Geospatial Streams Publish with Differential Privacy

  • Yiwen NieEmail author
  • Liusheng Huang
  • Zongfeng Li
  • Shaowei Wang
  • Zhenhua Zhao
  • Wei Yang
  • Xiaorong Lu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


Continuous releasing geospatial data is benefiting numerous areas, such as information push service, traffic scheduling and task assignment in crowdsourcing, etc. This kind of data is generated by people using positioning service in daily life, from which much sensitive information can be derived. Differential privacy is a strong theoretical and practical tool to provide protection; it has already been used on streams composing by datasets with fixed attributes. However, there is limited work on geospatial stream releasing with dynamic scopes for the requirement of accurate query. In this paper, aiming at achieving privacy protection of real-time geospatial synopsis with high utility, we introduce a method, called Realtime Geospatial Publish (RGP), which adopts differential privacy to geospatial stream with a new structure k-memo. We prove the privacy and utility of RGP theoretically and show the improvement of utility by experimental comparison with existing approaches on real datasets.


Differential privacy Geospatial partition Streams Location 


  1. 1.
    Amici, R., Bonola, M., Bracciale, L., Rabuffi, A., Loreti, P., Bianchi, G.: Performance assessment of an epidemic protocol in vanet using real traces. Procedia Comput. Sci. 40, 92–99 (2014)CrossRefGoogle Scholar
  2. 2.
    Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: Differential privacy for location-based systems. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 901–914. ACM (2013)Google Scholar
  3. 3.
    Bolot, J., Fawaz, N., Muthukrishnan, S., Nikolov, A., Taft, N.: Private decayed predicate sums on streams. In: ICDT, pp. 284–295. ACM (2013)Google Scholar
  4. 4.
    Chan, T.-H.H., Li, M., Shi, E., Xu, W.: Differentially private continual monitoring of heavy hitters from distributed streams. In: Fischer-Hübner, S., Wright, M. (eds.) PETS 2012. LNCS, vol. 7384, pp. 140–159. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31680-7_8 CrossRefGoogle Scholar
  5. 5.
    Cormode, G., Procopiuc, C., Srivastava, D., Shen, E., Yu, T.: Differentially private spatial decompositions. In: ICDE, pp. 20–31. IEEE (2012)Google Scholar
  6. 6.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). doi: 10.1007/11787006_1 CrossRefGoogle Scholar
  7. 7.
    Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Fan, L., Xiong, L.: Real-time aggregate monitoring with differential privacy. In: CIKM, pp. 2169–2173. ACM (2012)Google Scholar
  10. 10.
    Fan, L., Xiong, L., Sunderam, V.: Fast: differentially private real-time aggregate monitor with filtering and adaptive sampling. In: SIGMOD, pp. 1065–1068. ACM (2013)Google Scholar
  11. 11.
    Inan, A., Kantarcioglu, M., Ghinita, G., Bertino, E.: Private record matching using differential privacy. In: EDBT, pp. 123–134. ACM (2010)Google Scholar
  12. 12.
    Kellaris, G., Papadopoulos, S., Xiao, X., Papadias, D.: Differentially private event sequences over infinite streams. VLDB 7(12), 1155–1166 (2014)Google Scholar
  13. 13.
    McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: FOCS, pp. 94–103. IEEE (2007)Google Scholar
  14. 14.
    McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD, pp. 19–30. ACM (2009)Google Scholar
  15. 15.
    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
  16. 16.
    Qardaji, W., Yang, W., Li, N.: Differentially private grids for geospatial data. In: ICDE, pp. 757–768. IEEE (2013)Google Scholar
  17. 17.
    To, H., Ghinita, G., Shahabi, C.: A framework for protecting worker location privacy in spatial crowdsourcing. VLDB 7(10), 919–930 (2014)Google Scholar
  18. 18.
    Xiao, Y., Xiong, L.: Protecting locations with differential privacy under temporal correlations. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1298–1309. ACM (2015)Google Scholar
  19. 19.
    Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: SIGSPATIAL, pp. 99–108. ACM (2010)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Yiwen Nie
    • 1
    Email author
  • Liusheng Huang
    • 1
  • Zongfeng Li
    • 1
  • Shaowei Wang
    • 1
  • Zhenhua Zhao
    • 1
  • Wei Yang
    • 1
  • Xiaorong Lu
    • 1
  1. 1.University of Science and Technology of ChinaHefeiChina

Personalised recommendations