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What data are smartphone users willing to share with researchers?

Designing and evaluating a privacy model for mobile data collection apps

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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 track your daily routine (TYDR), which tracks and records smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than many other existing apps, including metadata on notifications, photos taken, and music played back by the user. Based on 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 developed the Privacy Model for Mobile Data Collection Applications (PM-MoDaC) specifically tailored for apps that are related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Our experimental evaluation is based on data collected with TYDR during a two-month period. We find evidence that our users accept our proposed privacy model. Based on data about granting TYDR all or no Android system permissions, we find evidence that younger users tend to be less willing to share their data (average age of 30 years compared to 35 years). We also observe that female users tend to be less willing to share data compared to male users. We did not find any evidence that education or personality traits are a factor related to data sharing. TYDR users score higher on the personality trait openness to experience than the average of the population, which we assume to be evidence that the type of app influences the user base it attracts in terms of average personality traits.

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Notes

  1. We consider DS2 instead of DS1 here, as DS1 contains several users that probably never used or intended to use the app; see Note on DS1 above.

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Acknowledgements

We are grateful for the support provided by Daniel Lenz, Sakshi Bansal, Marcel Müller, Soumya Siladitya Mishra, and Sarjo Das. We also thank all TYDR users.

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Authors and Affiliations

  1. Service-centric Networking, Telekom Innovation Laboratories, Technische Universität Berlin, Berlin, Germany

    Felix Beierle

  2. Technische Universität Berlin, Berlin, Germany

    Vinh Thuy Tran

  3. Department of Psychology, University of Zurich, Zurich, Switzerland

    Mathias Allemand

  4. Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany

    Patrick Neff & Winfried Schlee

  5. Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria

    Thomas Probst

  6. Department of Psychology, University of Kassel, Kassel, Germany

    Johannes Zimmermann

  7. Institute of Databases and Information Systems, Ulm University, Ulm, Germany

    Rüdiger Pryss

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  1. Felix Beierle
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Correspondence to Felix Beierle.

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This work was done in the context of project DYNAMIC (http://www.dynamic-project.de) (grant No 01IS12056), which is funded as part of the Software Campus initiative by the German Federal Ministry of Education and Research (BMBF)

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Beierle, F., Tran, V.T., Allemand, M. et al. What data are smartphone users willing to share with researchers?. J Ambient Intell Human Comput 11, 2277–2289 (2020). https://doi.org/10.1007/s12652-019-01355-6

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