I am a researcher at Google DeepMind, where I work on machine learning privacy and security. My research interests include understanding the security and privacy issues of machine learning systems and developing techniques to mitigate these risks. I am also interested in circumventing internet censorship. I received my PhD from the University of Massachusetts Amherst in 2022, where I worked on designing censorship circumvention technologies and also studying machine learning privacy.
Selected Recent works
2023
- Privacy Auditing with One (1) Training Run
Thomas Steinke, Milad Nasr and Matthew Jagielski
NeurIPS, 2023 (Outstanding Paper). - Tight Auditing of Differentially Private Machine Learning
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
USENIX Security, 2023 (Distinguished Paper). - Extracting Training Data from Diffusion Models
Nicholas Carlini*, Jamie Hayes*, Milad Nasr*, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
USENIX Security, 2023.
2022
- Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramer
IEEE Symposium on Security and Privacy (SP) 2022.
2021
- Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
IEEE Symposium on Security and Privacy (SP) 2021.