  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.
