Matthew Jagielski

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 72

Top Topics

Machine Learning

Top Venues

USENIX Security Symposium

IACR Cryptol. ePrint Arch.

Publications

Harsh Chaudhari, Giorgio Severi, John Abascal, Matthew Jagielski, Christopher A. Choquette-Choo, Milad Nasr, Cristina Nita-Rotaru, Alina Oprea
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation. CoRR (2024)
Matthew Jagielski, Om Thakkar, Lun Wang
Noise Masking Attacks and Defenses for Pretrained Speech Models. CoRR (2024)
Aldo G. Carranza, Rezsa Farahani, Natalia Ponomareva, Alexey Kurakin, Matthew Jagielski, Milad Nasr
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models. NAACL-HLT (2024)
Matthew Jagielski, Om Thakkar, Lun Wang
Noise Masking Attacks and Defenses for Pretrained Speech Models. ICASSP (2024)
Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jiménez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI. CoRR (2024)
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
Stealing Part of a Production Language Model. CoRR (2024)
Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, Florian Tramèr
Privacy Side Channels in Machine Learning Systems. USENIX Security Symposium (2024)
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR (2024)
Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr
Auditing Private Prediction. CoRR (2024)
Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
Backdoor Attacks for In-Context Learning with Language Models. CoRR (2023)
Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu
How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models. Proc. Priv. Enhancing Technol. 2023 (3) (2023)
Thomas Steinke, Milad Nasr, Matthew Jagielski
Privacy Auditing with One (1) Training Run. CoRR (2023)
Matthew Jagielski, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, Nicholas Carlini
Students Parrot Their Teachers: Membership Inference on Model Distillation. CoRR (2023)
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr
Poisoning Web-Scale Training Datasets is Practical. CoRR (2023)
Matthew Jagielski
A Note On Interpreting Canary Exposure. CoRR (2023)
Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini, Florian Tramèr
Students Parrot Their Teachers: Membership Inference on Model Distillation. NeurIPS (2023)
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
Extracting Training Data from Diffusion Models. USENIX Security Symposium (2023)
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
Counterfactual Memorization in Neural Language Models. NeurIPS (2023)
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee
Scalable Extraction of Training Data from (Production) Language Models. CoRR (2023)
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
Tight Auditing of Differentially Private Machine Learning. CoRR (2023)
Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators. CoRR (2023)
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. INLG (2023)
Harsh Chaudhari, Matthew Jagielski, Alina Oprea
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning. SaTML (2023)
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
SNAP: Efficient Extraction of Private Properties with Poisoning. SP (2023)
Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr
Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models. CoRR (2023)
Thomas Steinke, Milad Nasr, Matthew Jagielski
Privacy Auditing with One (1) Training Run. NeurIPS (2023)
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
Extracting Training Data from Diffusion Models. CoRR (2023)
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
Are aligned neural networks adversarially aligned? CoRR (2023)
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang
Quantifying Memorization Across Neural Language Models. ICLR (2023)
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
Tight Auditing of Differentially Private Machine Learning. USENIX Security Symposium (2023)
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, Ludwig Schmidt
Are aligned neural networks adversarially aligned? NeurIPS (2023)
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
Challenges towards the Next Frontier in Privacy. CoRR (2023)
Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, Florian Tramèr
Privacy Side Channels in Machine Learning Systems. CoRR (2023)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. ICLR (2023)
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
The Privacy Onion Effect: Memorization is Relative. NeurIPS (2022)
Avijit Ghosh, Matthew Jagielski, Christo Wilson
Subverting Fair Image Search with Generative Adversarial Perturbations. FAccT (2022)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. CoRR (2022)
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR (2022)
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CCS (2022)
Giorgio Severi, Matthew Jagielski, Gökberk Yar, Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru
Network-Level Adversaries in Federated Learning. CNS (2022)
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CoRR (2022)
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang
Quantifying Memorization Across Neural Language Models. CoRR (2022)
Giorgio Severi, Matthew Jagielski, Gökberk Yar, Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru
Network-Level Adversaries in Federated Learning. CoRR (2022)
Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu
How to Combine Membership-Inference Attacks on Multiple Updated Models. CoRR (2022)
Harsh Chaudhari, Matthew Jagielski, Alina Oprea
SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. CoRR (2022)
Avijit Ghosh, Matthew Jagielski, Christo Wilson
Subverting Fair Image Search with Generative Adversarial Perturbations. CoRR (2022)
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
The Privacy Onion Effect: Memorization is Relative. CoRR (2022)
Harsh Chaudhari, Matthew Jagielski, Alina Oprea
SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. IACR Cryptol. ePrint Arch. 2022 (2022)
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. CoRR (2022)
Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini
Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR (2022)
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
Counterfactual Memorization in Neural Language Models. CoRR (2021)
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
Extracting Training Data from Large Language Models. USENIX Security Symposium (2021)
Shan Chen, Samuel Jero, Matthew Jagielski, Alexandra Boldyreva, Cristina Nita-Rotaru
Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) versus QUIC. J. Cryptol. 34 (3) (2021)
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea
Subpopulation Data Poisoning Attacks. CCS (2021)
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea
Subpopulation Data Poisoning Attacks. CoRR (2020)
Matthew Jagielski, Jonathan R. Ullman, Alina Oprea
Auditing Differentially Private Machine Learning: How Private is Private SGD? CoRR (2020)
Matthew Jagielski, Jonathan R. Ullman, Alina Oprea
Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS (2020)
Nicholas Carlini, Matthew Jagielski, Ilya Mironov
Cryptanalytic Extraction of Neural Network Models. CoRR (2020)
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot
High Accuracy and High Fidelity Extraction of Neural Networks. USENIX Security Symposium (2020)
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
Extracting Training Data from Large Language Models. CoRR (2020)
Nicholas Carlini, Matthew Jagielski, Ilya Mironov
Cryptanalytic Extraction of Neural Network Models. CRYPTO (3) (2020)
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks. USENIX Security Symposium (2019)
Shan Chen, Samuel Jero, Matthew Jagielski, Alexandra Boldyreva, Cristina Nita-Rotaru
Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) vs. QUIC. IACR Cryptol. ePrint Arch. 2019 (2019)
Shan Chen, Samuel Jero, Matthew Jagielski, Alexandra Boldyreva, Cristina Nita-Rotaru
Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) vs. QUIC. ESORICS (1) (2019)
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot
High-Fidelity Extraction of Neural Network Models. CoRR (2019)
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
Differentially Private Fair Learning. ICML (2019)
Hengyi Liang, Matthew Jagielski, Bowen Zheng, Chung-Wei Lin, Eunsuk Kang, Shinichi Shiraishi, Cristina Nita-Rotaru, Qi Zhu
Network and system level security in connected vehicle applications. ICCAD (2018)
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
Differentially Private Fair Learning. CoRR (2018)
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. IEEE Symposium on Security and Privacy (2018)
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. CoRR (2018)
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli
On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks. CoRR (2018)
Matthew Jagielski, Nicholas Jones, Chung-Wei Lin, Cristina Nita-Rotaru, Shinichi Shiraishi
Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars. WISEC (2018)