  The recent emergence of ubiquitous, multi-platform eye tracking has raised
user privacy concerns over re-identification across platforms, where a person
is re-identified across multiple eye tracking-enabled platforms using
personally identifying information that is implicitly expressed through their
eye movement. We present an empirical investigation quantifying a modern eye
movement biometric model's ability to link subject identities across three
different eye tracking devices using eye movement signals from each device. We
show that a state-of-the art eye movement biometrics model demonstrates
above-chance levels of biometric performance (34.99% equal error rate, 15%
rank-1 identification rate) when linking user identities across one pair of
devices, but not for the other. Considering these findings, we also discuss the
impact that eye tracking signal quality has on the model's ability to
meaningfully associate a subject's identity between two substantially different
eye tracking devices. Our investigation advances a fundamental understanding of
the privacy risks for identity linkage across platforms by employing both
quantitative and qualitative measures of biometric performance, including a
visualization of the model's ability to distinguish genuine and imposter
authentication attempts across platforms.
