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Privacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation / Alexander Dunkel in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 10 (October 2020) . - 20 p. Titre : Privacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation Type de document : Article/Communication Auteurs : Alexander Dunkel, Auteur ; Marc Löchner, Auteur ; Dirk Burghardt, Auteur Année de publication : 2020 Article en page(s) : 20 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatiale [Termes IGN] base de données d'images [Termes IGN] chaîne de traitement [Termes IGN] confidentialité [Termes IGN] données localisées des bénévoles [Termes IGN] protection de la vie privée [Termes IGN] réseau social [Termes IGN] ressources naturelles [Termes IGN] réutilisation des données [Termes IGN] visualisation de données Résumé : (auteur) Through volunteering data, people can help assess information on various aspects of their surrounding environment. Particularly in natural resource management, Volunteered Geographic Information (VGI) is increasingly recognized as a significant resource, for example, supporting visitation pattern analysis to evaluate collective values and improve natural well-being. In recent years, however, user privacy has become an increasingly important consideration. Potential conflicts often emerge from the fact that VGI can be re-used in contexts not originally considered by volunteers. Addressing these privacy conflicts is particularly problematic in natural resource management, where visualizations are often explorative, with multifaceted and sometimes initially unknown sets of analysis outcomes. In this paper, we present an integrated and component-based approach to privacy-aware visualization of VGI, specifically suited for application to natural resource management. As a key component, HyperLogLog (HLL)—a data abstraction format—is used to allow estimation of results, instead of more accurate measurements. While HLL alone cannot preserve privacy, it can be combined with existing approaches to improve privacy while, at the same time, maintaining some flexibility of analysis. Together, these components make it possible to gradually reduce privacy risks for volunteers at various steps of the analytical process. A specific use case demonstration is provided, based on a global, publicly-available dataset that contains 100 million photos shared by 581,099 users under Creative Commons licenses. Both the data processing pipeline and resulting dataset are made available, allowing transparent benchmarking of the privacy–utility tradeoffs. Numéro de notice : A2020-664 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9100607 Date de publication en ligne : 20/10/2020 En ligne : https://doi.org/10.3390/ijgi9100607 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96141 [article]