Real-Time Lightweight Gaze Privacy-Preservation Techniques Validated via Offline Gaze-Based Interaction Simulation

TL;DR

This paper presents a framework for evaluating real-time gaze privacy-preservation techniques using offline data, demonstrating their effectiveness in reducing identity information and enhancing gaze interaction with a lightweight Kalman filter.

Contribution

It introduces a novel offline simulation framework for validating real-time gaze privacy methods and employs a lightweight Kalman filter to improve privacy and interaction quality.

Findings

Reduced identity-related information in gaze data

Improved gaze-based interaction performance

Validated effectiveness on large datasets

Abstract

This study examines the effectiveness of the real-time privacy-preserving techniques through an offline gaze-based interaction simulation framework. Those techniques aim to reduce the amount of identity-related information in eye-tracking data while improving the efficacy of the gaze-based interaction. Although some real-time gaze privatization methods were previously explored, their validation on the large dataset was not conducted. We propose a functional framework that allows to study the efficacy of real-time gaze privatization on an already collected offline dataset. The key metric used to assess the reduction of identity-related information is the identification rate, while improvements in gaze-based interactions are evaluated through signal quality during interaction. Our additional contribution is the employment of an extremely lightweight Kalman filter framework that reduces the amount of identity-related information in the gaze signal and improves gaze-based interaction performance.