Rüdiger Pryss from the Institute of Databases and Information Systems (DBIS) received another best paper award, this time at the 15th Int’l Conference on Mobile Systems and Pervasive Computing (MobiSPC 2018).

Together with Felix Beierle, Vinh Thuy Tran (both TU Berlin), Mathias Allemand (University of Zurich), Patrick Neff, Winfried Schlee (both University of Regensburg), Thomas Probst (Danube University Krems), and Johannes Zimmermann (Psychologische Hochschule Berlin), <link en in iui-dbis team staff ruediger-pryss internal link current>Rüdiger Pryss received the Best Paper Award at the 15th Int’l Conference on Mobile Systems and Pervasive Computing (MobiSPC) for the work on context data categories and privacy models for mobile data collection apps.

MobiSPC took place in August 2018 in Gran Canaria, Spain. It provided a leading edge, scholarly forum for researchers, engineers, and students alike to share their state-of-the art research and developmental work in the broad areas of pervasive computing and communications.

Reference

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, Spain.

Abstract

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.