I have a new article up on Win-Vector, discussing differential privacy and the new recent results on applying differential privacy to enable reuse of holdout data in machine learning.
Differential privacy was originally developed to facilitate secure analysis over sensitive data, with mixed success. It’s back in the news again now, with exciting results from Cynthia Dwork, et. al. (see references at the end of the article) that apply results from differential privacy to machine learning.
In this article we’ll work through the definition of differential privacy and demonstrate how Dwork et.al.’s recent results can be used to improve the model fitting process.