Data pertaining to Chapter 4 of the PhD dissertation: “Protecting User Privacy in Gaze Estimation Services"

This dataset accompanies the paper “PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services” (Chapter 4 of the PhD dissertation). This research focuses on protecting user privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. This dataset contains the codes to reproduce the results presented in the paper and the real-time video demo of the proposed method.