Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies

TL;DR

This paper introduces new privacy-preserving machine learning methods that remove sensitive information from data during model training, ensuring user privacy without sacrificing predictive accuracy, and are computationally efficient for on-device use.

Contribution

It proposes novel supervised and adversarial learning variants that optimize privacy and utility simultaneously in an end-to-end, low-computation framework.

Findings

Models effectively hide sensitive info while maintaining utility.

Experimental results on mobile sensing and face datasets show high privacy and accuracy.

Methods are computationally efficient for on-device privacy protection.

Abstract

The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there is a strong need to develop techniques that ensure the data serve only the intended purposes, giving users control over the information they share. To this end, this paper studies new variants of supervised and adversarial learning methods, which remove the sensitive information in the data before they are sent out for a particular application. The explored methods optimize privacy preserving feature mappings and predictive models simultaneously in an end-to-end fashion. Additionally, the models are built with an emphasis on placing little computational burden on the user side so that the data can be desensitized on device in a cheap manner. Experimental results on mobile sensing and face datasets demonstrate that our models can successfully maintain the utility performances of predictive models while causing sensitive predictions to perform poorly.