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Anonymous smartphone data collection: factors influencing the users’ acceptance in mobile crowd sensing

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Abstract

Mobile crowd sensing (MCS) assumes a collaborative effort from mobile smartphone users to sense and share their data needed to fulfill a given MCS objective (e.g., modeling of urban traffic or wellness index of a community). In this paper, we investigate the user’s perception of anonymity in MCS and factors influencing it. We conducted a 4-week extensive smartphone user study to fulfill three main objectives. (1) Understand if users prefer to share data anonymously or not anonymously. (2) Investigate the possible factors influencing the difference between these two modalities, considering: (a) users’ sharing attitude, (b) shared data kind and (c) users’ intimacy when data are shared (we defined intimacy as the users’ perception of their context with respect to place, number and kind of people around them). (3) Identify further users’ personal factors influencing their perception of anonymity via multiple interviews along the user study. In the results, we show that data are shared significantly more when anonymously collected. We found that the shared data kind is the factor significantly contributing to this difference. Additionally, users have a common way to perceive anonymity and its effectiveness. To ensure the success of anonymization algorithms in the context of MCS systems, we highlight which issues the researchers developing these algorithms should carefully consider. Finally, we argue about new research paths to better investigate the user perception of anonymity and develop anonymous MCS systems that users are more likely to trust based on our findings.

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Acknowledgments

Our research has been supported by the Swiss National Science Foundation PCS-OBEY and MIQModel projects, and the European Ambient Assisted Living MyGuardian, ANIMATE and CoME Projects.

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Authors and Affiliations

  1. Faculty of Economics and Management, Information Science Institute, University of Geneva, 1227, Carouge, Switzerland

    Mattia Gustarini & Katarzyna Wac

  2. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, 15213-3891, USA

    Anind K. Dey

Authors
  1. Mattia Gustarini
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  2. Katarzyna Wac
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  3. Anind K. Dey
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Correspondence to Mattia Gustarini.

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Gustarini, M., Wac, K. & Dey, A.K. Anonymous smartphone data collection: factors influencing the users’ acceptance in mobile crowd sensing. Pers Ubiquit Comput 20, 65–82 (2016). https://doi.org/10.1007/s00779-015-0898-0

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  • DOI: https://doi.org/10.1007/s00779-015-0898-0

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