Best Paper Award der MobiSPC’18 Konferenz

Universität Ulm

Rüdiger Pryss vom Institut für Datenbanken und Informationssysteme (DBIS) erhält einen weiteren Best Paper Award, dieses mal auf der 15th Int’l Conference on Mobile Systems and Pervasive Computing (MobiSPC 2018).

Zusammen mit Felix Beierle, Vinh Thuy Tran (beide TU Berlin), Mathias Allemand (Uni Zürich), Patrick Neff, Winfried Schlee (beide Uni Regensburg), Thomas Probst (Donauuni Krems), und Johannes Zimmermann (Psychologische Hochschule Berlin), hat Rüdiger Pryss den Best Paper Award der 15th Int’l Conference on Mobile Systems and Pervasive Computing (MobiSPC) erhalten. Der Preis wurde für die Arbeit der Gruppe zu Kontextdaten und Privacy Modellen für Mobile Data Collection Apps vergeben.

Die MobiSPC fand vom 13.-15. August 2018 in Gran Canaria statt. Die Fachtagung bietet eines der führenden Foren über das sich Forscher, Praktiker und Studierende zu verschiedenen Themen rund um Pervasive Computing und mobile Systeme austauschen.

Referenz

Beierle, Felix and Tran, Vinh Thuy and Allemand, Mathias and Neff, Patrick and Schlee, Winfried and Probst, Thomas and Pryss, Rüdiger and Zimmermann, Johannes (2018) Context Data Categories and Privacy Model for Mobile Data Collection Apps. In: 15th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2018) , August 13-15, Gran Canaria, Spanien.

Zusammenfassung

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than similar existing apps, including metadata on notifications, photos taken, and music played back by the user. For the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we develop the privacy model PM-MoDaC specifically for apps related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Although the utilization of the user’s personality based on the usage of his or her smartphone is a challenging endeavor, it seems to be a promising approach for various types of context-aware mobile applications.