ICLR DPML 2021: Inference Risks for Machine Learning

I gave an invited talk at the Distributed and Private Machine Learning (DPML) workshop at ICLR 2021 on Inference Risks for Machine Learning.

The talk mostly covers work by Bargav Jayaraman on evaluating privacy in machine learning and connecting attribute inference and imputation, and recent work by Anshuman Suri on property inference.

Codaspy 2021 Keynote: When Models Learn Too Much

Here are the slides for my talk at the 11th ACM Conference on Data and Application Security and Privacy: When Models Learn Too Much [PDF] The talk includes Bargav Jayaraman’s work (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) on evaluating privacy in machine learning, as well as more recent work by Anshuman Suri on property inference attacks, and Bargav on attribute inference and imputation: Merlin, Morgan, and the Importance of Thresholds and Priors Evaluating Differentially Private Machine Learning in Practice “When models learn too much.

Read More…

Merlin, Morgan, and the Importance of Thresholds and Priors

Post by Katherine Knipmeyer Machine learning poses a substantial risk that adversaries will be able to discover information that the model does not intend to reveal. One set of methods by which consumers can learn this sensitive information, known broadly as membership inference attacks, predicts whether or not a query record belongs to the training set. A basic membership inference attack involves an attacker with a given record and black-box access to a model who tries to determine whether said record was a member of the model’s training set.

Read More…

All Posts by Category or Tags.