Here are the slides for my talk at the Practical and Theoretical Privacy of Machine Learning Training Pipelines Workshop at the Microsoft Research Summit (21 October 2021):
The work by Bargav Jayaraman (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) that I talked about on improving membership inference attacks is described in more details here:
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Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans. Revisiting Membership Inference Under Realistic Assumptions (PETS 2021).
[Blog] [Code: https://github.com/bargavj/EvaluatingDPML] -
Bargav Jayaraman, David Evans. Evaluating Differentially Private Machine Learning in Practice (USENIX Security 2019).
[Blog] [Talk Video] [Code]
The work on distribution inference is described in this paper (by Anshuman Suri):
- Formalizing and Estimating Distribution Inference Risks
[Blog] [Code: https://github.com/iamgroot42/form_est_dist_risks]
The work on attribute inference and imputation isn’t yet posted, but feel free to contact me with any questions about it.