdogdu, Nicolas Papernot
2024	A*	`conf`	Confidential-DPproof: Confidential Proof of Differentially Private Training. ICLR Ali Shahin Shamsabadi, Gefei Tan, Tudor Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller
2024	J	`jnl`	Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data. CoRR Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol Verma, Fahad Razak, Nicolas Papernot, Bo Wang
2024	A	`conf`	Exploring Strategies for Guiding Symbolic Analysis with Machine Learning Prediction. SANER Mingyue Yang, David Lie, Nicolas Papernot
2024	—	`conf`	Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias. FAccT Sierra Calanda Wyllie, Ilia Shumailov, Nicolas Papernot
2024	J	`jnl`	Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias. CoRR Sierra Calanda Wyllie, Ilia Shumailov, Nicolas Papernot
2024	J	`jnl`	From Differential Privacy to Bounds on Membership Inference: Less can be More. Trans. Mach. Learn. Res. Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot
2024	A*	`conf`	Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. USENIX Security Symposium Anvith Thudi, Hengrui Jia, Casey Meehan, Ilia Shumailov, Nicolas Papernot
2024	J	`jnl`	Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy. CoRR Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, Nicolas Papernot
2024	A*	`conf`	LLM Dataset Inference: Did you train on my dataset? NeurIPS Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic
2024	J	`jnl`	LLM Dataset Inference: Did you train on my dataset? CoRR Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic
2024	J	`jnl`	Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. CoRR A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando A. Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna M. Wallach, Nicolas Papernot, Katherine Lee
2024	A*	`conf`	Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
2024	J	`jnl`	Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
2024	J	`jnl`	On the Privacy Risk of In-context Learning. CoRR Haonan Duan, Adam Dziedzic, Mohammad Yaghini, Nicolas Papernot, Franziska Boenisch
2024	A*	`conf`	Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches. ICML David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan
2024	B	`conf`	Privacy-Preserving Federated Learning for Coverage Prediction. PIMRC Congyu Fang, Akram Bin Sediq, Hamza Umit Sokun, Israfil Bahceci, A Ahmed Ibrahim, Nicolas Papernot
2024	J	`jnl`	Regulation Games for Trustworthy Machine Learning. CoRR Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
2024	A*	`conf`	Temporal-Difference Learning Using Distributed Error Signals. NeurIPS Jonas Guan, Shon Eduard Verch, Claas Voelcker, Ethan C. Jackson, Nicolas Papernot, William A. Cunningham
2024	J	`jnl`	Temporal-Difference Learning Using Distributed Error Signals. CoRR Jonas Guan, Shon Eduard Verch, Claas Voelcker, Ethan C. Jackson, Nicolas Papernot, William A. Cunningham
2024	A*	`conf`	The Fundamental Limits of Least-Privilege Learning. ICML Theresa Stadler, Bogdan Kulynych, Michael Gastpar, Nicolas Papernot, Carmela Troncoso
2024	J	`jnl`	The Fundamental Limits of Least-Privilege Learning. CoRR Theresa Stadler, Bogdan Kulynych, Nicolas Papernot, Michael Gastpar, Carmela Troncoso
2024	J	`jnl`	Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model. CoRR Tudor Cebere, Aurélien Bellet, Nicolas Papernot
2024	J	`jnl`	UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI. CoRR Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jiménez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan
2024	J	`jnl`	Unlearnable Algorithms for In-context Learning. CoRR Andrei Ioan Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot
2023	A*	`conf`	Architectural Backdoors in Neural Networks. CVPR Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot
2023	J	`jnl`	Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. CoRR Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
2023	J	`jnl`	Beyond Labeling Oracles: What does it mean to steal ML models? CoRR Avital Shafran, Ilia Shumailov, Murat A. Erdogdu, Nicolas Papernot
2023	J	`jnl`	Challenges towards the Next Frontier in Privacy. CoRR 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
2023	A*	`conf`	Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. ICLR Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller
2023	J	`jnl`	Differentially Private Speaker Anonymization. Proc. Priv. Enhancing Technol. Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot
2023	A*	`conf`	Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
2023	J	`jnl`	Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
2023	J	`jnl`	Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. CoRR Anvith Thudi, Hengrui Jia, Casey Meehan, Ilia Shumailov, Nicolas Papernot
2023	A*	`conf`	Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
2023	J	`jnl`	Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
2023	J	`jnl`	Is Federated Learning a Practical PET Yet? CoRR Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2023	J	`jnl`	LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? CoRR David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan
2023	J	`jnl`	Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility. CoRR Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
2023	J	`jnl`	Losing Less: A Loss for Differentially Private Deep Learning. Proc. Priv. Enhancing Technol. Ali Shahin Shamsabadi, Nicolas Papernot
2023	A*	`conf`	Measuring Forgetting of Memorized Training Examples. ICLR Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang
2023	J	`jnl`	Private Multi-Winner Voting for Machine Learning. Proc. Priv. Enhancing Technol. Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith M. Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	A	`conf`	Proof-of-Learning is Currently More Broken Than You Think. EuroS&P Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot
2023	A	`conf`	Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. EuroS&P Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2023	A*	`conf`	Robust and Actively Secure Serverless Collaborative Learning. NeurIPS Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	J	`jnl`	Robust and Actively Secure Serverless Collaborative Learning. CoRR Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	—	`conf`	The Adversarial Implications of Variable-Time Inference. AISec@CCS Dudi Biton, Aditi Misra, Efrat Levy, Jaidip Kotak, Ron Bitton, Roei Schuster, Nicolas Papernot, Yuval Elovici, Ben Nassi
2023	J	`jnl`	The Adversarial Implications of Variable-Time Inference. CoRR Dudi Biton, Aditi Misra, Efrat Levy, Jaidip Kotak, Ron Bitton, Roei Schuster, Nicolas Papernot, Yuval Elovici, Ben Nassi
2023	J	`jnl`	The Curse of Recursion: Training on Generated Data Makes Models Forget. CoRR Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross J. Anderson
2023	A*	`conf`	Training Private Models That Know What They Don't Know. NeurIPS Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
2023	J	`jnl`	Training Private Models That Know What They Don't Know. CoRR Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
2023	A*	`conf`	Tubes Among Us: Analog Attack on Automatic Speaker Identification. USENIX Security Symposium Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz
2023	J	`jnl`	When Vision Fails: Text Attacks Against ViT and OCR. CoRR Nicholas Boucher, Jenny Blessing, Ilia Shumailov, Ross Anderson, Nicolas Papernot
2023	A	`conf`	When the Curious Abandon Honesty: Federated Learning Is Not Private. EuroS&P Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2022	A*	`conf`	A Zest of LIME: Towards Architecture-Independent Model Distances. ICLR Hengrui Jia, Hongyu Chen, Jonas Guan, Ali Shahin Shamsabadi, Nicolas Papernot
2022	J	`jnl`	Adversarial examples for network intrusion detection systems. J. Comput. Secur. Ryan Sheatsley, Nicolas Papernot, Michael J. Weisman, Gunjan Verma, Patrick D. McDaniel
2022	J	`jnl`	Architectural Backdoors in Neural Networks. CoRR Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot
2022	A*	`conf`	Bad Characters: Imperceptible NLP Attacks. SP Nicholas Boucher, Ilia Shumailov, Ross Anderson, Nicolas Papernot
2022	J	`jnl`	Bounding Membership Inference. CoRR Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot
2022	A*	`conf`	Dataset Inference for Self-Supervised Models. NeurIPS Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
2022	J	`jnl`	Dataset Inference for Self-Supervised Models. CoRR Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
2022	J	`jnl`	Differentially Private Speaker Anonymization. CoRR Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot
2022	J	`jnl`	Efficient Adversarial Training With Data Pruning. CoRR Maximilian Kaufmann, Yiren Zhao, Ilia Shumailov, Robert Mullins, Nicolas Papernot
2022	J	`jnl`	Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search. CoRR Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot, Abhradeep Thakurta
2022	J	`jnl`	Generative Extraction of Audio Classifiers for Speaker Identification. CoRR Tejumade Afonja, Lucas Bourtoule, Varun Chandrasekaran, Sageev Oore, Nicolas Papernot
2022	A*	`conf`	Hyperparameter Tuning with Renyi Differential Privacy. ICLR Nicolas Papernot, Thomas Steinke
2022	J	`jnl`	In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning. CoRR Jiaqi Wang, Roei Schuster, Ilia Shumailov, David Lie, Nicolas Papernot
2022	A*	`conf`	In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning. NeurIPS Jiaqi Wang, Roei Schuster, Ilia Shumailov, David Lie, Nicolas Papernot
2022	A*	`conf`	Increasing the Cost of Model Extraction with Calibrated Proof of Work. ICLR Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
2022	J	`jnl`	Increasing the Cost of Model Extraction with Calibrated Proof of Work. CoRR Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
2022	J	`jnl`	Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
2022	A*	`conf`	Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. ICLR Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi
2022	J	`jnl`	Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. CoRR Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi
2022	J	`jnl`	Learned Systems Security. CoRR Roei Schuster, Jin Peng Zhou, Thorsten Eisenhofer, Paul Grubbs, Nicolas Papernot
2022	J	`jnl`	Measuring Forgetting of Memorized Training Examples. CoRR Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
2022	A*	`conf`	On the Difficulty of Defending Self-Supervised Learning against Model Extraction. ICML Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
2022	J	`jnl`	On the Difficulty of Defending Self-Supervised Learning against Model Extraction. CoRR Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
2022	J	`jnl`	On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. CoRR Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot
2022	A*	`conf`	On the Limitations of Stochastic Pre-processing Defenses. NeurIPS Yue Gao, Ilia Shumailov, Kassem Fawaz, Nicolas Papernot
2022	J	`jnl`	On the Limitations of Stochastic Pre-processing Defenses. CoRR Yue Gao, Ilia Shumailov, Kassem Fawaz, Nicolas Papernot
2022	A*	`conf`	On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. USENIX Security Symposium Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot
2022	J	`jnl`	Pipe Overflow: Smashing Voice Authentication for Fun and Profit. CoRR Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz
2022	J	`jnl`	Private Multi-Winner Voting for Machine Learning. CoRR Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang
2022	J	`jnl`	Selective Classification Via Neural Network Training Dynamics. CoRR Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot
2022	A*	`conf`	The Privacy Onion Effect: Memorization is Relative. NeurIPS Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
2022	J	`jnl`	The Privacy Onion Effect: Memorization is Relative. CoRR Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
2022	—	`conf`	The Role of Randomization in Trustworthy Machine Learning. MTD@CCS Nicolas Papernot
2022	A*	`conf`	Towards More Robust Keyword Spotting for Voice Assistants. USENIX Security Symposium Shimaa Ahmed, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz
2022	A	`conf`	Unrolling SGD: Understanding Factors Influencing Machine Unlearning. EuroS&P Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot
2022	J	`jnl`	Verifiable and Provably Secure Machine Unlearning. CoRR Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran, Esha Ghosh, Olga Ohrimenko, Nicolas Papernot
2022	A*	`conf`	Washing The Unwashable : On The (Im)possibility of Fairwashing Detection. NeurIPS Ali Shahin Shamsabadi, Mohammad Yaghini, Natalie Dullerud, Sierra Calanda Wyllie, Ulrich Aïvodji, Aisha Alaagib, Sébastien Gambs, Nicolas Papernot
2022	J	`jnl`	p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. CoRR Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot
2021	—	`conf`	Accelerating Symbolic Analysis for Android Apps. ASE Workshops Mingyue Yang, David Lie, Nicolas Papernot
2021	A*	`conf`	Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. SP Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
2021	J	`jnl`	Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
2021	J	`jnl`	Bad Characters: Imperceptible NLP Attacks. CoRR Nicholas Boucher, Ilia Shumailov, Ross J. Anderson, Nicolas Papernot
2021	A*	`conf`	CaPC Learning: Confidential and Private Collaborative Learning. ICLR Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
2021	J	`jnl`	CaPC Learning: Confidential and Private Collaborative Learning. CoRR Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
2021	—	`conf`	Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. FAccT Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi
2021	A*	`conf`	Data-Free Model Extraction. CVPR Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, Nicolas Papernot
2021	A*	`conf`	Dataset Inference: Ownership Resolution in Machine Learning. ICLR Pratyush Maini, Mohammad Yaghini, Nicolas Papernot
2021	J	`jnl`	Dataset Inference: Ownership Resolution in Machine Learning. CoRR Pratyush Maini, Mohammad Yaghini, Nicolas Papernot
2021	A*	`conf`	Entangled Watermarks as a Defense against Model Extraction. USENIX Security Symposium Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran, Nicolas Papernot
2021	—	`conf`	Fourth International Workshop on Dependable and Secure Machine Learning - DSML 2021. DSN Workshops Hui Xu, Guanpeng Li, Homa Alemzadeh, Rakesh Bobba, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramèr
2021	J	`jnl`	Hyperparameter Tuning with Renyi Differential Privacy. CoRR Nicolas Papernot, Thomas Steinke
2021	J	`jnl`	Interpretability in Safety-Critical FinancialTrading Systems. CoRR Gabriel Deza, Adelin Travers, Colin Rowat, Nicolas Papernot
2021	A*	`conf`	Label-Only Membership Inference Attacks. ICML Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot
2021	A*	`conf`	Machine Unlearning. SP Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot
2021	A*	`conf`	Manipulating SGD with Data Ordering Attacks. NeurIPS Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson
2021	J	`jnl`	Manipulating SGD with Data Ordering Attacks. CoRR Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson
2021	A*	`conf`	Markpainting: Adversarial Machine Learning meets Inpainting. ICML David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson
2021	J	`jnl`	Markpainting: Adversarial Machine Learning meets Inpainting. CoRR David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson
2021	J	`jnl`	On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. CoRR Adelin Travers, Lorna Licollari, Guanghan Wang, Varun Chandrasekaran, Adam Dziedzic, David Lie, Nicolas Papernot
2021	J	`jnl`	On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. CoRR Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot
2021	A*	`conf`	Proof-of-Learning: Definitions and Practice. SP Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot
2021	J	`jnl`	Proof-of-Learning: Definitions and Practice. CoRR Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot
2021	J	`jnl`	SoK: Machine Learning Governance. CoRR Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, Nicolas Papernot
2021	A*	`conf`	SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems. SP Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, Patrick Traynor
2021	A	`conf`	Sponge Examples: Energy-Latency Attacks on Neural Networks. EuroS&P Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, Ross Anderson
2021	A*	`conf`	Tempered Sigmoid Activations for Deep Learning with Differential Privacy. AAAI Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson
2021	J	`jnl`	Unrolling SGD: Understanding Factors Influencing Machine Unlearning. CoRR Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot
2021	J	`jnl`	When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2020	J	`jnl`	Adversarial Examples in Constrained Domains. CoRR Ryan Sheatsley, Nicolas Papernot, Michael J. Weisman, Gunjan Verma, Patrick D. McDaniel
2020	J	`jnl`	Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. CoRR Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi
2020	J	`jnl`	Data-Free Model Extraction. CoRR Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, Nicolas Papernot
2020	J	`jnl`	Entangled Watermarks as a Defense against Model Extraction. CoRR Hengrui Jia, Christopher A. Choquette-Choo, Nicolas Papernot
2020	A*	`conf`	Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
2020	J	`jnl`	Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. CoRR Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
2020	A*	`conf`	High Accuracy and High Fidelity Extraction of Neural Networks. USENIX Security Symposium Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot
2020	J	`jnl`	Label-Only Membership Inference Attacks. CoRR Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot
2020	—	`conf`	Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs. FPT Andrew Boutros, Mathew Hall, Nicolas Papernot, Vaughn Betz
2020	J	`jnl`	Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs. CoRR Andrew Boutros, Mathew Hall, Nicolas Papernot, Vaughn Betz
2020	J	`jnl`	Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media. CoRR Baiwu Zhang, Jin Peng Zhou, Ilia Shumailov, Nicolas Papernot
2020	J	`jnl`	On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping. CoRR Sanghyun Hong, Varun Chandrasekaran, Yigitcan Kaya, Tudor Dumitras, Nicolas Papernot
2020	—	`conf`	On the Robustness of Cooperative Multi-Agent Reinforcement Learning. SP (Workshops) Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot
2020	J	`jnl`	On the Robustness of Cooperative Multi-Agent Reinforcement Learning. CoRR Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot
2020	J	`jnl`	SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems. CoRR Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, Patrick Traynor
2020	J	`jnl`	Sponge Examples: Energy-Latency Attacks on Neural Networks. CoRR Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, Ross J. Anderson
2020	J	`jnl`	Tempered Sigmoid Activations for Deep Learning with Differential Privacy. CoRR Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson
2020	A*	`conf`	Thieves on Sesame Street! Model Extraction of BERT-based APIs. ICLR Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer
2020	—	`conf`	Third International Workshop on Dependable and Secure Machine Learning - DSML 2020. DSN Workshops Homa Alemzadeh, Rakesh Bobba, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramèr
2019	A*	`conf`	Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. ICML Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton
2019	J	`jnl`	Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. CoRR Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton
2019	J	`jnl`	Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications. CoRR Nicholas Carlini, Úlfar Erlingsson, Nicolas Papernot
2019	J	`jnl`	Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. CoRR Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot
2019	J	`jnl`	High-Fidelity Extraction of Neural Network Models. CoRR Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot
2019	J	`jnl`	How Relevant Is the Turing Test in the Age of Sophisbots? IEEE Secur. Priv. Dan Boneh, Andrew J. Grotto, Patrick D. McDaniel, Nicolas Papernot
2019	J	`jnl`	How Relevant is the Turing Test in the Age of Sophisbots? CoRR Dan Boneh, Andrew J. Grotto, Patrick D. McDaniel, Nicolas Papernot
2019	J	`jnl`	Improving Differentially Private Models with Active Learning. CoRR Zhengli Zhao, Nicolas Papernot, Sameer Singh, Neoklis Polyzotis, Augustus Odena
2019	J	`jnl`	Machine Unlearning. CoRR Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot
2019	A*	`conf`	MixMatch: A Holistic Approach to Semi-Supervised Learning. NeurIPS David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel
2019	J	`jnl`	MixMatch: A Holistic Approach to Semi-Supervised Learning. CoRR David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel
2019	J	`jnl`	On Evaluating Adversarial Robustness. CoRR Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin
2019	J	`jnl`	Thieves on Sesame Street! Model Extraction of BERT-based APIs. CoRR Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer
2018	J	`jnl`	A Marauder's Map of Security and Privacy in Machine Learning. CoRR Nicolas Papernot
2018	—	`conf`	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. AISec@CCS Nicolas Papernot
2018	A*	`conf`	Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. NeurIPS Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein
2018	J	`jnl`	Adversarial Examples that Fool both Human and Computer Vision. CoRR Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein
2018	J	`jnl`	Adversarial Vision Challenge. CoRR Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge
2018	J	`jnl`	Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. CoRR Nicolas Papernot, Patrick D. McDaniel
2018	A	`conf`	Detection under Privileged Information. AsiaCCS Z. Berkay Celik, Patrick D. McDaniel, Rauf Izmailov, Nicolas Papernot, Ryan Sheatsley, Raquel Alvarez, Ananthram Swami
2018	—	`conf`	Ensemble Adversarial Training: Attacks and Defenses. ICLR (Poster) Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel
2018	J	`jnl`	Making machine learning robust against adversarial inputs. Commun. ACM Ian J. Goodfellow, Patrick D. McDaniel, Nicolas Papernot
2018	—	`conf`	Scalable Private Learning with PATE. ICLR Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson
2018	J	`jnl`	Scalable Private Learning with PATE. CoRR Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson
2018	A	`conf`	SoK: Security and Privacy in Machine Learning. EuroS&P Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, Michael P. Wellman
2017	—	`conf`	Adversarial Attacks on Neural Network Policies. ICLR (Workshop) Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel
2017	J	`jnl`	Adversarial Attacks on Neural Network Policies. CoRR Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel
2017	—	`conf`	Adversarial Examples for Malware Detection. ESORICS (2) Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel
2017	J	`jnl`	Ensemble Adversarial Training: Attacks and Defenses. CoRR Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, Patrick D. McDaniel
2017	J	`jnl`	Extending Defensive Distillation. CoRR Nicolas Papernot, Patrick D. McDaniel
2017	J	`jnl`	On the (Statistical) Detection of Adversarial Examples. CoRR Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick D. McDaniel
2017	J	`jnl`	On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CoRR Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
2017	A	`conf`	On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. CSF Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
2017	A	`conf`	Practical Black-Box Attacks against Machine Learning. AsiaCCS Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami
2017	—	`conf`	Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar
2017	J	`jnl`	The Space of Transferable Adversarial Examples. CoRR Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel
2016	J	`jnl`	Adversarial Perturbations Against Deep Neural Networks for Malware Classification. CoRR Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel
2016	J	`jnl`	Building Better Detection with Privileged Information. CoRR Z. Berkay Celik, Patrick D. McDaniel, Rauf Izmailov, Nicolas Papernot, Ananthram Swami
2016	J	`jnl`	Crafting Adversarial Input Sequences for Recurrent Neural Networks. CoRR Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, Richard E. Harang
2016	—	`conf`	Crafting adversarial input sequences for recurrent neural networks. MILCOM Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, Richard E. Harang
2016	A*	`conf`	Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. IEEE Symposium on Security and Privacy Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, Ananthram Swami
2016	J	`jnl`	Machine Learning in Adversarial Settings. IEEE Secur. Priv. Patrick D. McDaniel, Nicolas Papernot, Z. Berkay Celik
2016	—	`conf`	Mapping sample scenarios to operational models. MILCOM Z. Berkay Celik, Nan Hu, Yun Li, Nicolas Papernot, Patrick D. McDaniel, Robert J. Walls, Jeff Rowe, Karl N. Levitt, Novella Bartolini, Thomas F. La Porta, Ritu Chadha
2016	J	`jnl`	On the Effectiveness of Defensive Distillation. CoRR Nicolas Papernot, Patrick D. McDaniel
2016	J	`jnl`	Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. CoRR Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami
2016	J	`jnl`	Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. CoRR Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar
2016	A	`conf`	The Limitations of Deep Learning in Adversarial Settings. EuroS&P Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami
2016	J	`jnl`	Towards the Science of Security and Privacy in Machine Learning. CoRR Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, Michael P. Wellman
2016	J	`jnl`	Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. CoRR Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow
2016	J	`jnl`	cleverhans v0.1: an adversarial machine learning library. CoRR Ian J. Goodfellow, Nicolas Papernot, Patrick D. McDaniel
2015	J	`jnl`	Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks. CoRR Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, Ananthram Swami
2015	—	`conf`	Enforcing agile access control policies in relational databases using views. MILCOM Nicolas Papernot, Patrick D. McDaniel, Robert J. Walls
2015	J	`jnl`	The Limitations of Deep Learning in Adversarial Settings. CoRR Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami
2014	—	`conf`	Security and Science of Agility. MTD@CCS 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