IFTS: A Location Privacy Protection Method Based on Initial and Final Trajectory Segments

Privacy protection problem is one of the most concerning issues related to Location-Based Services (LBS) in our daily life. Privacy protection of LBS often requires anonymizing customer's trajectory data. Currently available methods for trajectory anonymity often assume an entire trajectory as...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.18112-18122
Main Authors: Xu, Jiuyun, Liu, Lele, Zhang, Ruru, Xie, Jin, Duan, Qiang, Shi, Leyi
Format: Article
Language:English
Subjects:
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Clustering algorithms
Customers
Data privacy
Egress
Euclidean distance
initial and final trajectory segments (IFTS)
Location based service (LBS)
Location based services
location privacy
Motion segmentation
Privacy
Roads
Segmentation
Segments
Trajectory
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Summary:Privacy protection problem is one of the most concerning issues related to Location-Based Services (LBS) in our daily life. Privacy protection of LBS often requires anonymizing customer's trajectory data. Currently available methods for trajectory anonymity often assume an entire trajectory as one anonymous unit, which may lead to low anonymity efficiency due to the massive amount of trajectory data, especially for customers travel through a long road. Considering people's routine activities, the starting and ending locations of a trip often uncover the user's request intent, which may lead to exposure of user privacy. In order to address the problem of inefficient trajectory anonymity, we propose a location privacy protection method that is based on the initial and final trajectory segmentation (IFTS) in this paper. In the IFTS method, the road network structure is first transformed into an edge cluster model based on its location type. Then, the user trajectory is divided to segments according to the temporal sequence of the ingress and egress nodes. The initial and final trajectory segments are identified and divided into equivalence classes, which then are used for constructing a trajectory graph and the corresponding k-anonymous set. Our experimental results show that the proposed method can reduce the anonymous area, improve anonymity efficiency, and enhance trajectory data utilization compared to the existing methods for trajectory anonymity.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3052169