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2020 – today
- 2023
[i30]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)- [i29]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) - [i28]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 abs/2302.10149 (2023) - [i27]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) - [i26]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) - [i25]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) - [i24]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) - [i23]Thomas Steinke, Milad Nasr, Matthew Jagielski:
Privacy Auditing with One (1) Training Run. CoRR abs/2305.08846 (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, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini:
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, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang:
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, 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: 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, Chang Liu, Cristina Nita-Rotaru, Bo Li:
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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last updated on 2023-05-18 23:16 CEST by the dblp team
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