Fast Estimation of Privacy Risk in Human Mobility Data

  • Roberto PellungriniEmail author
  • Luca Pappalardo
  • Francesca Pratesi
  • Anna Monreale
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10489)


Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual’s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods.



Funded by the European project SoBigData (Grant Agreement 654024).


  1. 1.
    Abul, O., Bonchi, F., Nanni, M.: Never Walk Alone: Uncertainty for anonymity in moving objects databases. In: ICDE, pp. 376–385 (2008)Google Scholar
  2. 2.
    Armando, A., Bezzi, M., Metoui, N., Sabetta, A.: Risk-based privacy-aware information disclosure. Int. J. Secur. Softw. Eng. 6(2), 70–89 (2015)CrossRefGoogle Scholar
  3. 3.
    Cormode, G., Procopiuc, C.M., Srivastava, D., Tran, T.T.L.: Differentially private summaries for sparse data. In: ICDT 2012, pp. 299–311 (2012)Google Scholar
  4. 4.
    Deng, M., Wuyts, K., Scandariato, R., Preneel, B., Joosen, W.: A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements. Requir. Eng. 16(1), 3–32 (2011)CrossRefGoogle Scholar
  5. 5.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). doi: 10.1007/11681878_14 CrossRefGoogle Scholar
  6. 6.
    Eagle, N., Pentland, A.S.: Eigenbehaviors: identifying structure in routine. Behav. Ecol. Sociobiol. 63(7), 1057–1066 (2009)CrossRefGoogle Scholar
  7. 7.
    Monreale, A., Wang, W.H., Pratesi, F., Rinzivillo, S., Pedreschi, D., Andrienko, G., Andrienko, N.: Privacy-preserving distributed movement data aggregation. In: Vandenbroucke, D., Bucher, B., Crompvoets, J. (eds.) Geographic Information Science at the Heart of Europe. LNGC, pp. 225–245. Springer, Cham (2013). doi: 10.1007/978-3-319-00615-4_13 CrossRefGoogle Scholar
  8. 8.
    Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., Barabasi, A.-L.: Returners and explorers dichotomy in human mobility. Nat. Commun. 6, 1–8 (2015)CrossRefGoogle Scholar
  9. 9.
    Pappalardo, L., Simini, F.: Modelling spatio-temporal routines in human mobility. CoRR abs/1607.05952 (2016)Google Scholar
  10. 10.
    Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., Giannotti, F.: An analytical framework to nowcast well-being using mobile phone data. Int. J. Data Sci. Anal. 2(1), 75–92 (2016)CrossRefGoogle Scholar
  11. 11.
    Pratesi, F., Monreale, A., Trasarti, R., Giannotti, F., Pedreschi, D., Yanagihara, T.: PRISQUIT: a system for assessing privacy risk versus quality in data sharing. Technical report 2016-TR-043. ISTI - CNR, Pisa, Italy. FriNov20162291 (2016)Google Scholar
  12. 12.
    Rubinstein, I.S.: Big Data: The end of privacy or a new beginning? International Data Privacy Law (2013)Google Scholar
  13. 13.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (Abstract). In: PODS, p. 188 (1998)Google Scholar
  14. 14.
    Song, C., Koren, T., Wang, P., Barabasi, A.-L.: Modelling the scaling properties of human mobility. Nat. Phys. 6(10), 818–823 (2010)CrossRefGoogle Scholar
  15. 15.
    Song, Y., Dahlmeier, D., Bressan, S.: Not so unique in the crowd: a simple and effective algorithm for anonymizing location data. In: PIR@SIGIR, pp. 19–24 (2014)Google Scholar
  16. 16.
    Stoneburner, G., Goguen, A., Feringa, A.: Risk Management Guide for Information Technology Systems: Recommendations of the National Institute of Standards and Technology. NIST special publication, vol. 800 (2002)Google Scholar
  17. 17.
    Terrovitis, M., Mamoulis, N.: Privacy preservation in the publication of trajectories. In: MDM, pp. 65–72 (2008)Google Scholar
  18. 18.
    Trabelsi, S., Salzgeber, V., Bezzi, M., Montagnon, G.: Data disclosure risk evaluation. In: CRiSIS 2009, pp. 35–72 (2009)Google Scholar
  19. 19.
    Williams, N.E., Thomas, T.A., Dunbar, M., Eagle, N., Dobra, A.: Measures of human mobility using mobile phone records enhanced with GIS data. PLoS ONE 10(7), 1–16 (2015)Google Scholar
  20. 20.
    Yarovoy, R., Bonchi, F., Lakshmanan, L.V.S., Wang, W.H.: Anonymizing moving objects: how to hide a MOB in a crowd? In: Proceeding of the EDBT Conference, pp. 72–83 (2009)Google Scholar
  21. 21.
    Ji, S., Li, W., Srivatsa, M., He, J.S., Beyah, R.: Structure based data de-anonymization of social networks and mobility traces, pp. 237–254 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Roberto Pellungrini
    • 1
    Email author
  • Luca Pappalardo
    • 1
    • 2
  • Francesca Pratesi
    • 1
    • 2
  • Anna Monreale
    • 1
  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly
  2. 2.ISTI-CNRPisaItaly

Personalised recommendations