基于相对熵和K-means的形状相似差分隐私轨迹保护机制

doi:10.11959/j.issn.1000-436x.2021008

Abstract

Abstract:

To solve the problem that most studies had not fully considered the sensitivity of location to privacy budget and the influence of trajectory shape, which made the usability of published trajectory poor, a shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means was proposed.Firstly, according to the topological relationship of geographic space, relative entropy was used to calculate the sensitivity of real location to privacy budget, a real-time calculation method of location sensitive privacy level was designed, and a new privacy model was built in combination with differential privacy budget.Secondly, K-means algorithm was used to cluster the release position to obtain the release position set that was most similar to the real position direction, and Fréchet distance was introduced to measure the similarity between the release track and the real track, so as to improve the availability of the release track.Experiments on real data sets show that the proposed trajectory protection mechanism has obvious advantages in trajectory availability compared with others.

Key words: trajectory privacy, differential privacy, relative entropy, K-means, shape similarity

CLC Number:

TP391

Suxia ZHU, Shulun LIU, Guanglu SUN. Shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means[J]. Journal on Communications, 2021, 42(2): 113-123.

Figures/Tables 9

符号表示	符号含义
p^(t)-	t时刻的先验概率
p^(t)+	t时刻的后验概率
TL	整条轨迹的真实位置区域的集合
DL	整条轨迹的干扰位置区域的集合
PL^t	t时刻真实位置可能存在的集合
PRN^t	根据状态转移矩阵得到的t时刻可能存在位置的集合
PRNN^t	t时刻可能发布位置的集合
$R^{t}$	t时刻真实区域到扰乱区域的概率转移矩阵
$M$	真实区域状态转移概率矩阵
ep^t	t时刻隐私预算的集合
fr^t	t时刻Fréchet距离的集合

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