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Clustering-Anonymization-Based Differential Location Privacy Preserving Protocol in WSN

  • Ren-ji HuangEmail author
  • Qing Ye
  • Mo-Ci Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)

Abstract

Playing a vital role in the period of big data and intelligent life, wireless sensor networks (WSN) transmits a bulk of data. Location information as the vital data in transmission is widely used in detecting and routing for the network. With the big data mining and analysis, the security of location and data privacy in WSN faces great challenges. To the problem of active attacking like node capture in wireless sensor network node location privacy, existing location privacy preserving protocols are analyzed and Differential Location Privacy protocol based on Clustering Anonymization is proposed. By sensor nodes clustering using genetic clustering algorithm, the individual location is hidden in the statistical location information of the group. The Laplace Mechanism is also added to the protocol to realize differential location privacy. Node location privacy in WSN is preserved as well as privacy preserving budget is saved. The result of theoretical analysis and contrastive simulation experience shows that the protocol can be useful.

Keywords

Wireless Sensor Network Location privacy preserving Differential privacy Clustering anonymization 

References

  1. 1.
    Li-min, S., Yuang, Z., Qing-chao, L., et al.: Fundamentals of wireless sensor networks, Tsinghua University, Beijing, pp. 3–13 (2014)Google Scholar
  2. 2.
    Feng, H., Hong-wei, C., Zong-ke, J., Ti-jiang, S., et al.: Review of recent progress on wireless sensor network applications. J. Comput. Res. Dev. 47(S2), 81–87 (2010)Google Scholar
  3. 3.
    Ynag, X., Ma, K.: Evolution of wireless sensor network security. In: World Automation Congress, pp. 1–5 (2016)Google Scholar
  4. 4.
    Peng, H., Chen, H., Zhang, X.-Y., et al.: Location privacy preservation in wireless sensor networks. J. Softw. 26(3), 617–639 (2015)Google Scholar
  5. 5.
    Cheng, L., Wang, Y., Wu, H., et al.: Non-parametric location estimation in rough wireless environments for wireless sensor network. Sens. Actuators, A 224, 57–64 (2015)CrossRefGoogle Scholar
  6. 6.
    Groat, M., He, W., Forrest, S.: KIPDA: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks. In: Proceedings of the INFOCOM, pp. 2024–2032. IEEE (2011)Google Scholar
  7. 7.
    Babar, S.A., Prasad, N., et al.: Proposed embedded security framework for Internet of Things (IoT). In: The Wireless Communication, Vehicular Technology, pp. 1–5 (2011)Google Scholar
  8. 8.
    Ozturk, C., Zhang, Y., Trappe, W.: Source-location privacy in energy-constrained sensor network routing. In: The 2nd ACM Workshop on Security of Ad Hoc and Sensor Networks, pp. 88–93 (2004)Google Scholar
  9. 9.
    Juan, C., Bin-xing, F., Li-hua, Y., et al.: A source-location privacy preservation protocol in wireless sensor networks using source- based restricted flooding. Chin. J. Comput. 33(9), 1736–1747 (2010)CrossRefGoogle Scholar
  10. 10.
    Fu-zhi, X.: Research and Implementation of Location Privacy Protection Solution in Wireless Sensor Network. University of Electronic Science and Technology of China, Chengdu (2014)Google Scholar
  11. 11.
    Jiangnan, Z., Chun-liang, C.: A Scheme to protect the source location privacy in wireless sensor networks. Chin. J. Sens. Actuators 29(9), 1405–1409 (2016)Google Scholar
  12. 12.
    Shao, M., Yang, Y., Zhu, S., et al.: Towards statistically strong source anonymity for sensor networks. In: The 27th Conference on Computer Communications, pp. 51–55 (2008)Google Scholar
  13. 13.
    Xiao-yan, H.: Research on the source-location privacy in wireless sensor networks against a global eavesdropper. Central South University, Changsha (2014)Google Scholar
  14. 14.
    Xiaoguang, N., Chuan-bo, W., Ya-lan, Y.: Energy-consumption-balanced efficient source-location privacy preserving protocol in WSN. J. Commun. 37(4), 23–33 (2016)Google Scholar
  15. 15.
    Ji-xiang, S.: Modern Pattern Recognition, pp. 16–40. Higher Education Press, Beijing (2016)Google Scholar
  16. 16.
    Kumar, K.A, Rangan, C.P.: Privacy preserving DBSCAN algorithm for clustering. In: Proceedings of Advanced Data Mining and Applications, Third International Conference, ADMA 2007, Harbin, China, 6–8 August 2007, DBLP, pp. 57-68 (2007)Google Scholar
  17. 17.
    Xiao-qian, L., Qian-mu, L.: Differentially private data release based on clustering anonymization. J. Commun. 37(5), 125–129 (2016)Google Scholar
  18. 18.
    Min-rui, C., Hui-hui, F.: Efficient (K,L)-anonymous privacy protection based on clustering. Comput. Eng. 41(01), 139–142 + 163 (2015)Google Scholar
  19. 19.
    Zhao-yan, M.: Research on anonymous privacy protection algorithm based on clustering, Xi’an University of Technology (2017)Google Scholar
  20. 20.
    Dwork, C.: Differential privacy. In: Proceedings of the 33rd International Colloquium on Automata, Languages and Programming, Venice, Italy, pp. 1–12 (2006)Google Scholar
  21. 21.
    Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)CrossRefGoogle Scholar
  22. 22.
    Ping, X., Tian-qing, Z., Xiao-feng, W.: A survey on differential privacy and applications. Chin. J. Comput. 37(1), 101–122 (2014)Google Scholar
  23. 23.
    Dwork, C., McSherry, F., Nissim, K., et al.: Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd Conference on Theory of Cryptography, New York, USA, pp. 265–284 (2006)Google Scholar
  24. 24.
    Hardt, M., Rothblum, G.N.: A multiplicative weights mechanism for privacy-preserving data analysis. In: IEEE, Symposium on Foundations of Computer Science, pp. 61–70. IEEE Computer Society (2010)Google Scholar
  25. 25.
    Xue-jun, Z., Xiao-lin, G., Jing-hua, J.: A user-centric location privacy-preserving method with differential perturbation for location-based services. J. Xi’an Jiao-tong Univ. 50(12), 79–86 (2016)MathSciNetGoogle Scholar
  26. 26.
    Wei-jun, Z., Qing-huang, Y., Wei-dong, Y., et al.: Trajectory privacy preserving based on statistical differential privacy. J. Comput. Res. Develop. 54(12), 2825–2832 (2017)Google Scholar
  27. 27.
    Huo-wen, J., Guo-sun, Z., Ke-kun, H.: A graph-clustering anonymity method implemented by genetic algorithm for privacy-preserving. J. Comput. Res. Develop. 53(10), 2354–2364 (2016)Google Scholar
  28. 28.
    Mcsherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: The 2009 ACM SIGMOD International Conference on Management of Data. Providence, pp. 19–30. ACM, Rhode Island (2009)Google Scholar
  29. 29.
    Yu-hong, W.: General overview on clustering algorithms. Comput. Sci. 42(S1), 491–499 + 524 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information SecurityNaval University of EngineeringWuhanChina

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