  Eye tracking is handled as one of the key technologies for applications that
assess and evaluate human attention, behavior, and biometrics, especially using
gaze, pupillary, and blink behaviors. One of the challenges with regard to the
social acceptance of eye tracking technology is however the preserving of
sensitive and personal information. To tackle this challenge, we employ a
privacy-preserving framework based on randomized encoding to train a Support
Vector Regression model using synthetic eye images privately to estimate the
human gaze. During the computation, none of the parties learn about the data or
the result that any other party has. Furthermore, the party that trains the
model cannot reconstruct pupil, blinks or visual scanpath. The experimental
results show that our privacy-preserving framework is capable of working in
real-time, with the same accuracy as compared to non-private version and could
be extended to other eye tracking related problems.
