Abstract
By judging whether the start-point and end-point of a trajectory conform to the user’s behavioral habits, an attacker who possesses background knowledge can breach the anonymous trajectory. Traditional trajectory privacy preservation schemes often generate an anonymous set of trajectories without considering the security of the trajectory start- and end-points. To address this problem, this paper proposes a privacy-preserving trajectory publication method based on generating secure start- and end-points. First, a candidate set containing a secure start-point and end-point is generated according to the user’s habits. Second, k−1 anonymous trajectories are generated bidirectionally according to that secure candidate set. Finally, accessibility corrections are made for each anonymous trajectory. This method integrates features such as local geographic reachability and trajectory similarity when generating an anonymized set of trajectories. This provides users with privacy preservation at the k-anonymity level, without relying on the trusted third parties and with low algorithm complexity. Compared with existing methods such as trajectory rotation and unidirectional generation, theoretical analysis and experimental results on the datasets of real trajectories show that the anonymous trajectories generated by the proposed method can ensure the security of trajectory privacy while maintaining a higher trajectory similarity.
Highlights
With the development of wireless communication and positioning technology, users can publish their trajectories to obtain convenient point-of-interest information [1]
Experimental Results and Analysis. e parameters involved in the following experiments include the following: (1) trajectory difference degree, which is an important index to evaluate the pros and cons of generating dummy trajectory; (2) anonymity level k, that is, the number of dummy trajectories generated at one time; and (3) trajectory leakage probability, which addresses the probability that the real trajectory is seen by the attacker
Schemes compared with this paper include the following: (1) the efficient trajectory privacy protection scheme (Rotation) [15]; (2) the pseudotrajectory privacy protection scheme based on spatiotemporal correlation in trajectory publishing (Round) [16]; and (3) the idealized k-anonymity model (Optimal); (4) k-CS algorithm: trajectory data privacy-preserving based on semantic location protection (KCS) [21]; (5) on the privacy offered by (k, δ) − anonymity) ( (k, δ) − anonymity) [29]
Summary
With the development of wireless communication and positioning technology, users can publish their trajectories to obtain convenient point-of-interest information [1]. An attacker can obtain other spatial and temporal attributes from the trajectory data with the purpose of identifying the user’s private information. Most privacy protection methods based on generalization do not take into account the security of the trajectory start- and end-points after generalization. E theoretical analysis and experiments show that the proposed method ensures the high availability of trajectory data under the premise of effectively protecting the privacy of users. E content of the paper is arranged as follows: Section 2 introduces the related work of trajectory privacy protection along with related concepts and definitions used in the article; Section 3 introduces the personalized trajectory privacy protection plan proposed in the study; Section 4 includes the relevant experimental data and analysis of results; the fifth section is the summary and the work introduction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
