Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking
<|reference_start|>Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking: 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.<|reference_end|>
@article{ozdel2024privacy-preserving,
    title={Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking},
    author={Suleyman Ozdel, Efe Bozkir, Enkelejda Kasneci},
    journal={arXiv preprint arXiv:2404.06216},
    year={2024},
    doi={10.1145/3655605},
    archivePrefix={arXiv},
    eprint={2404.06216},
    primaryClass={cs.CR cs.HC}
}