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[Seminar] When Models Learn Too Much

Friday, October 8, 2021

11:00 am - 12:00 pm


PGH 232


Statistical machine learning uses training data to produce models that capture patterns in that data. When models are trained on private data, such as medical records or personal emails, there is a risk that those models not only learn the hoped-for patterns, but will also learn and expose sensitive information about their training data.

Several different types of inference attacks on machine learning models have been found, and methods have been proposed to mitigate the risks of exposing sensitive aspects of training data. Differential privacy provides formal guarantees bounding a particular type of inference risk, but provides no guarantees against other kinds of inferences, and, at least with state-of-the-art methods, providing
substantive differential privacy guarantees requires adding so much noise to the training process for complex models that the resulting models are useless. Experimental evidence, however, suggests that inference attacks have limited power, and in many cases a very small amount of privacy noise seems to be enough to defuse inference attacks.

In this talk, I will give an overview of a variety of different inference risks for machine learning models, talk about strategies for evaluating model inference risks, and report on some experiments by our research group to better understand the power of inference attacks in more realistic settings, and speculate on some broader the connections between privacy, fairness, and adversarial robustness.

About the Speaker

David Evans is a Professor of Computer Science at the University of Virginia where he leads a research group focusing on security and privacy ( He won the Outstanding Faculty Award from the State Council of Higher Education for Virginia, and was Program Co-Chair for the 24th ACM Conference on Computer and Communications Security (CCS 2017) and the 30th (2009) and 31st (2010) IEEE Symposia on Security and Privacy, where he initiated the Systematization of Knowledge (SoK) papers. He is the author of an open computer science textbook ( and a children’s book on combinatorics and computability (, and co-author of a book on secure multi-party computation ( He has SB, SM and PhD degrees from MIT and has been a faculty member at the University of Virginia since 1999.

October 8th 2021 seminar with David Evans. When Models Learn Too Much
Philip Guthrie Hoffman Hall (PGH), 3551 Cullen Blvd, Houston, TX 77004, USA