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Our Differential Privacy Mini-series

We’ve just finished off a series of articles on some recent research results applying differential privacy to improve machine learning. Some of these results are pretty technical, so we thought it was worth working through concrete examples. And some of the original results are locked behind academic journal paywalls, so […]

A simple differentially private-ish procedure

Authors: John Mount and Nina Zumel Nina and I were noodling with some variations of differentially private machine learning, and think we have found a variation of a standard practice that is actually fairly efficient in establishing differential privacy a privacy condition (but, as commenters pointed out- not differential privacy). […]

Using differential privacy to reuse training data

Win-Vector LLC‘s Nina Zumel wrote a great article explaining differential privacy and demonstrating how to use it to enhance forward step-wise logistic regression (essentially reusing test data). This allowed her to reproduce results similar to the recent Science paper “The reusable holdout: Preserving validity in adaptive data analysis”. The technique […]

A Simpler Explanation of Differential Privacy

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 […]