mFingerprint: Privacy-Preserving User Modeling with Multimodal Mobile Device Footprints

  • Haipeng Zhang
  • Zhixian Yan
  • Jun Yang
  • Emmanuel Munguia Tapia
  • David J. Crandall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)

Abstract

Mobile devices collect a variety of information about their environments, recording “digital footprints” about the locations and activities of their human owners. These footprints come from physical sensors such as GPS, WiFi, and Bluetooth, as well as social behavior logs like phone calls, application usage, etc. Existing studies analyze mobile device footprints to infer daily activities like driving/running/walking, etc. and social contexts such as personality traits and emotional states. In this paper, we propose a different approach that uses multimodal mobile sensor and log data to build a novel user modeling framework called mFingerprint that can effectively and uniquely depict users. mFingerprint does not expose raw sensitive information from the mobile device, e.g., the exact location, WiFi access points, or apps installed, but computes privacy-preserving statistical features to model the user. These descriptive features obscure sensitive information, and thus can be shared, transmitted, and reused with fewer privacy concerns. By testing on 22 users’ mobile phone data collected over 2 months, we demonstrate the effectiveness of mFingerprint in user modeling and identification, with our proposed statistics achieving 81% accuracy across 22 users over 10-day intervals.

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References

  1. 1.
    The Funf Open Sensing Framework, http://www.funf.org/
  2. 2.
    Angulo, J., Wästlund, E.: Exploring touch-screen biometrics for user identification on smart phones. In: Camenisch, J., Crispo, B., Fischer-Hübner, S., Leenes, R., Russello, G. (eds.) Privacy and Identity Management for Life. IFIP AICT, vol. 375, pp. 130–143. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Campbell, A.T., Eisenman, S.B., Lane, N.D., Miluzzo, E., Peterson, R.A., Lu, H., Zheng, X., Musolesi, M., Fodor, K., Ahn, G.-S.: The rise of people-centric sensing. IEEE Internet Computing 12(4), 12–21 (2008)CrossRefGoogle Scholar
  4. 4.
    Chittaranjan, G., Blom, J., Gatica-Perez, D.: Who’s who with big-five: Analyzing and classifying personality traits with smartphones. In: ISWC, pp. 29–36 (2011)Google Scholar
  5. 5.
    de Montjoye, Y.-A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: The privacy bounds of human mobility. Scientific Reports 3 (2013)Google Scholar
  6. 6.
    de Montjoye, Y.-A., Quoidbach, J., Robic, F., Pentland, A(S.): Predicting personality using novel mobile phone-based metrics. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 48–55. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Do, T.M.T., Gatica-Perez, D.: Contextual conditional models for smartphone-based human mobility prediction. In: Ubicomp, pp. 163–172 (2012)Google Scholar
  8. 8.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74–82 (2011)CrossRefGoogle Scholar
  9. 9.
    LiKamWa, R., Liu, Y., Lane, N.D., Zhong, L.: Moodscope: building a mood sensor from smartphone usage pattern. In: Mobisys (2013)Google Scholar
  10. 10.
    Lu, H., Pan, W., Lane, N.D., Choudhury, T., Campbell, A.T.: Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: MobiSys. ACM (2009)Google Scholar
  11. 11.
    Rachuri, K.K., Musolesi, M., Mascolo, C., Rentfrow, P.J., Longworth, C., Aucinas, A.: Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: Ubicomp, pp. 281–290 (2010)Google Scholar
  12. 12.
    Shin, C., Hong, J.-H., Dey, A.K.: Understanding and prediction of mobile application usage for smart phones. In: Ubicomp, pp. 173–182 (2012)Google Scholar
  13. 13.
    Weppner, J., Lukowicz, P.: Collaborative crowd density estimation with mobile phones. In: SenSys (2011)Google Scholar
  14. 14.
    Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semitri: a framework for semantic annotation of heterogeneous trajectories. In: EDBT, pp. 259–270 (2011)Google Scholar
  15. 15.
    Yan, Z., Yang, J., Tapia, E.M.: Smartphone bluetooth based social sensing. In: Ubicomp, pp. 95–98 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haipeng Zhang
    • 1
  • Zhixian Yan
    • 2
  • Jun Yang
    • 2
  • Emmanuel Munguia Tapia
    • 2
  • David J. Crandall
    • 1
  1. 1.School of Informatics & ComputingIndiana UniversityBloomingtonUSA
  2. 2.Samsung Research AmericaSan JoseUSA

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