Privacy Preserving Feature Selection for Sparse Linear Regression

ePrint Report: Privacy Preserving Feature Selection for Sparse Linear Regression

Adi Akavia, Ben Galili, Hayim Shaul, Mor Weiss, Zohar Yakhini

Privacy-Preserving Machine Learning (PPML) provides protocols for learning and statistical analysis of data that may be distributed amongst multiple data owners (e.g., hospitals that own proprietary healthcare data), while preserving data privacy. The PPML literature includes protocols for various learning methods, including ridge regression. Ridge regression controls the $L_2$ norm of the model, but does not aim to strictly reduce …

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