Matthew Jagielski

102 papers A* 31A 1B 1Misc 1Journal 59Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning. CoRR Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
2026	J	`jnl`	Thought-Transfer: Indirect Targeted Poisoning Attacks on Chain-of-Thought Reasoning Models. CoRR Harsh Chaudhari, Ethan Rathbun, Hanna Foerster, Jamie Hayes, Matthew Jagielski, Milad Nasr, Ilia Shumailov, Alina Oprea
2025	A*	`conf`	AISec '25: 18th ACM Workshop on Artificial Intelligence and Security. CCS Maura Pintor, Matthew Jagielski, Ruoxi Jia
2025	J	`jnl`	Black-Box Privacy Attacks on Shared Representations in Multitask Learning. CoRR John Abascal, Nicolás Berrios, Alina Oprea, Jonathan R. Ullman, Adam Smith, Matthew Jagielski
2025	A*	`conf`	Cascading Adversarial Bias from Injection to Distillation in Language Models. CCS Harsh Chaudhari, Jamie Hayes, Matthew Jagielski, Ilia Shumailov, Milad Nasr, Alina Oprea
2025	J	`jnl`	Cascading Adversarial Bias from Injection to Distillation in Language Models. CoRR Harsh Chaudhari, Jamie Hayes, Matthew Jagielski, Ilia Shumailov, Milad Nasr, Alina Oprea
2025	J	`jnl`	Covert Attacks on Machine Learning Training in Passively Secure MPC. CoRR Matthew Jagielski, Daniel Escudero, Rahul Rachuri, Peter Scholl
2025	J	`jnl`	Covert Attacks on Machine Learning Training in Passively Secure MPC. IACR Cryptol. ePrint Arch. Matthew Jagielski, Rahul Rachuri, Daniel Escudero, Peter Scholl
2025	A*	`conf`	Differentially Private Prototypes for Imbalanced Transfer Learning. AAAI Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
2025	A*	`conf`	Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. ICML Yangsibo Huang, Milad Nasr, Anastasios Nikolas Angelopoulos, Nicholas Carlini, Wei-Lin Chiang, Christopher A. Choquette-Choo, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Ken Liu, Ion Stoica, Florian Tramèr, Chiyuan Zhang
2025	J	`jnl`	Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. CoRR Yangsibo Huang, Milad Nasr, Anastasios Angelopoulos, Nicholas Carlini, Wei-Lin Chiang, Christopher A. Choquette-Choo, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Ken Ziyu Liu, Ion Stoica, Florian Tramèr, Chiyuan Zhang
2025	J	`jnl`	Extracting alignment data in open models. CoRR Federico Barbero, Xiangming Gu, Christopher A. Choquette-Choo, Chawin Sitawarin, Matthew Jagielski, Itay Yona, Petar Velickovic, Ilia Shumailov, Jamie Hayes
2025	J	`jnl`	LLMs unlock new paths to monetizing exploits. CoRR Nicholas Carlini, Milad Nasr, Edoardo Debenedetti, Barry Wang, Christopher A. Choquette-Choo, Daphne Ippolito, Florian Tramèr, Matthew Jagielski
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	A*	`conf`	On Evaluating the Durability of Safeguards for Open-Weight LLMs. ICLR Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson
2025	—	`conf`	Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training. ACL (Findings) Jaydeep Borkar, Matthew Jagielski, Katherine Lee, Niloofar Mireshghallah, David A. Smith, Christopher A. Choquette-Choo
2025	J	`jnl`	Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training. CoRR Jaydeep Borkar, Matthew Jagielski, Katherine Lee, Niloofar Mireshghallah, David A. Smith, Christopher A. Choquette-Choo
2025	A*	`conf`	Scalable Extraction of Training Data from Aligned, Production Language Models. ICLR Milad Nasr, Javier Rando, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Florian Tramèr, Katherine Lee
2025	J	`jnl`	SoK: Data Minimization in Machine Learning. CoRR Robin Staab, Nikola Jovanovic, Kimberly Mai, Prakhar Ganesh, Martin T. Vechev, Ferdinando Fioretto, Matthew Jagielski
2025	J	`jnl`	Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. CoRR Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Katherine Lee, Milad Nasr, Sahra Ghalebikesabi, Niloofar Mireshghallah, Meenatchi Sundaram Mutu Selva Annamalai, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Franziska Boenisch, Adam Dziedzic, A. Feder Cooper
2025	A*	`conf`	The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. ICLR Milad Nasr, Thomas Steinke, Borja Balle, Christopher A. Choquette-Choo, Arun Ganesh, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis
2024	A*	`conf`	AISec '24: 17th ACM Workshop on Artificial Intelligence and Security. CCS Maura Pintor, Matthew Jagielski, Xinyun Chen
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`	Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
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	Misc	`conf`	Noise Masking Attacks and Defenses for Pretrained Speech Models. ICASSP Matthew Jagielski, Om Thakkar, Lun Wang
2024	J	`jnl`	Noise Masking Attacks and Defenses for Pretrained Speech Models. CoRR Matthew Jagielski, Om Thakkar, Lun Wang
2024	J	`jnl`	On Evaluating the Durability of Safeguards for Open-Weight LLMs. CoRR Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson
2024	J	`jnl`	Phantom: General Trigger Attacks on Retrieval Augmented Language Generation. CoRR Harsh Chaudhari, Giorgio Severi, John Abascal, Matthew Jagielski, Christopher A. Choquette-Choo, Milad Nasr, Cristina Nita-Rotaru, Alina Oprea
2024	A*	`conf`	Poisoning Web-Scale Training Datasets is Practical. SP Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum S. Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr
2024	A*	`conf`	Privacy Side Channels in Machine Learning Systems. USENIX Security Symposium Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, Florian Tramèr
2024	—	`ed.`	Proceedings of the 2024 Workshop on Artificial Intelligence and Security, AISec 2024, Salt Lake City, UT, USA, October 14-18, 2024 AISec@CCS Maura Pintor, Xinyun Chen, Matthew Jagielski
2024	J	`jnl`	Stealing Part of a Production Language Model. CoRR Nicholas Carlini, Daniel Paleka, Krishnamurthy (Dj) Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr
2024	A*	`conf`	Stealing part of a production language model. ICML Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr
2024	—	`conf`	Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models. NAACL-HLT Aldo G. Carranza, Rezsa Farahani, Natalia Ponomareva, Alexey Kurakin, Matthew Jagielski, Milad Nasr
2024	J	`jnl`	The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. CoRR Thomas Steinke, Milad Nasr, Arun Ganesh, Borja Balle, Christopher A. Choquette-Choo, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis
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
2023	J	`jnl`	A Note On Interpreting Canary Exposure. CoRR Matthew Jagielski
2023	A*	`conf`	Are aligned neural networks adversarially aligned? NeurIPS Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, Ludwig Schmidt
2023	J	`jnl`	Are aligned neural networks adversarially aligned? CoRR Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramèr, Ludwig Schmidt
2023	J	`jnl`	Backdoor Attacks for In-Context Learning with Language Models. CoRR Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
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`	Counterfactual Memorization in Neural Language Models. NeurIPS Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
2023	A*	`conf`	Extracting Training Data from Diffusion Models. USENIX Security Symposium Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
2023	J	`jnl`	Extracting Training Data from Diffusion Models. CoRR Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
2023	J	`jnl`	How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models. Proc. Priv. Enhancing Technol. Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu
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`	Poisoning Web-Scale Training Datasets is Practical. CoRR Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum S. Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr
2023	B	`conf`	Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. INLG Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
2023	A*	`conf`	Privacy Auditing with One (1) Training Run. NeurIPS Thomas Steinke, Milad Nasr, Matthew Jagielski
2023	J	`jnl`	Privacy Auditing with One (1) Training Run. CoRR Thomas Steinke, Milad Nasr, Matthew Jagielski
2023	J	`jnl`	Privacy Side Channels in Machine Learning Systems. CoRR Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, Florian Tramèr
2023	J	`jnl`	Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models. CoRR Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr
2023	A*	`conf`	Quantifying Memorization Across Neural Language Models. ICLR Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang
2023	J	`jnl`	Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators. CoRR Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini
2023	A*	`conf`	SNAP: Efficient Extraction of Private Properties with Poisoning. SP Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
2023	—	`conf`	SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning. SaTML Harsh Chaudhari, Matthew Jagielski, Alina Oprea
2023	J	`jnl`	Scalable Extraction of Training Data from (Production) Language Models. CoRR Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee
2023	A*	`conf`	Students Parrot Their Teachers: Membership Inference on Model Distillation. NeurIPS Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini, Florian Tramèr
2023	J	`jnl`	Students Parrot Their Teachers: Membership Inference on Model Distillation. CoRR Matthew Jagielski, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, Nicholas Carlini
2023	A*	`conf`	Tight Auditing of Differentially Private Machine Learning. USENIX Security Symposium Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
2023	J	`jnl`	Tight Auditing of Differentially Private Machine Learning. CoRR Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
2022	J	`jnl`	Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini
2022	J	`jnl`	How to Combine Membership-Inference Attacks on Multiple Updated Models. CoRR Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu
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	—	`conf`	Network-Level Adversaries in Federated Learning. CNS Giorgio Severi, Matthew Jagielski, Gökberk Yar, Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru
2022	J	`jnl`	Network-Level Adversaries in Federated Learning. CoRR Giorgio Severi, Matthew Jagielski, Gökberk Yar, Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru
2022	J	`jnl`	Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. CoRR Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
2022	J	`jnl`	Quantifying Memorization Across Neural Language Models. CoRR Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang
2022	J	`jnl`	SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
2022	J	`jnl`	SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. CoRR Harsh Chaudhari, Matthew Jagielski, Alina Oprea
2022	J	`jnl`	SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. IACR Cryptol. ePrint Arch. Harsh Chaudhari, Matthew Jagielski, Alina Oprea
2022	—	`conf`	Subverting Fair Image Search with Generative Adversarial Perturbations. FAccT Avijit Ghosh, Matthew Jagielski, Christo Wilson
2022	J	`jnl`	Subverting Fair Image Search with Generative Adversarial Perturbations. CoRR Avijit Ghosh, Matthew Jagielski, Christo Wilson
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	A*	`conf`	Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CCS Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini
2022	J	`jnl`	Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CoRR Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini
2021	J	`jnl`	Counterfactual Memorization in Neural Language Models. CoRR Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
2021	A*	`conf`	Extracting Training Data from Large Language Models. USENIX Security Symposium Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel
2021	J	`jnl`	Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) versus QUIC. J. Cryptol. Shan Chen, Samuel Jero, Matthew Jagielski, Alexandra Boldyreva, Cristina Nita-Rotaru
2021	A*	`conf`	Subpopulation Data Poisoning Attacks. CCS Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea
2020	A*	`conf`	Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS Matthew Jagielski, Jonathan R. Ullman, Alina Oprea
2020	J	`jnl`	Auditing Differentially Private Machine Learning: How Private is Private SGD? CoRR Matthew Jagielski, Jonathan R. Ullman, Alina Oprea
2020	—	`conf`	Cryptanalytic Extraction of Neural Network Models. CRYPTO (3) Nicholas Carlini, Matthew Jagielski, Ilya Mironov
2020	J	`jnl`	Cryptanalytic Extraction of Neural Network Models. CoRR Nicholas Carlini, Matthew Jagielski, Ilya Mironov
2020	J	`jnl`	Extracting Training Data from Large Language Models. CoRR Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel
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`	Subpopulation Data Poisoning Attacks. CoRR Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea
2019	A*	`conf`	Differentially Private Fair Learning. ICML Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
2019	J	`jnl`	High-Fidelity Extraction of Neural Network Models. CoRR Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot
2019	—	`conf`	Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) vs. QUIC. ESORICS (1) Shan Chen, Samuel Jero, Matthew Jagielski, Alexandra Boldyreva, Cristina Nita-Rotaru
2019	J	`jnl`	Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) vs. QUIC. IACR Cryptol. ePrint Arch. Shan Chen, Samuel Jero, Matthew Jagielski, Alexandra Boldyreva, Cristina Nita-Rotaru
2019	A*	`conf`	Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks. USENIX Security Symposium Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli
2018	J	`jnl`	Differentially Private Fair Learning. CoRR Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
2018	A*	`conf`	Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. IEEE Symposium on Security and Privacy Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li
2018	J	`jnl`	Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. CoRR Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li
2018	A	`conf`	Network and system level security in connected vehicle applications. ICCAD Hengyi Liang, Matthew Jagielski, Bowen Zheng, Chung-Wei Lin, Eunsuk Kang, Shinichi Shiraishi, Cristina Nita-Rotaru, Qi Zhu
2018	J	`jnl`	On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks. CoRR Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli
2018	—	`conf`	Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars. WISEC Matthew Jagielski, Nicholas Jones, Chung-Wei Lin, Cristina Nita-Rotaru, Shinichi Shiraishi