An investigation of privacy preservation in deep learning-based eye-tracking.
Journal:
Biomedical engineering online
Published Date:
Sep 13, 2022
Abstract
BACKGROUND: The expanding usage of complex machine learning methods such as deep learning has led to an explosion in human activity recognition, particularly applied to health. However, complex models which handle private and sometimes protected data, raise concerns about the potential leak of identifiable data. In this work, we focus on the case of a deep network model trained on images of individual faces.