False Trajectory Privacy Protection Scheme Based on Location Service

Yang, Chen; Yan, Wei

doi:10.1088/1757-899x/768/7/072025

Cited by 1 publication

(2 citation statements)

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“…Zhang [ 20 ] proposed the

algorithm, which constructs a dummy location set by randomly selecting probabilistic similarity offset locations under guaranteed semantic distinction, improved security of location privacy, avoiding background knowledge attacks, edge information attacks, and homogeneity attacks to some extent. Yang [ 21 ] generated similar trajectories in real-time by the angle and distance between real trajectory points during continuous query service, which can also prevent edge information attacks when protecting user trajectories. Guo [ 22 ] introduced a new query privacy algorithm for continuous querying of location-based services that consider users with similar directions, speeds, and the same transmission patterns for anonymization, thus protecting users’ privacy throughout the query cycle.…”

Section: Introductionmentioning

confidence: 99%

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A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

Shen,

Wang,

Zhang

2023

Sensors

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With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k− 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k−1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.

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“…Zhang [ 20 ] proposed the

Section: Introductionmentioning

confidence: 99%

“…Spatial similarity [21] Spatial similarity Sim spa (p i , p j ) refers to the degree of geographic closeness between two points. In our method, we calculate spatial similarity by employing the Euclidean distance:…”

mentioning

confidence: 99%