Research on Clustering-Differential Privacy for Express Data Release

  • Tianying Chen
  • Haiyan Kang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10631)


With the rapid development of “Internet +”, the express delivery industry has exposed more privacy leakage problems. One way is the circulation of the express orders, and the other way is the express data release. For the second problem, this paper proposes a clustering-differential privacy preserving method combining with the theory of anonymization. Firstly, we use DBSCAN density clustering algorithm to initialize the original data set to achieve the first clustering. Secondly, in order to reduce the data generalization we combine the micro-aggregation technology to achieve the second clustering of the data set. Finally, adding Laplace noise to the clustering data record and correct the data that does not satisfy the differential privacy model to ensure the data availability. Simulation experiments show that the clustering-differential privacy preserving method can apply on the express data release, and it can keep higher data availability relative to the traditional differential privacy preserving.


Express data release Density clustering Micro-aggregation 



This work is partially supported by Natural Science Foundation of China No.61370139, Social Science Foundation of Beijing No.15JGB099 and High level talents cross training “real training plan” (scientific reserach) fund.


  1. 1.
    Wei, Q., Li, X.Y.: Express information protection application based on K- anonymity. Appl. Res. Comput. 31(2), 555–557 (2014)Google Scholar
  2. 2.
    Zhou, C.Q., Zhu, S.Z., Wang, S.S., Ao, L.N.: Research on privacy protection in express information management system. Logist. Eng. Manage. 37(12), 30–32 (2015)Google Scholar
  3. 3.
    Zhang, X.W., Li, H.K., Yang, Y.T., Sun, G.Z.: Logistic information privacy protection system based on encrypted QR code. Appl. Res. Comput. 33(11), 3455–3459 (2016)Google Scholar
  4. 4.
    Chai, R.M., Feng, H.H.: Efficient (K, L)-anonymous privacy protection based on clustering. Comput. Eng. 41(1), 139–142 (2015)Google Scholar
  5. 5.
    Chong, Z., Ni, W., Liu, T., et al.: A privacy-preserving data publishing algorithm for clustering application. J. Comput. Res. Dev. 47(12), 2083–2089 (2010)Google Scholar
  6. 6.
    Liu, X.Q., Li, Q.M.: Differentially private data release based on clustering anonymization. J. Commun. 37(5), 125–129 (2016)Google Scholar
  7. 7.
    Song, J., Xu, G.Y., Yao, R.P.: Anonymized data privacy protection method based on differential privacy. J. Comput. Appl. 36(10), 2753–2757 (2016)Google Scholar
  8. 8.
    Xiong, P., Zhu, T.Q., Wang, X.F.: A survey on differential privacy and applications. Chin. J. Comput. 37(1), 101–122 (2014)Google Scholar
  9. 9.
    Bhaskar, R., Laxman, S., Thakurta, A.: Discovering frequent patterns in sensitive data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, USA, pp. 503–512 (2010)Google Scholar
  10. 10.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Sarwar, B., Karypis, G., Konstan, J., et al.: Intembased collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  12. 12.
    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, Rhode Island, pp. 19–30. ACM (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information SecurityBeijing Information Science and Technology UniversityBeijingChina

Personalised recommendations