Journal on Communications ›› 2020, Vol. 41 ›› Issue (4): 123-133.doi: 10.11959/j.issn.1000-436x.2020049

• Papers • Previous Articles     Next Articles

Trajectory differential privacy protection mechanism based on prediction and sliding window

Ayong YE1,2,Lingyu MENG1,2,Ziwen ZHAO1,2,Yiqing DIAO1,2,Jiaomei ZHANG1,2   

  1. 1 College of Mathematics and Informatics,Fujian Normal University,Fuzhou 350007,China
    2 Fujian Provincial Key Laboratory of Network Security and Cryptology,Fuzhou 350007,China
  • Revised:2020-01-30 Online:2020-04-25 Published:2020-04-30
  • Supported by:
    The National Natural Science Foundation of China(61972096);The National Natural Science Foundation of China(61872088);The National Natural Science Foundation of China(61872090);The Natural Science Foundation of Fujian Province(2018J01780)

Abstract:

To address the issues of privacy budget and quality of service in trajectory differential privacy protection,a trajectory differential privacy mechanism integrating prediction disturbance was proposed.Firstly,Markov chain and exponential perturbation method were used to predict the location which satisfies the differential privacy and temporal and spatial security,and service similarity map was introduced to detect the availability of the location.If the prediction was successful,the prediction location was directly used to replace the location of differential disturbance,to reduce the privacy cost of continuous query and improve the quality of service.Based on this,the trajectory privacy budget allocation mechanism based on w sliding window was designed to ensure that any continuous w queries in the trajectory meet the ε-differential privacy and solve the trajectory privacy problem of continuous queries.In addition,a privacy customization strategy was designed based on the sensitivity map.By customizing the privacy sensitivity of semantic location,the privacy budget could be customized to improve its utilization.Finally,the validity of the scheme was verified by real data set experiment.The results illustrate that it offers the better privacy and quality of service.

Key words: location privacy, trajectory privacy, differential privacy, privacy accumulation

CLC Number: 

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