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Matthew Jagielski
Extracting Training Data from Large Language Models

Dec 14, 2020
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, Colin Raffel


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Subpopulation Data Poisoning Attacks

Jun 24, 2020
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea


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Auditing Differentially Private Machine Learning: How Private is Private SGD?

Jun 13, 2020
Matthew Jagielski, Jonathan Ullman, Alina Oprea


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Cryptanalytic Extraction of Neural Network Models

Mar 10, 2020
Nicholas Carlini, Matthew Jagielski, Ilya Mironov


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High-Fidelity Extraction of Neural Network Models

Sep 03, 2019
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot


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Differentially Private Fair Learning

Dec 06, 2018
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman


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On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks

Sep 08, 2018
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli


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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Apr 01, 2018
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li

* Preprint of the work accepted for publication at the 39th IEEE Symposium on Security and Privacy, San Francisco, CA, USA, May 21-23, 2018 
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