  As eye tracking becomes pervasive with screen-based devices and head-mounted
displays, privacy concerns regarding eye-tracking data have escalated. While
state-of-the-art approaches for privacy-preserving eye tracking mostly involve
differential privacy and empirical data manipulations, previous research has
not focused on methods for scanpaths. We introduce a novel privacy-preserving
scanpath comparison protocol designed for the widely used Needleman-Wunsch
algorithm, a generalized version of the edit distance algorithm. Particularly,
by incorporating the Paillier homomorphic encryption scheme, our protocol
ensures that no private information is revealed. Furthermore, we introduce a
random processing strategy and a multi-layered masking method to obfuscate the
values while preserving the original order of encrypted editing operation
costs. This minimizes communication overhead, requiring a single communication
round for each iteration of the Needleman-Wunsch process. We demonstrate the
efficiency and applicability of our protocol on three publicly available
datasets with comprehensive computational performance analyses and make our
source code publicly accessible.
