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Interact."],"published-print":{"date-parts":[[2024,5,20]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3655605","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T16:00:05Z","timestamp":1716912005000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3390-6154","authenticated-orcid":false,"given":"S\u00fcleyman","family":"\u00d6zdel","sequence":"first","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4594-4318","authenticated-orcid":false,"given":"Efe","family":"Bozkir","sequence":"additional","affiliation":[{"name":"Technical University of Munich &amp; University of T\u00fcbingen, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3146-4484","authenticated-orcid":false,"given":"Enkelejda","family":"Kasneci","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"A ground-truth data set and a classification algorithm for eye movements in 360-degree videos. arXiv preprint arXiv:1903.06474","author":"Agtzidis Ioannis","year":"2019","unstructured":"Ioannis Agtzidis, Mikhail Startsev, and Michael Dorr. 2019. 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