Cloud Computing, Data Modeling and Data Silos

Meet the Final Winners of the U.S. PETs Prize Challenge

DrivenData Labs

MARCH 30, 2023

Our framework involves three key components: (1) model personalization for capturing data heterogeneity across data silos, (2) local noisy gradient descent for silo-specific, node-level differential privacy in contact graphs, and (3) model mean-regularization to balance privacy-heterogeneity trade-offs and minimize the loss of accuracy.

Machine Learning

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Meet the Final Winners of the U.S. PETs Prize Challenge

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