Research on Clustering-Differential Privacy for Express Data Release

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10631)

Abstract

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.

Keywords

Express data release Density clustering Micro-aggregation 

Notes

Acknowledgments

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.

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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

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