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.
We presented this work as a paper at MobiSPC 2018 and received the Best Paper award! You can find the full paper here (PDF).
The nine privacy measures we propose and implemented in TYDR are the following. See the paper for details.
(A) User Consent
(B) Let Users View Their Own Data
(C) Opt-out Option
(D) Approval by Ethics Commission / Review Board
(E) Random Identifiers
(F) Data Anonymization
(G) Utilize Permission System
(H) Secured Transfer
(I )Identifying Individual Users Without Linking to Their Collected Data
