JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2018, Vol. 53 ›› Issue (9): 12-22.doi: 10.6040/j.issn.1671-9352.0.2017.418

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Differential privacy partitioning algorithm based on adaptive density grids

YAN Yan1, 2, HAO Xiao-hong1*   

  1. 1. School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China;
    2. School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, Gansu, China
  • Received:2017-08-21 Online:2018-09-20 Published:2018-09-10

Abstract: In order to balance the influence of noise error and uniform hypothesis error for the two-dimensional partitioning publishing, a new hierarchical differential privacy partitioning algorithm DP-ADG is proposed. Firstly, the position space is clustered to form the density adaptive grids in the first layer. Then in the second layer, different partitioning methods are adopted for different density blocks. The noise error introduced by a large number of null nodes is avoided while reducing the uniform hypothesis error. While using the hierarchical partitioning strategy, different Laplace noise of different privacy budgets is added to the results of two phases according to the sequential composition of differential privacy, in order to realize the overall ε differential privacy protection for the publishing data. Experimental results show that the algorithm has good effect on improving the accuracy of range counting query, saving unnecessary spatial decomposition process, as well as improving the efficiency of the algorithm.

Key words: location big data, differential privacy, spatial decomposition, density adaptive grids

CLC Number: 

  • TP309
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