Machine Learning Privacy
Our research focuses on understanding and mitigating privacy risks associated with machine learning. This includes both data privacy (protecting sensitive data used to train a model during the collection and learning process) and inference privacy (limiting what can be inferred about sensitive training data from an exposed model).
Inference Privacy
These blog posts summarize our recent work on evaluating inference leakage from models:
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On the Risks of Distribution Inference (PETS 2022 Paper: Formalizing and Estimating Distribution Inference Risks
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Merlin, Morgan, and the Importance of Thresholds and Priors (PETS 2021 Paper: Revisiting Membership Inference Under Realistic Assumptions )
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Evaluating Differentially Private Machine Learning in Practice (USENIX Security 2019 Paper)
Publications
Are Attribute Inference Attacks Just Imputation?. Bargav Jayaraman and David Evans. In 29th ACM Conference on Computer and Communications Security (CCS). November 2022. [Arxiv] [Code]
Formalizing and Estimating Distribution Inference Risks . Anshuman Suri and David Evans. In Privacy Enhancing Technologies Symposium (PETS). July 2022. (Also published in Proceedings on Privacy Enhancing Technologies, Issue 4, 2022.) [Arxiv] [Code]
Revisiting Membership Inference Under Realistic Assumptions. Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, and David Evans. In Proceedings on Privacy Enhancing Technologies (PETS). July 2021. [Arxiv] [PDF] [Code]
Evaluating Differentially Private Machine Learning in Practice. Bargav Jayaraman and David Evans. In 28th USENIX Security Symposium. Santa Clara. August 2019. [PDF] [arXiv] [code]
Privacy-Preserving Machine Learning
Distributed learning (sometimes marketed as federated learning) allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data.
Projects
Integrating Multi-Party Computation with Differential Privacy (Code, NeurIPS 2018 Paper
Bargav Jayaraman, Lingxiao Wang, Quanquan Gu
Privacy-preserving Medical Decision Systems
Josephine Lamp and Lu Feng
Privacy-Preserving Nonconvex Optimization [Preprint]
Lingxiao Wang, Bargav Jayaraman, Quanquan Gu
Privacy Study Group
Meetings on Tuesdays, 11am (Summer 2020)
Leader: Bargav Jayaraman