Assessing and Mitigating the Privacy Implications of Eye Tracking on Handheld Mobile Devices

While gaze data brings benefits like allowing hands-free interaction, it can also reveal sensitive information about people, such as their gender, age, and geographical origin. Privacy leakage and safeguards have been explored for gaze data collected through headsets and stationary eye trackers, but never for gaze data collected through handheld mobile devices, like smartphones. Eye tracking on handheld mobile devices has the potential to be ubiquitous, but gaze data is typically of lower quality, compounded by additional noise and instability due to less controlled environments and screen size constraints. To address this gap, we provide the first evidence of privacy leakage through gaze data collected on handheld mobile devices. In a user study (N=35), we collected our novel SmartEyePhone dataset of gaze data using a smartphone’s front-facing camera. Second, we present the first evaluation and comparison of three Differential Privacy (DP) techniques against our dataset and the TüEyeQ dataset, which was collected in prior work using a stationary remote eye tracker. We found that SmartEyePhone dataset leaks on average 65.5% of private data. DP mechanisms reduce privacy leakage in our data by 22.33% using Laplace mechanism, 10.43% using Exponential mechanism, 22.60% using Gaussian mechanism, and 28.34% using AI model perturbation. However, this also reduces the accuracy of the main task prediction, and is impacted by the choice of privacy parameter values. We present insights on the tradeoff between privacy preservation and the practical usefulness of gaze data. Our insights advance the understanding of privacy in mobile settings and pave the way for privacy preserving gaze-enabled handheld mobile devices.