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Context-aware applications stemming from diverse fields like mobile health,recommender systems, and mobile commerce potentially benefit from knowingaspects of the user's personality. As filling out personality questionnaires istedious, we propose the prediction of the user's personality from smartphonesensor and usage data. In order to collect data for researching therelationship between smartphone data and personality, we developed the Androidapp TYDR (Track Your Daily Routine) which tracks smartphone data and utilizespsychometric personality questionnaires. With TYDR, we track a larger varietyof smartphone data than similar existing apps, including metadata onnotifications, photos taken, and music played back by the user. For thedevelopment of TYDR, we introduce a general context data model consisting offour categories that focus on the user's different types of interactions withthe smartphone: physical conditions and activity, device status and usage, corefunctions usage, and app usage. On top of this, we develop the privacy modelPM-MoDaC specifically for apps related to the collection of mobile data,consisting of nine proposed privacy measures. We present the implementation ofall of those measures in TYDR. Although the utilization of the user'spersonality based on the usage of his or her smartphone is a challengingendeavor, it seems to be a promising approach for various types ofcontext-aware mobile applications.

Context Data Categories and Privacy Model for Mobile Data Collection Apps