Nicolas Papernot

Nicolas Papernot

I am an Assistant Professor at the University of Toronto, in the Department of Electrical and Computer Engineering and the Department of Computer Science. I am also a faculty member at the Vector Institute where I hold a Canada CIFAR AI Chair, and a faculty affiliate at the Schwartz Reisman Institute. In 2022, I was named an Alfred P. Sloan Research Fellow in Computer Science.

My research interests are at the intersection of security, privacy, and machine learning. If you would like to learn more about my research, I recommend reading the blog posts I co-authored on, for example about proof-of-learning, collaborative learning beyond federation, dataset inference, machine unlearning, differentially private ML, or adversarial examples.

My research has been cited in the press, including the New York Times, Popular Science, and Wired. I currently serve as a Program Committee Chair of the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), an Associate Chair of the IEEE Symposium on Security and Privacy (S&P), and an Area Chair of NeurIPS. I earned my Ph.D. in Computer Science and Engineering at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship. Upon graduating, I spent a year at Google Brain where I still spend some of my time.

Email: [email protected]

Office: Pratt 484E and MaRS Suite 710

Mail/Packages: 10 King's College Road, Room SFB540, Toronto, ON M5S 3G4, Canada

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Recent & selected older publications

A complete list of publications is available in my CV.

  • Is Federated Learning a Practical PET Yet?. Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot. preprint
  • Measuring Forgetting of Memorized Training Examples. Matthew Jagielski, Om Thakkar, Florian Tramer, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang. Proceedings of the 11th International Conference on Learning Representations. conference
  • Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller. Proceedings of the 11th International Conference on Learning Representations. conference (+oral)
  • Private Multi-Winner Voting for Machine Learning. Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang. Proceedings on Privacy Enhancing Technologies. conference
  • Differentially Private Speaker Anonymization. Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurelien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot. Proceedings on Privacy Enhancing Technologies. conference
2021 & earlier

Research group

Current students and postdocs
Past students and postdocs
Information for prospective graduate students and postdocs

Research Talks


Here is a list of talks I will be giving. Feel free to reach out if you will be attending one of these events and would like to meet.

Past Recorded Talks

These video resources are a good overview of my research interests.

Randomization in Trustworthy ML
Trustworthy ML
Lecture on ML security and privacy
Privacy-preserving ML
Adversarial examples

Blog Posts

Here is a list of blog posts discussing some of the research questions I'm interested in: