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 (MTD@CCS, 2014)

Enforcing agile access control policies in relational databases using views. - Nicolas Papernot; Patrick D. McDaniel; Robert J. Walls (MILCOM, 2015)

The Limitations of Deep Learning in Adversarial Settings. - Nicolas Papernot; Patrick D. McDaniel; Somesh Jha; Matt Fredrikson; Z. Berkay Celik; Ananthram Swami (EuroS&P, 2016)

Mapping sample scenarios to operational models. - 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 (MILCOM, 2016)

Crafting adversarial input sequences for recurrent neural networks. - Nicolas Papernot; Patrick D. McDaniel; Ananthram Swami; Richard E. Harang (MILCOM, 2016)

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. - Nicolas Papernot; Patrick D. McDaniel; Xi Wu 001; Somesh Jha; Ananthram Swami (IEEE Symposium on Security and Privacy, 2016)

Building Better Detection with Privileged Information. - Z. Berkay Celik; Patrick D. McDaniel; Rauf Izmailov; Nicolas Papernot; Ananthram Swami (CoRR, 2016)

cleverhans v0.1: an adversarial machine learning library. - Ian J. Goodfellow; Nicolas Papernot; Patrick D. McDaniel (CoRR, 2016)

Adversarial Perturbations Against Deep Neural Networks for Malware Classification. - Kathrin Grosse; Nicolas Papernot; Praveen Manoharan 001; Michael Backes 001; Patrick D. McDaniel (CoRR, 2016)

On the Effectiveness of Defensive Distillation. - Nicolas Papernot; Patrick D. McDaniel (CoRR, 2016)

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. - Nicolas Papernot; Patrick D. McDaniel; Ian J. Goodfellow (CoRR, 2016)

Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. - Nicolas Papernot; Patrick D. McDaniel; Ian J. Goodfellow; Somesh Jha; Z. Berkay Celik; Ananthram Swami (CoRR, 2016)

Towards the Science of Security and Privacy in Machine Learning. - Nicolas Papernot; Patrick D. McDaniel; Arunesh Sinha; Michael P. Wellman (CoRR, 2016)

Machine Learning in Adversarial Settings. - Patrick D. McDaniel; Nicolas Papernot; Z. Berkay Celik (IEEE Secur. Priv., 2016)

Practical Black-Box Attacks against Machine Learning. - Nicolas Papernot; Patrick D. McDaniel; Ian J. Goodfellow; Somesh Jha; Z. Berkay Celik; Ananthram Swami (AsiaCCS, 2017)

On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. - Martín Abadi; Úlfar Erlingsson; Ian J. Goodfellow; H. Brendan McMahan; Ilya Mironov; Nicolas Papernot; Kunal Talwar; Li Zhang 001 (CSF, 2017)

Adversarial Examples for Malware Detection. - Kathrin Grosse; Nicolas Papernot; Praveen Manoharan 001; Michael Backes 001; Patrick D. McDaniel (ESORICS (2), 2017)

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

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

On the (Statistical) Detection of Adversarial Examples. - Kathrin Grosse; Praveen Manoharan 001; Nicolas Papernot; Michael Backes 001; Patrick D. McDaniel (CoRR, 2017)

Extending Defensive Distillation. - Nicolas Papernot; Patrick D. McDaniel (CoRR, 2017)

Ensemble Adversarial Training: Attacks and Defenses. - Florian Tramèr; Alexey Kurakin; Nicolas Papernot; Dan Boneh; Patrick D. McDaniel (CoRR, 2017)

The Space of Transferable Adversarial Examples. - Florian Tramèr; Nicolas Papernot; Ian J. Goodfellow; Dan Boneh; Patrick D. McDaniel (CoRR, 2017)

On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. - Martín Abadi; Úlfar Erlingsson; Ian J. Goodfellow; H. Brendan McMahan; Ilya Mironov; Nicolas Papernot; Kunal Talwar; Li Zhang 001 (CoRR, 2017)

Detection under Privileged Information. - Z. Berkay Celik; Patrick D. McDaniel; Rauf Izmailov; Nicolas Papernot; Ryan Sheatsley; Raquel Alvarez; Ananthram Swami (AsiaCCS, 2018)

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 (AISec@CCS, 2018)

SoK: Security and Privacy in Machine Learning. - Nicolas Papernot; Patrick D. McDaniel; Arunesh Sinha; Michael P. Wellman (EuroS&P, 2018)

Scalable Private Learning with PATE. - Nicolas Papernot; Shuang Song 001; Ilya Mironov; Ananth Raghunathan; Kunal Talwar; Úlfar Erlingsson (ICLR, 2018)

Ensemble Adversarial Training: Attacks and Defenses. - Florian Tramèr; Alexey Kurakin; Nicolas Papernot; Ian J. Goodfellow; Dan Boneh; Patrick D. McDaniel (ICLR (Poster), 2018)

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. - Gamaleldin F. Elsayed; Shreya Shankar; Brian Cheung; Nicolas Papernot; Alexey Kurakin; Ian J. Goodfellow; Jascha Sohl-Dickstein (NeurIPS, 2018)

Making machine learning robust against adversarial inputs. - Ian J. Goodfellow; Patrick D. McDaniel; Nicolas Papernot (Commun. ACM, 2018)

Adversarial Examples that Fool both Human and Computer Vision. - Gamaleldin F. Elsayed; Shreya Shankar; Brian Cheung; Nicolas Papernot; Alex Kurakin; Ian J. Goodfellow; Jascha Sohl-Dickstein (CoRR, 2018)

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. - Nicolas Papernot; Patrick D. McDaniel (CoRR, 2018)

Adversarial Vision Challenge. - Wieland Brendel; Jonas Rauber; Alexey Kurakin; Nicolas Papernot; Behar Veliqi; Marcel Salathé; Sharada P. Mohanty; Matthias Bethge (CoRR, 2018)

A Marauder's Map of Security and Privacy in Machine Learning. - Nicolas Papernot (CoRR, 2018)

Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. - Nicholas Frosst; Nicolas Papernot; Geoffrey E. Hinton (ICML, 2019)

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

On Evaluating Adversarial Robustness. - Nicholas Carlini; Anish Athalye; Nicolas Papernot; Wieland Brendel; Jonas Rauber; Dimitris Tsipras; Ian J. Goodfellow; Aleksander Madry; Alexey Kurakin (CoRR, 2019)

Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. - Jörn-Henrik Jacobsen; Jens Behrmann; Nicholas Carlini; Florian Tramèr; Nicolas Papernot (CoRR, 2019)

High-Fidelity Extraction of Neural Network Models. - Matthew Jagielski; Nicholas Carlini; David Berthelot; Alex Kurakin; Nicolas Papernot (CoRR, 2019)

Improving Differentially Private Models with Active Learning. - Zhengli Zhao; Nicolas Papernot; Sameer Singh 001; Neoklis Polyzotis; Augustus Odena (CoRR, 2019)

Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications. - Nicholas Carlini; Úlfar Erlingsson; Nicolas Papernot (CoRR, 2019)

How Relevant Is the Turing Test in the Age of Sophisbots? - Dan Boneh; Andrew J. Grotto; Patrick D. McDaniel; Nicolas Papernot (IEEE Secur. Priv., 2019)

Third International Workshop on Dependable and Secure Machine Learning - DSML 2020. - Homa Alemzadeh; Rakesh Bobba; Varun Chandrasekaran; David E. Evans 001; Nicolas Papernot; Karthik Pattabiraman; Florian Tramèr (DSN Workshops, 2020)

Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs. - Andrew Boutros; Mathew Hall; Nicolas Papernot; Vaughn Betz (FPT, 2020)

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

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. - Florian Tramèr; Jens Behrmann; Nicholas Carlini; Nicolas Papernot; Jörn-Henrik Jacobsen (ICML, 2020)

On the Robustness of Cooperative Multi-Agent Reinforcement Learning. - Jieyu Lin; Kristina Dzeparoska; Sai Qian Zhang; Alberto Leon-Garcia; Nicolas Papernot (SP (Workshops), 2020)

High Accuracy and High Fidelity Extraction of Neural Networks. - Matthew Jagielski; Nicholas Carlini; David Berthelot; Alex Kurakin; Nicolas Papernot (USENIX Security Symposium, 2020)

On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping. - Sanghyun Hong 001; Varun Chandrasekaran; Yigitcan Kaya; Tudor Dumitras; Nicolas Papernot (CoRR, 2020)

Entangled Watermarks as a Defense against Model Extraction. - Hengrui Jia 001; Christopher A. Choquette-Choo; Nicolas Papernot (CoRR, 2020)

Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media. - Baiwu Zhang; Jin Peng Zhou; Ilia Shumailov; Nicolas Papernot (CoRR, 2020)

Adversarial Examples in Constrained Domains. - Ryan Sheatsley; Nicolas Papernot; Michael J. Weisman; Gunjan Verma; Patrick D. McDaniel (CoRR, 2020)

Tempered Sigmoid Activations for Deep Learning with Differential Privacy. - Nicolas Papernot; Abhradeep Thakurta; Shuang Song 001; Steve Chien; Úlfar Erlingsson (AAAI, 2021)

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

Fourth International Workshop on Dependable and Secure Machine Learning - DSML 2021. - Hui Xu 009; Guanpeng Li; Homa Alemzadeh; Rakesh Bobba; Varun Chandrasekaran; David E. Evans 001; Nicolas Papernot; Karthik Pattabiraman; Florian Tramèr (DSN Workshops, 2021)

Sponge Examples: Energy-Latency Attacks on Neural Networks. - Ilia Shumailov; Yiren Zhao; Daniel Bates; Nicolas Papernot; Robert D. Mullins; Ross Anderson 001 (EuroS&P, 2021)

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. - Vinith M. Suriyakumar; Nicolas Papernot; Anna Goldenberg; Marzyeh Ghassemi (FAccT, 2021)

CaPC Learning: Confidential and Private Collaborative Learning. - Christopher A. Choquette-Choo; Natalie Dullerud; Adam Dziedzic; Yunxiang Zhang 003; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (ICLR, 2021)

Dataset Inference: Ownership Resolution in Machine Learning. - Pratyush Maini; Mohammad Yaghini; Nicolas Papernot (ICLR, 2021)

Label-Only Membership Inference Attacks. - Christopher A. Choquette-Choo; Florian Tramèr; Nicholas Carlini; Nicolas Papernot (ICML, 2021)

Markpainting: Adversarial Machine Learning meets Inpainting. - David Khachaturov; Ilia Shumailov; Yiren Zhao; Nicolas Papernot; Ross J. Anderson (ICML, 2021)

Accelerating Symbolic Analysis for Android Apps. - Mingyue Yang; David Lie; Nicolas Papernot (ASE Workshops, 2021)

Manipulating SGD with Data Ordering Attacks. - Ilia Shumailov; Zakhar Shumaylov; Dmitry Kazhdan; Yiren Zhao; Nicolas Papernot; Murat A. Erdogdu; Ross J. Anderson (NeurIPS, 2021)

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 (SP, 2021)

Machine Unlearning. - Lucas Bourtoule; Varun Chandrasekaran; Christopher A. Choquette-Choo; Hengrui Jia 001; Adelin Travers; Baiwu Zhang; David Lie; Nicolas Papernot (SP, 2021)

Proof-of-Learning: Definitions and Practice. - Hengrui Jia 001; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Anvith Thudi; Varun Chandrasekaran; Nicolas Papernot (SP, 2021)

Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. - Milad Nasr; Shuang Song 001; Abhradeep Thakurta; Nicolas Papernot; Nicholas Carlini (SP, 2021)

Entangled Watermarks as a Defense against Model Extraction. - Hengrui Jia 001; Christopher A. Choquette-Choo; Varun Chandrasekaran; Nicolas Papernot (USENIX Security Symposium, 2021)

On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. - Adelin Travers; Lorna Licollari; Guanghan Wang; Varun Chandrasekaran; Adam Dziedzic; David Lie; Nicolas Papernot (CoRR, 2021)

SoK: Machine Learning Governance. - Varun Chandrasekaran; Hengrui Jia 001; Anvith Thudi; Adelin Travers; Mohammad Yaghini; Nicolas Papernot (CoRR, 2021)

Interpretability in Safety-Critical FinancialTrading Systems. - Gabriel Deza; Adelin Travers; Colin Rowat; Nicolas Papernot (CoRR, 2021)

The Role of Randomization in Trustworthy Machine Learning. - Nicolas Papernot (MTD@CCS, 2022)

Unrolling SGD: Understanding Factors Influencing Machine Unlearning. - Anvith Thudi; Gabriel Deza; Varun Chandrasekaran; Nicolas Papernot (EuroS&P, 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 (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)

A Zest of LIME: Towards Architecture-Independent Model Distances. - Hengrui Jia 001; Hongyu Chen; Jonas Guan; Ali Shahin Shamsabadi; Nicolas Papernot (ICLR, 2022)

Hyperparameter Tuning with Renyi Differential Privacy. - Nicolas Papernot; Thomas Steinke 002 (ICLR, 2022)

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

On the Limitations of Stochastic Pre-processing Defenses. - Yue Gao 011; Ilia 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 Tramèr (NeurIPS, 2022)

Dataset Inference for Self-Supervised Models. - Adam Dziedzic; Haonan Duan 002; Muhammad Ahmad Kaleem; Nikita Dhawan; Jonas Guan; Yannis Cattan; Franziska Boenisch; Nicolas Papernot (NeurIPS, 2022)

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

In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning. - Jiaqi Wang; Roei Schuster; Ilia Shumailov; David Lie; Nicolas Papernot (NeurIPS, 2022)

Bad Characters: Imperceptible NLP Attacks. - Nicholas Boucher; Ilia Shumailov; Ross Anderson 001; Nicolas Papernot (SP, 2022)

Towards More Robust Keyword Spotting for Voice Assistants. - Shimaa Ahmed; Ilia Shumailov; Nicolas Papernot; Kassem Fawaz (USENIX Security Symposium, 2022)

On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. - Anvith Thudi; Hengrui Jia 001; Ilia Shumailov; Nicolas Papernot (USENIX Security Symposium, 2022)

Pipe Overflow: Smashing Voice Authentication for Fun and Profit. - Shimaa Ahmed; Yash Wani; Ali Shahin Shamsabadi; Mohammad Yaghini; Ilia Shumailov; Nicolas Papernot; Kassem Fawaz (CoRR, 2022)

Bounding Membership Inference. - Anvith Thudi; Ilia Shumailov; Franziska Boenisch; Nicolas Papernot (CoRR, 2022)

Selective Classification Via Neural Network Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Nicolas Papernot (CoRR, 2022)

Intrinsic Anomaly Detection for Multi-Variate Time Series. - Stephan Rabanser; Tim Januschowski; Kashif Rasul; Oliver Borchert; Richard Kurle; Jan Gasthaus; Michael Bohlke-Schneider; Nicolas Papernot; Valentin Flunkert (CoRR, 2022)

Efficient Adversarial Training With Data Pruning. - Maximilian Kaufmann; Yiren Zhao; Ilia Shumailov; Robert D. Mullins; Nicolas Papernot (CoRR, 2022)

p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. - Adam Dziedzic; Stephan Rabanser; Mohammad Yaghini; Armin Ale; Murat A. Erdogdu; Nicolas Papernot (CoRR, 2022)

Generative Extraction of Audio Classifiers for Speaker Identification. - Tejumade Afonja; Lucas Bourtoule; Varun Chandrasekaran; Sageev Oore; Nicolas Papernot (CoRR, 2022)

On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (CoRR, 2022)

In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning. - Jiaqi Wang; Roei Schuster; Ilia Shumailov; David Lie; Nicolas Papernot (CoRR, 2022)

Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search. - Yannis Cattan; Christopher A. Choquette-Choo; Nicolas Papernot; Abhradeep Thakurta (CoRR, 2022)

Learned Systems Security. - Roei Schuster; Jin Peng Zhou; Thorsten Eisenhofer; Paul Grubbs; Nicolas Papernot (CoRR, 2022)

Adversarial examples for network intrusion detection systems. - Ryan Sheatsley; Nicolas Papernot; Michael J. Weisman; Gunjan Verma; Patrick D. McDaniel (J. Comput. Secur., 2022)

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 (AISec@CCS, 2023)

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

When the Curious Abandon Honesty: Federated Learning Is Not Private. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Proof-of-Learning is Currently More Broken Than You Think. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (EuroS&P, 2023)

Measuring Forgetting of Memorized Training Examples. - Matthew Jagielski; Om Thakkar 001; Florian Tramèr; Daphne Ippolito; Katherine Lee; Nicholas Carlini; Eric Wallace; Shuang Song 001; Abhradeep Guha Thakurta; Nicolas Papernot; Chiyuan Zhang (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 012; Adrian Weller (ICLR, 2023)

Have it your way: Individualized Privacy Assignment for DP-SGD. - Franziska Boenisch; Christopher Mühl; Adam Dziedzic; Roy Rinberg; Nicolas Papernot (NeurIPS, 2023)

Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. - Haonan Duan 002; Adam Dziedzic; Nicolas Papernot; Franziska Boenisch (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 012 (NeurIPS, 2023)

Training Private Models That Know What They Don't Know. - Stephan Rabanser; Anvith Thudi; Abhradeep Guha Thakurta; Krishnamurthy Dvijotham; Nicolas Papernot (NeurIPS, 2023)

Tubes Among Us: Analog Attack on Automatic Speaker Identification. - Shimaa Ahmed; Yash Wani; Ali Shahin Shamsabadi; Mohammad Yaghini; Ilia Shumailov; Nicolas Papernot; Kassem Fawaz (USENIX Security Symposium, 2023)

Is Federated Learning a Practical PET Yet? - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (CoRR, 2023)

Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility. - Mohammad Yaghini; Patty Liu; Franziska Boenisch; Nicolas Papernot (CoRR, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

The Curse of Recursion: Training on Generated Data Makes Models Forget. - Ilia Shumailov; Zakhar Shumaylov; Yiren Zhao; Yarin Gal; Nicolas Papernot; Ross J. Anderson (CoRR, 2023)

When Vision Fails: Text Attacks Against ViT and OCR. - Nicholas Boucher; Jenny Blessing; Ilia Shumailov; Ross Anderson 001; Nicolas Papernot (CoRR, 2023)

LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? - David Glukhov; Ilia Shumailov; Yarin Gal; Nicolas Papernot; Vardan Papyan (CoRR, 2023)

Robust and Actively Secure Serverless Collaborative Learning. - Olive Franzese; Adam Dziedzic; Christopher A. Choquette-Choo; Mark R. Thomas; Muhammad Ahmad Kaleem; Stephan Rabanser; Congyu Fang; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (CoRR, 2023)

Private Multi-Winner Voting for Machine Learning. - Adam Dziedzic; Christopher A. Choquette-Choo; Natalie Dullerud; Vinith M. Suriyakumar; Ali Shahin Shamsabadi; Muhammad Ahmad Kaleem; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (Proc. Priv. Enhancing Technol., 2023)

Differentially Private Speaker Anonymization. - Ali Shahin Shamsabadi; Brij Mohan Lal Srivastava; Aurélien Bellet; Nathalie Vauquier; Emmanuel Vincent 001; Mohamed Maouche; Marc Tommasi; Nicolas Papernot (Proc. Priv. Enhancing Technol., 2023)

Losing Less: A Loss for Differentially Private Deep Learning. - Ali Shahin Shamsabadi; Nicolas Papernot (Proc. Priv. Enhancing Technol., 2023)

Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias. - Sierra Calanda Wyllie; Ilia Shumailov; Nicolas Papernot (FAccT, 2024)

Confidential-DPproof: Confidential Proof of Differentially Private Training. - Ali Shahin Shamsabadi; Gefei Tan; Tudor Cebere; Aurélien Bellet; Hamed Haddadi; Nicolas Papernot; Xiao Wang 012; Adrian Weller (ICLR, 2024)

Memorization in Self-Supervised Learning Improves Downstream Generalization. - Wenhao Wang; Muhammad Ahmad Kaleem; Adam Dziedzic; Michael Backes 001; Nicolas Papernot; Franziska Boenisch (ICLR, 2024)

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

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

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

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

LLM Dataset Inference: Did you train on my dataset? - Pratyush Maini; Hengrui Jia 001; Nicolas Papernot; Adam Dziedzic (NeurIPS, 2024)

Privacy-Preserving Federated Learning for Coverage Prediction. - Congyu Fang; Akram Bin Sediq; Hamza Umit Sokun; Israfil Bahceci; A Ahmed Ibrahim; Nicolas Papernot (PIMRC, 2024)

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. - Anvith Thudi; Hengrui Jia 001; Casey Meehan; Ilia Shumailov; Nicolas Papernot (USENIX Security Symposium, 2024)

Exploring Strategies for Guiding Symbolic Analysis with Machine Learning Prediction. - Mingyue Yang; David Lie; Nicolas Papernot (SANER, 2024)

Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data. - Congyu Fang; Adam Dziedzic; Lin Zhang; Laura Oliva; Amol Verma; Fahad Razak; Nicolas Papernot; Bo Wang (CoRR, 2024)

Unlearnable Algorithms for In-context Learning. - Andrei Ioan Muresanu; Anvith Thudi; Michael R. Zhang; Nicolas Papernot (CoRR, 2024)

Regulation Games for Trustworthy Machine Learning. - Mohammad Yaghini; Patty Liu; Franziska Boenisch; Nicolas Papernot (CoRR, 2024)

UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI. - Ilia Shumailov; Jamie Hayes; Eleni Triantafillou; Guillermo Ortiz-Jiménez; Nicolas Papernot; Matthew Jagielski; Itay Yona; Heidi Howard; Eugene Bagdasaryan (CoRR, 2024)

A False Sense of Safety: Unsafe Information Leakage in 'Safe' AI Responses. - David Glukhov; Ziwen Han; Ilia Shumailov; Vardan Papyan; Nicolas Papernot (CoRR, 2024)

On the Privacy Risk of In-context Learning. - Haonan Duan 002; Adam Dziedzic; Mohammad Yaghini; Nicolas Papernot; Franziska Boenisch (CoRR, 2024)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. - 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; et al. (CoRR, 2024)

Beyond Labeling Oracles - What does it mean to steal ML models? - Avital Shafran; Ilia Shumailov; Murat A. Erdogdu; Nicolas Papernot (Trans. Mach. Learn. Res., 2024)

From Differential Privacy to Bounds on Membership Inference: Less can be More. - Anvith Thudi; Ilia Shumailov; Franziska Boenisch; Nicolas Papernot (Trans. Mach. Learn. Res., 2024)

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. - Jiapeng Wu; Atiyeh Ashari Ghomi; David Glukhov; Jesse C. Cresswell; Franziska Boenisch; Nicolas Papernot (Trans. Mach. Learn. Res., 2024)

AI models collapse when trained on recursively generated data. - Ilia Shumailov; Zakhar Shumaylov; Yiren Zhao; Nicolas Papernot; Ross J. Anderson; Yarin Gal (Nat., 2024)

Trustworthy ML Regulation as a Principal-Agent Problem. - Mohammad Yaghini; Patty Liu; Andrew Magnuson; Natalie Dullerud; Nicolas Papernot (FAccT, 2025)

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

Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses. - David Glukhov; Ziwen Han; Ilia Shumailov; Vardan Papyan; Nicolas Papernot (ICLR, 2025)

Verifiable and Provably Secure Machine Unlearning. - Thorsten Eisenhofer; Doreen Riepel; Varun Chandrasekaran; Esha Ghosh; Olga Ohrimenko; Nicolas Papernot (SaTML, 2025)

Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy. - Jamie Hayes; Ilia Shumailov; Eleni Triantafillou; Amr Khalifa; Nicolas Papernot (SaTML, 2025)

Backdoor Detection Through Replicated Execution of Outsourced Training. - Hengrui Jia 001; Sierra Calanda Wyllie; Akram Bin Sediq; Ahmed Ibrahim; Nicolas Papernot (SaTML, 2025)

Architectural Neural Backdoors from First Principles. - Harry Langford; Ilia Shumailov; Yiren Zhao; Robert D. Mullins; Nicolas Papernot (SP, 2025)

Secure Noise Sampling for Differentially Private Collaborative Learning. - Olive Franzese; Congyu Fang; Radhika Garg 002; Somesh Jha; Nicolas Papernot; Xiao Wang 012; Adam Dziedzic (IACR Cryptol. ePrint Arch., 2025)

Selective Prediction via Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Israfil Bahceci; Akram Bin Sediq; Hamza Umit Sokun; Nicolas Papernot (Trans. Mach. Learn. Res., 2025)

Learned-Database Systems Security. - Roei Schuster; Jin Peng Zhou; Thorsten Eisenhofer; Paul Grubbs; Nicolas Papernot (Trans. Mach. Learn. Res., 2025)

Societal Alignment Frameworks Can Improve LLM Alignment. - Karolina Stanczak; Nicholas Meade; Mehar Bhatia; Hattie Zhou; Konstantin Böttinger; Jeremy Barnes 002; Jason Stanley; Jessica Montgomery; Richard S. Zemel; Nicolas Papernot; Nicolas Chapados; Denis Therien; et al. (CoRR, 2025)

Language Models May Verbatim Complete Text They Were Not Explicitly Trained On. - Ken Ziyu Liu; Christopher A. Choquette-Choo; Matthew Jagielski; Peter Kairouz; Sanmi Koyejo; Percy Liang; Nicolas Papernot (CoRR, 2025)

Prεεmpt: Sanitizing Sensitive Prompts for LLMs. - Amrita Roy Chowdhury 001; David Glukhov; Divyam Anshumaan; Prasad Chalasani; Nicolas Papernot; Somesh Jha; Mihir Bellare (CoRR, 2025)

Leveraging Per-Instance Privacy for Machine Unlearning. - Nazanin Mohammadi Sepahvand; Anvith Thudi; Berivan Isik; Ashmita Bhattacharyya; Nicolas Papernot; Eleni Triantafillou; Daniel M. Roy 001; Gintare Karolina Dziugaite (CoRR, 2025)

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

Private Rate-Constrained Optimization with Applications to Fair Learning. - Mohammad Yaghini; Tudor Cebere; Michael Menart; Aurélien Bellet; Nicolas Papernot (CoRR, 2025)

Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. - Stephan Rabanser; Ali Shahin Shamsabadi; Olive Franzese; Xiao Wang 012; Adrian Weller; Nicolas Papernot (CoRR, 2025)

Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning. - Roy Rinberg; Ilia Shumailov; Vikrant Singhal; Rachel Cummings; Nicolas Papernot (CoRR, 2025)