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Matthew Jagielski
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2020 – today
- 2024
- [c32]Matthew Jagielski, Om Thakkar, Lun Wang:
Noise Masking Attacks and Defenses for Pretrained Speech Models. ICASSP 2024: 4810-4814 - [c31]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. ICML 2024 - [c30]Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr:
Auditing Private Prediction. ICML 2024 - [c29]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: 3920-3930 - [c28]Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum S. Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr:
Poisoning Web-Scale Training Datasets is Practical. SP 2024: 407-425 - [c27]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 - [i42]Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr:
Auditing Private Prediction. CoRR abs/2402.09403 (2024) - [i41]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 abs/2403.06634 (2024) - [i40]Matthew Jagielski, Om Thakkar, Lun Wang:
Noise Masking Attacks and Defenses for Pretrained Speech Models. CoRR abs/2404.02052 (2024) - [i39]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 abs/2405.20485 (2024) - [i38]Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch:
Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR abs/2406.08039 (2024) - [i37]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 abs/2407.00106 (2024) - [i36]Thomas Steinke, Milad Nasr, Arun Ganesh, Borja Balle, Christopher A. Choquette-Choo, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam D. Smith, Andreas Terzis:
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. CoRR abs/2410.06186 (2024) - 2023
- [j2]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): 211-232 (2023) - [c26]Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang:
Quantifying Memorization Across Neural Language Models. ICLR 2023 - [c25]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 - [c24]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: 28-53 - [c23]Thomas Steinke, Milad Nasr, Matthew Jagielski:
Privacy Auditing with One (1) Training Run. NeurIPS 2023 - [c22]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 - [c21]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 - [c20]Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini:
Counterfactual Memorization in Neural Language Models. NeurIPS 2023 - [c19]Harsh Chaudhari, Matthew Jagielski, Alina Oprea:
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning. SaTML 2023: 176-196 - [c18]Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. SP 2023: 400-417 - [c17]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: 1631-1648 - [c16]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: 5253-5270 - [i35]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 abs/2301.13188 (2023) - [i34]Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis:
Tight Auditing of Differentially Private Machine Learning. CoRR abs/2302.07956 (2023) - [i33]Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum S. Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr:
Poisoning Web-Scale Training Datasets is Practical. CoRR abs/2302.10149 (2023) - [i32]Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini:
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators. CoRR abs/2302.13464 (2023) - [i31]Matthew Jagielski, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, Nicholas Carlini:
Students Parrot Their Teachers: Membership Inference on Model Distillation. CoRR abs/2303.03446 (2023) - [i30]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 abs/2304.06929 (2023) - [i29]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 abs/2305.05973 (2023) - [i28]Thomas Steinke, Milad Nasr, Matthew Jagielski:
Privacy Auditing with One (1) Training Run. CoRR abs/2305.08846 (2023) - [i27]Matthew Jagielski:
A Note On Interpreting Canary Exposure. CoRR abs/2306.00133 (2023) - [i26]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 abs/2306.15447 (2023) - [i25]Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini:
Backdoor Attacks for In-Context Learning with Language Models. CoRR abs/2307.14692 (2023) - [i24]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 abs/2309.05610 (2023) - [i23]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 abs/2311.17035 (2023) - 2022
- [c15]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: 2779-2792 - [c14]Giorgio Severi, Matthew Jagielski, Gökberk Yar, Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru:
Network-Level Adversaries in Federated Learning. CNS 2022: 19-27 - [c13]Avijit Ghosh, Matthew Jagielski, Christo Wilson
Subverting Fair Image Search with Generative Adversarial Perturbations. FAccT 2022: 637-650 - [c12]Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr:
The Privacy Onion Effect: Memorization is Relative. NeurIPS 2022 - [i22]Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang:
Quantifying Memorization Across Neural Language Models. CoRR abs/2202.07646 (2022) - [i21]Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini:
Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR abs/2202.12219 (2022) - [i20]Florian Tramèr
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CoRR abs/2204.00032 (2022) - [i19]Avijit Ghosh, Matthew Jagielski, Christo Wilson:
Subverting Fair Image Search with Generative Adversarial Perturbations. CoRR abs/2205.02414 (2022) - [i18]Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu:
How to Combine Membership-Inference Attacks on Multiple Updated Models. CoRR abs/2205.06369 (2022) - [i17]Harsh Chaudhari, Matthew Jagielski, Alina Oprea:
SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. CoRR abs/2205.09986 (2022) - [i16]Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
The Privacy Onion Effect: Memorization is Relative. CoRR abs/2206.10469 (2022) - [i15]Matthew Jagielski, Om Thakkar, Florian Tramèr
Measuring Forgetting of Memorized Training Examples. CoRR abs/2207.00099 (2022) - [i14]Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR abs/2208.12348 (2022) - [i13]Giorgio Severi, Matthew Jagielski, Gökberk Yar, Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru:
Network-Level Adversaries in Federated Learning. CoRR abs/2208.12911 (2022) - [i12]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 abs/2210.17546 (2022) - [i11]Harsh Chaudhari, Matthew Jagielski, Alina Oprea:
SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. IACR Cryptol. ePrint Arch. 2022: 663 (2022) - 2021
- [j1]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): 26 (2021) - [c11]Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea:
Subpopulation Data Poisoning Attacks. CCS 2021: 3104-3122 - [c10]Nicholas Carlini, Florian Tramèr
Extracting Training Data from Large Language Models. USENIX Security Symposium 2021: 2633-2650 - [i10]Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini:
Counterfactual Memorization in Neural Language Models. CoRR abs/2112.12938 (2021) - 2020
- [c9]Nicholas Carlini, Matthew Jagielski, Ilya Mironov:
Cryptanalytic Extraction of Neural Network Models. CRYPTO (3) 2020: 189-218 - [c8]Matthew Jagielski, Jonathan R. Ullman, Alina Oprea:
Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS 2020 - [c7]Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot:
High Accuracy and High Fidelity Extraction of Neural Networks. USENIX Security Symposium 2020: 1345-1362 - [i9]Nicholas Carlini, Matthew Jagielski, Ilya Mironov:
Cryptanalytic Extraction of Neural Network Models. CoRR abs/2003.04884 (2020) - [i8]Matthew Jagielski, Jonathan R. Ullman, Alina Oprea:
Auditing Differentially Private Machine Learning: How Private is Private SGD? CoRR abs/2006.07709 (2020) - [i7]Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea:
Subpopulation Data Poisoning Attacks. CoRR abs/2006.14026 (2020) - [i6]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 abs/2012.07805 (2020)
2010 – 2019
- 2019
- [c6]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: 404-426 - [c5]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. ICML 2019: 3000-3008 - [c4]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: 321-338 - [i5]Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot:
High-Fidelity Extraction of Neural Network Models. CoRR abs/1909.01838 (2019) - [i4]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: 433 (2019) - 2018
- [c3]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: 94 - [c2]Matthew Jagielski, Alina Oprea, Battista Biggio
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. IEEE Symposium on Security and Privacy 2018: 19-35 - [c1]Matthew Jagielski, Nicholas Jones, Chung-Wei Lin, Cristina Nita-Rotaru, Shinichi Shiraishi:
Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars. WISEC 2018: 184-189 - [i3]Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li:
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. CoRR abs/1804.00308 (2018) - [i2]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 abs/1809.02861 (2018) - [i1]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. CoRR abs/1812.02696 (2018)
Coauthor Index
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