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An Efficient Model and Algorithm for Privacy-Preserving Trajectory Data Publishing

  • Songyuan LiEmail author
  • Hong Shen
  • Yingpeng Sang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 931)

Abstract

Since Abul et al. first proposed the k-anonymity based privacy protection for trajectory data, the researchers have proposed a variety of trajectory privacy-preserving methods, these methods mainly adopt the static anonymity algorithm, which directly anonymize processing and data publishing after initialization. They do not take into account the real application scenarios of moving trajectory data. The objective of this paper is to realize the dynamic data publishing of high dimensional vehicle trajectory data privacy protection under (k, δ) security constraints. First of all, we propose the partition storage and calculation for trajectory data. According to the spatial and temporal characteristics of vehicle trajectory data, we choose the sample point \( (x_{2} ,y_{2} ,t) \) at the time \( t_{i} \) as partition fields, partition storage of the trajectory data according to the time sequence and the location of the running vehicle is \( Region\left( {m,n} \right)\_\left( {x_{i} ,y_{i} ,t_{i} } \right) \). The computation of data scanning in trajectory data clustering and privacy processing is reduced greatly through this method. Secondly, the dynamic clustering method is used to cluster the regional data. According to the characteristics of the vehicle trajectory data, \( \left( {x_{i} ,y_{i} ,t_{m - n} } \right) \) as the release data identifier, trajectory attributes of the vehicle as the sensitive attributes, we use Data Partitioning and Cartesian Product (DPCP) method to cluster trajectory data under the (k, δ) security constraints. Thirdly, the anonymization function \( f_{DPCP} \) is used to preserve the privacy of clustering trajectory data. In each sampling time slice, \( f_{DPCP} \) function is used to generalize the location data in the grouping. Through the continuous algorithm optimization and the experimental verification of real trajectory data, this model and algorithm can effectively protect privacy under the security constraint of (k, δ). By means of data simulation and data availability evaluation, the data processed by the anonymization method has a certain usability under the threshold of δ. At the same time, the experimental results are compared with the classical NWA algorithm, and DLBG, the method in this paper have been proved to be advanced in time cost and data availability evaluation.

References

  1. 1.
    Sweeney, L.: k-Anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness and Knowl.-Based Syst. 10(5), 1–14 (2002)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Machanavajjhala, A., et al.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1), 3 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Li, N., Li, T., Venkatasubramanian, S.: t-Closeness: privacy beyond k-Anonymity and l-Diversity. In: IEEE, International Conference on Data Engineering, pp. 106–115. IEEE (2007)Google Scholar
  4. 4.
    Tramp, S., Frischmuth, P., Arndt, N., Ermilov, T., Auer, S.: Weaving a distributed, semantic social network for mobile users. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 200–214. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21034-1_14CrossRefGoogle Scholar
  5. 5.
    Li, F., Gao, F., Yao, L., Pan, Yu.: Privacy preserving in the publication of large-scale trajectory databases. In: Wang, Yu., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds.) BigCom 2016. LNCS, vol. 9784, pp. 367–376. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42553-5_31CrossRefGoogle Scholar
  6. 6.
    Liberti, L., et al.: Euclidean distance geometry and applications. Quant. Biol. 56(1), 3–69 (2012)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Zhu, T., et al.: Faces of the cone of Euclidean distance matrices: characterizations, structure and induced geometry. Linear Algebr. Its Appl. 408(1), 1–13 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Abul, O., Bonchi, F., Nanni, M.: Never walk alone: uncertainty for anonymity in moving objects databases. In: IEEE, International Conference on Data Engineering. IEEE Computer Society, pp. 376–385 (2008)Google Scholar
  9. 9.
    Basu, A., et al.: A privacy risk model for trajectory data. In: Zhou, J., Gal-Oz, N., Zhang, J., Gudes, E. (eds.) IFIPTM 2014. IAICT, vol. 430, pp. 125–140. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-43813-8_9CrossRefGoogle Scholar
  10. 10.
    Sun, X., Sun, L., Wang, H.: Extended k-anonymity models against sensitive attribute disclosure. Comput. Commun. 34(4), 526–535 (2011)CrossRefGoogle Scholar
  11. 11.
    Poulis, G., et al.: Distance-based k^m-anonymization of trajectory data. In: IEEE, International Conference on Mobile Data Management, pp. 57–62. IEEE (2013)Google Scholar
  12. 12.
    Xin, Y., Xie, Z.Q., Yang, J.: The privacy preserving method for dynamic trajectory releasing based on adaptive clustering. Inf. Sci. 378, 131–143 (2017)CrossRefGoogle Scholar
  13. 13.
    Geometry and applications. Quant. Biol. 56(1), 3–69 (2012) Google Scholar
  14. 14.
    Kiran, P., Kavya, N.P.: A survey on methods, attacks and metric for privacy preserving data publishing. Int. J. Comput. Appl. 53(18), 20–28 (2013)Google Scholar
  15. 15.
    Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)CrossRefGoogle Scholar
  16. 16.
    Gehrke, J., Kifer, D., Machanavajjhala, A.: ℓ-Diversity. In: van Tilborg H.C.A., Jajodia S. (eds.) Encyclopedia of Cryptography and Security, pp. 707–709. Springer, Boston (2011).  https://doi.org/10.1007/978-1-4419-5906-5
  17. 17.
    Bonchi, F., Lakshmanan, L.V.S., Wang, H.: Trajectory anonymity in publishing personal mobility data. ACM Sigkdd Explor. Newsl. 13(1), 30–42 (2011)CrossRefGoogle Scholar
  18. 18.
    Shin, H., et al. Ensuring Privacy and Security for LBS through Trajectory Partitioning. In: Eleventh International Conference on Mobile Data Management IEEE Computer Society, pp. 224–226 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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