Differential Privacy for Eye-Tracking Data
This addresses privacy concerns for the eye-tracking community, offering a solution to protect individuals in large datasets, though it is incremental as it applies existing differential privacy concepts to a specific domain.
The paper tackled the problem of privacy in eye-tracking data by showing that noise-free heatmaps do not guarantee privacy, and proposed Gaussian noise mechanisms to ensure differential privacy with analyzed tradeoffs.
As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals' data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of (noise-free) heatmaps and show that they do not guarantee privacy. We further propose two noise mechanisms that guarantee privacy and analyze their privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is an elegant solution to preserve privacy for heatmaps. Our results have implications for interdisciplinary research to create differentially private mechanisms for eye tracking.