Skip to main content
Download book PDF
View expanded cover

IFIP Annual Conference on Data and Applications Security and Privacy

DBSec 2013: Data and Applications Security and Privacy XXVII pp 33–48Cite as

Differentially Private Multi-dimensional Time Series Release for Traffic Monitoring

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7964)

Abstract

Sharing real-time traffic data can be of great value to understanding many important phenomena, such as congestion patterns or popular places. To this end, private user data must be aggregated and shared continuously over time with data privacy guarantee. However, releasing time series data with standard differential privacy mechanism can lead to high perturbation error due to the correlation between time stamps. In addition, data sparsity in the spatial domain imposes another challenge to user privacy as well as utility. To address the challenges, we propose a real-time framework that guarantees differential privacy for individual users and releases accurate data for research purposes. We present two estimation algorithms designed to utilize domain knowledge in order to mitigate the effect of perturbation error. Evaluations with simulated traffic data show our solutions outperform existing methods in both utility and computation efficiency, enabling real-time data sharing with strong privacy guarantee.

Keywords

  • Traffic Monitoring
  • Multi-Dimensional Time-Series
  • Differential Privacy

Download conference paper PDF

References

  1. Blum, A., Ligett, K., Roth, A.: A learning theory approach to non-interactive database privacy. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 609–618. ACM, New York (2008)

    Google Scholar 

  2. Brinkhoff, T.: A framework for generating network-based moving objects. Geoinformatica 6(2), 153–180 (2002)

    MATH  CrossRef  Google Scholar 

  3. Hubert Chan, T.-H., Shi, E., Song, D.: Private and continual release of statistics. In: Abramsky, S., Gavoille, C., Kirchner, C., Meyer auf der Heide, F., Spirakis, P.G. (eds.) ICALP 2010. LNCS, vol. 6199, pp. 405–417. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  4. Cormode, G., Procopiuc, C., Srivastava, D., Shen, E., Yu, T.: Differentially private spatial decompositions. In: Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, pp. 20–31. IEEE Computer Society, Washington, DC (2012)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  6. Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N.: Differential privacy under continual observation. In: Proceedings of the 42nd ACM Symposium on Theory of Computing, pp. 715–724. ACM, New York (2010)

    Google Scholar 

  7. Dwork, C., Naor, M., Reingold, O., Rothblum, G.N., Vadhan, S.: On the complexity of differentially private data release: efficient algorithms and hardness results, pp. 381–390. ACM, New York (2009)

    Google Scholar 

  8. Fan, L., Xiong, L.: Adaptively sharing time-series with differential privacy. CoRR, abs/1202.3461 (2012)

    Google Scholar 

  9. Fan, L., Xiong, L.: Real-time aggregate monitoring with differential privacy. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 2169–2173. ACM, New York (2012)

    Google Scholar 

  10. Hay, M., Rastogi, V., Miklau, G., Suciu, D.: Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB Endow. 3(1-2), 1021–1032 (2010)

    Google Scholar 

  11. Li, C., Miklau, G.: An adaptive mechanism for accurate query answering under differential privacy. Proc. VLDB Endow. 5(6), 514–525 (2012)

    Google Scholar 

  12. McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis, vol. 53, pp. 89–97. ACM, New York (2010)

    Google Scholar 

  13. Rastogi, V., Nath, S.: Differentially private aggregation of distributed time-series with transformation and encryption. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 735–746. ACM, New York (2010)

    CrossRef  Google Scholar 

  14. Xiao, X., Bender, G., Hay, M., Gehrke, J.: ireduct: differential privacy with reduced relative errors. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 229–240. ACM, New York (2011)

    CrossRef  Google Scholar 

  15. Xiao, X., Wang, G., Gehrke, J.: Differential privacy via wavelet transforms. IEEE Trans. on Knowl. and Data Eng. 23(8), 1200–1214 (2011)

    CrossRef  Google Scholar 

  16. Xiao, Y., Xiong, L., Yuan, C.: Differentially private data release through multidimensional partitioning. In: Jonker, W., Petković, M. (eds.) SDM 2010. LNCS, vol. 6358, pp. 150–168. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  17. Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G.: Differentially private histogram publication. In: Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, pp. 32–43. IEEE Computer Society, Washington, DC (2012)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Emory University, Atlanta, GA, 30322, USA

    Liyue Fan, Li Xiong & Vaidy Sunderam

Authors
  1. Liyue Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vaidy Sunderam
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, 1455 de Maisonneuve Blvd. West, H3G 1M8, Montreal, QU, Canada

    Lingyu Wang

  2. Department of Computer Science, DHA, Lahore Cantt, Lahore University of Management Sciences (LUMS), 54792, Lahore, Pakistan

    Basit Shafiq

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 IFIP International Federation for Information Processing

About this paper

Cite this paper

Fan, L., Xiong, L., Sunderam, V. (2013). Differentially Private Multi-dimensional Time Series Release for Traffic Monitoring. In: Wang, L., Shafiq, B. (eds) Data and Applications Security and Privacy XXVII. DBSec 2013. Lecture Notes in Computer Science, vol 7964. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39256-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39256-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39255-9

  • Online ISBN: 978-3-642-39256-6

  • eBook Packages: Computer ScienceComputer Science (R0)