Differentially Private Location Privacy-preserving Scheme withSemantic Location

How to realize more reasonable noise addition in location differential privacy-preserving is a hot topic issue.However,adding the same amount of noise in different locations will result in the decrease of service availability and privacy preservation.To this end,a differentially private location privacy-preserving scheme with semantic location is examined in this paper,which can systematically solve the contradiction among privacy-preserving,service availability and time overhead.The proposed method firstly constructs the expected distance by employing the framework of geo-indistinguishability,then determines the sensitivity of different locations by using the privacy quality function and requirement function,and finally adds Laplace noise to different types of region at fine granularity according to the location sensitivity.Comprehensive simulation experiments are carried out on two public datasets,which compare the proposed scheme with the existing methods in terms of query success rate based on Bayesian attack,service availability based on expected distance quantization and time overhead.The experimental results demonstrate that the proposed scheme is feasible and effective,and obtains a better trade-offs among privacy preservation,service availability and time consuming.

Abstract licence: CC BY-NC-ND

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