Trajectory privacy protection method based on the time interval divided

Hu, Zhaowei; Yang, Jing; Zhang, Jianpei

doi:10.1016/j.cose.2018.05.001

Cited by 29 publications

(21 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e algorithm constructed the anonymous set to realize privacy protection by partitioning the graph. Hu et al [3] assumed that there was an independent privacy level on different trajectory segments and selected the maximum privacy requirements of trajectory segments as the privacy requirement. Similar to nonpersonalized trajectory privacy protection method, the…”

Section: Trajectory Body Sets the Personalized Privacy Requirementsmentioning

confidence: 99%

“…Although the traditional Euclidean distance accurately expresses the linear distance between geographical objects, the paths are usually polygonal in the actual geographic connected region. erefore, it is more reasonable to use the Manhattan distance to measure the distance between the trajectory points [3].…”

Section: Modified Manhattan Space Distancementioning

confidence: 99%

“…Although the purpose of step (5) is to find out which trajectory points can use p ′ as CTPT, the desensitization threshold is the same as that in step (3). erefore, the purpose of step (5) can be regarded as to search the CTPTs of p ′ , similar to searching the CTPTs of p i in step (3).…”

Section: Data Structurementioning

confidence: 99%

“…Select a note p′ in the NT i of p i while p′.NeighborNum ≥ d i (4) If exist p′ then add TID i to p′ (5) Delete TID i from p i (6) Next i (7) Output RNTP as the TD * ALGORITHM 3: Algorithm based on translocation of trajectory points on RNTP. (1) Input: TD (2) Map TD to a sequential linked list (SLL) (3) For i � 1 to |TD| p (4) For each p′ in the time-window of p i (5) If…”

Section: 1mentioning

confidence: 99%

“…e location service requests (LSR) contain personalized privacy requirements of the body on that location, and the requirements can inform the SA of body's sensitivity. For the trajectory data with personalized privacy requirements, the existing privacy protection methods usually resist the identity link attack of trajectories and realize the personalized anonymity of trajectories [1][2][3]. Due to the curse of dimensionality [4], the anonymity of trajectories will cause a lot of data distortion.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Wang

Chen

Zhang

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

show abstract

Section: Trajectory Body Sets the Personalized Privacy Requirementsmentioning

confidence: 99%

Section: Modified Manhattan Space Distancementioning

confidence: 99%

Section: Data Structurementioning

confidence: 99%

Section: 1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Wang

Chen

Zhang

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

DLPM: A dynamic location protection mechanism supporting continuous queries

Zhu

Hong

Xie

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Currently, the protection of users' location privacy, particularly for moveable users, is a major concern for both academia and business. In order to receive required services, a moveable user needs to constantly disclose his/her location information with an untrusted third party in his/her locations, which raises security and privacy issues. To settle the above problems, in this work, we creatively integrate local differential privacy (LDP) with conditional random field (CRF) to facilitate continuous location sharing among moveable users. Firstly, we advance a novel approach of employing CRF to represent users' mobility. After that, we establish a system to provide continuous location sharing by combining the δ$$ \delta $$‐location set and ε$$ \varepsilon $$‐LDP. Finally, we evaluate the system performance on actual data sets. The experimental results indicate that our technique outperforms the planar isotropic mechanism (PIM) and AGENT.

show abstract

An Enhanced Location-Data Differential Privacy Protection Method Based on Filter

Zhang

Kang

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Trajectory privacy protection method based on the time interval divided

Cited by 29 publications

References 11 publications

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

DLPM: A dynamic location protection mechanism supporting continuous queries

An Enhanced Location-Data Differential Privacy Protection Method Based on Filter

Contact Info

Product

Resources

About