[2303.07404] GazePair: Efficient Pairing of Augmented Reality Devices Using Gaze Tracking

As Augmented Reality (AR) devices become more prevalent and commercially
viable, the need for quick, efficient, and secure schemes for pairing these
devices has become more pressing. Current methods to securely exchange
holograms require users to send this information through large data centers,
creating security and privacy concerns. Existing techniques to pair these
devices on a local network and share information fall short in terms of
usability and scalability. These techniques either require hardware not
available on AR devices, intricate physical gestures, removal of the device
from the head, do not scale to multiple pairing partners, or rely on methods
with low entropy to create encryption keys. To that end, we propose a novel
pairing system, called GazePair, that improves on all existing local pairing
techniques by creating an efficient, effective, and intuitive pairing protocol.
GazePair uses eye gaze tracking and a spoken key sequence cue (KSC) to generate
identical, independently generated symmetric encryption keys with 64 bits of
entropy. GazePair also achieves improvements in pairing success rates and times
over current methods. Additionally, we show that GazePair can extend to
multiple users. Finally, we assert that GazePair can be used on any Mixed
Reality (MR) device equipped with eye gaze tracking.