Skip to main content
SpringerLink
Log in

Privacy Preservation and Inference with Minimal Mobility Information

  • Conference paper
  • First Online:
Book cover Information Management and Big Data (SIMBig 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1070))

Included in the following conference series:

  • 635 Accesses

Abstract

There is much debate about the challenge to anonymize a large amount of information obtained in big data scenarios. Besides, it is even harder considering inferences from data may be used as additional adversary knowledge. This is the case of geo-located data, where the Points of Interest (POIs) may have additional information that can be used to link them to a user’s real identity. However, in most cases, when a model of the raw data is published, this processing protects up to some point the privacy of the data subjects by minimizing the published information. In this paper, we measure the privacy obtained by the minimization of the POIs published when we apply the Mobility Markov Chain (MMC) model, which extracts the most important POIs of an individual. We consider the gender inferences that an adversary may obtain from publishing the MMC model together with additional information such as the gender or age distribution of each POI, or the aggregated gender distribution of all the POIs visited by a data subject. We measure the unicity obtained after applying the MMC model, and the probability that an adversary that knows some POIs in the data before processing may be able to link them with the POIs published after the MMC model. Finally, we measure the anonymity lost when adding the gender attribute to the side knowledge of an adversary that has access to the MMC model. We test our algorithms on a real transaction database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    VISA Merchant Category Classification (MCC) codes directory.

References

  1. Bi, B., Shokouhi, M., Kosinski, M., Graepel, T.: Inferring the demographics of search users: social data meets search queries. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 131–140. New York (2013)

    Google Scholar 

  2. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  3. Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 1082–1090. New York (2011)

    Google Scholar 

  4. Danezis, G., et al.: Privacy and data protection by design - from policy to engineering. Technical report, ENISA (2015)

    Google Scholar 

  5. Fiore, M., et al.: Privacy of trajectory micro-data: a survey. CoRR abs/1903.12211 (2019)

    Google Scholar 

  6. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  7. Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Show me how you move and I will tell you who you are. Trans. Data Priv. 4(2), 103–126 (2011)

    MathSciNet  Google Scholar 

  8. Gambs, S., Killijian, M.O., Núñez del Prado Cortez, M.: Next place prediction using mobility Markov chains. In: Proceedings of the First Workshop on Measurement, Privacy, and Mobilit, Bern, Switzerland, vol. 3, pp. 1–6, April 2012

    Google Scholar 

  9. Gambs, S., Killijian, M.O., Núñez del Prado Cortez, M.: GEPETO: a geoprivacy-enhancing toolkit. In: 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops, pp. 1071–1076. IEEE (2010)

    Google Scholar 

  10. Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: De-anonymization attack on geolocated data. J. Comput. Syst. Sci. 80(8), 1597–1614 (2014)

    Article  MathSciNet  Google Scholar 

  11. Hastie, T., Rosset, S., Zhu, J., Zou, H.: Multi-class AdaBoost. Stat. Interface 2(3), 349–360 (2009)

    Article  MathSciNet  Google Scholar 

  12. Hu, J., Zeng, H.J., Li, H., Niu, C., Chen, Z.: Demographic prediction based on user’s browsing behavior. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 151–160. ACM, New York (2007)

    Google Scholar 

  13. Mayer, J., Mutchler, P., Mitchell, J.C.: Evaluating the privacy properties of telephone metadata. Proc. Nat. Acad. Sci. 113(20), 5536–5541 (2016)

    Article  Google Scholar 

  14. de Montjoye, Y.A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3, 1376 (2013)

    Article  Google Scholar 

  15. de Montjoye, Y.A., Radaelli, L., Singh, V.K., Pentland, A.S.: Unique in the shopping mall: On the reidentifiability of credit card metadata. Science 347(6221), 536–539 (2015). https://doi.org/10.1126/science.1256297

    Article  Google Scholar 

  16. Riederer, C., Kim, Y., Chaintreau, A., Korula, N., Lattanzi, S.: Linking users across domains with location data: theory and validation. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, pp. 707–719, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2016)

    Google Scholar 

  17. Salas, J., Domingo-Ferrer, J.: Some basics on privacy techniques, anonymization and their big data challenges. Math. Comput. Sci. 12(3), 263–274 (2018). https://doi.org/10.1007/s11786-018-0344-6

    Article  MathSciNet  MATH  Google Scholar 

  18. Salas, J., Megías, D., Torra, V.: SwapMob: swapping trajectories for mobility anonymization. In: Domingo-Ferrer, J., Montes, F. (eds.) PSD 2018. LNCS, vol. 11126, pp. 331–346. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99771-1_22

    Chapter  Google Scholar 

  19. Scellato, S., Noulas, A., Lambiotte, R., Mascolo, C.: Socio-spatial properties of online location-based social networks. In: Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, 17–21 July 2011 (2011)

    Google Scholar 

  20. Wang, P., Guo, J., Lan, Y., Xu, J., Cheng, X.: Your cart tells you: inferring demographic attributes from purchase data. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 173–182. ACM (2016)

    Google Scholar 

  21. Weinsberg, U., Bhagat, S., Ioannidis, S., Taft, N.: BlurMe: inferring and obfuscating user gender based on ratings. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 195–202. New York (2012)

    Google Scholar 

  22. Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: SeMiTri: a framework for semantic annotation of heterogeneous trajectories. In: Proceedings of the 14th International Conference on Extending Database Technology, EDBT/ICDT 2011, pp. 259–270. ACM, New York (2011)

    Google Scholar 

  23. Zhong, E., Tan, B., Mo, K., Yang, Q.: User demographics prediction based on mobile data. Pervasive Mob. Comput. 9(6), 823–837 (2013)

    Article  Google Scholar 

  24. Zhong, Y., Yuan, N.J., Zhong, W., Zhang, F., Xie, X.: You are where you go: inferring demographic attributes from location check-ins. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM 2015, pp. 295–304. ACM, New York (2015)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the Spanish Government, in part under Grant RTI2018-095094-B-C22 “CONSENT”, and in part under Grant TIN2014-57364-C2-2-R “SMARTGLACIS.”

Author information

Authors and Affiliations

  1. Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, Spain

    Julián Salas

  2. Universidad del Pacífico, Av. Salaverry 2020, Lima, Peru

    Miguel Nunez-del-Prado

  3. CYBERCAT-Center for Cybersecurity Research of Catalonia, Barcelona, Spain

    Julián Salas

Authors
  1. Julián Salas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel Nunez-del-Prado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miguel Nunez-del-Prado .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. University of A Coruña, A Coruña, Spain

    Nelly Condori-Fernandez

  3. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Salas, J., Nunez-del-Prado, M. (2020). Privacy Preservation and Inference with Minimal Mobility Information. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-46140-9_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46139-3

  • Online ISBN: 978-3-030-46140-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation