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Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns / Hua Liao in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° 1 (January 2022) . - pp 50 - 69 Titre : Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns Type de document : Article/Communication Auteurs : Hua Liao, Auteur ; Weihua Dong, Auteur ; Zhicheng Zhan, Auteur Année de publication : 2022 Article en page(s) : pp 50 - 69 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] comportement [Termes IGN] confidentialité [Termes IGN] identité [Termes IGN] lecture de carte [Termes IGN] oculométrie [Termes IGN] orientation [Termes IGN] partage de données localisées [Termes IGN] protection de la vie privée [Termes IGN] utilisateur [Termes IGN] visualisation cartographique [Vedettes matières IGN] Cartologie Résumé : (auteur) Individuals with different characteristics exhibit different eye movement patterns in map reading and wayfinding tasks. In this study, we aim to explore whether and to what extent map users’ eye movements can be used to detect who created them. Specifically, we focus on the use of gaze data for inferring users’ identities when users are performing map-based spatial tasks. We collected 32 participants’ eye movement data as they utilized maps to complete a series of self-localization and spatial orientation tasks. We extracted five sets of eye movement features and trained a random forest classifier. We used a leave-one-task-out approach to cross-validate the classifier and achieved the best identification rate of 89%, with a 2.7% equal error rate. This result is among the best performances reported in eye movement user identification studies. We evaluated the feature importance and found that basic statistical features (e.g. pupil size, saccade latency and fixation dispersion) yielded better performance than other feature sets (e.g. spatial fixation densities, saccade directions and saccade encodings). The results open the potential to develop personalized and adaptive gaze-based map interactions but also raise concerns about user privacy protection in data sharing and gaze-based geoapplications. Numéro de notice : A2022-018 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1980435 Date de publication en ligne : 06/10/2021 En ligne : https://doi.org/10.1080/15230406.2021.1980435 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99161 [article]

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