Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (11): 64-71.doi: 10.16180/j.cnki.issn1007-7820.2022.11.010

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Differential Privacy Fuzzy Clustering Location Protection Method

LIN Jing,HU Demin,WANG Kuihao   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2021-03-28 Online:2022-11-15 Published:2022-11-11
  • Supported by:
    National Natural Science Foundation of China(61170277);National Natural Science Foundation of China(61472256);Key Project of Scientific Research and Innovation of Shanghai Municipal Education Commission(12zz137);Shanghai First Class Discipline Construction Project(S1201YLXK)


In view of the problems of sensitive initial value, inapplicability of discrete data and large error in the existing differential privacy clustering location protection methods, a differential privacy fuzzy clustering location protection method is proposed in this study. Firstly, the points are mapped to the feature space by Gaussian kernel function, and the computational efficiency is significantly improved due to the relatively small amount of computation. Secondly, the differential privacy is combined with the improved fuzzy C-means clustering algorithm, so that each group of input data no longer belongs to a specific class, but to the degree of membership. Finally, the Laplacian noise satisfying the differential privacy constraint is added to the centroid of the clustering set to obtain the disturbance position of each point, and the disturbance position is used to query. The experimental results show that the differential privacy fuzzy clustering location protection method reduces the query error and improves the efficiency of the algorithm under the premise of ensuring the location privacy security.

Key words: differential privacy, privacy preserving, Laplacian mechanism, kernel function, location preserving, clustering algorithm, DPK-F, KFCM

CLC Number: 

  • TP309