Segment Clustering Based Privacy Preserving Algorithm for Trajectory Data Publishing

2018 ◽  
pp. 211-221
Author(s):  
Li Fengyun ◽  
Xue Junchao ◽  
Sun Dawei ◽  
Gao Yanfang
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Trajectory Data

Personalized Privacy-Preserving Trajectory Data Publishing

2017 ◽  
Vol 26 (2) ◽  
pp. 285-291 ◽  
Author(s):  
Qiwei Lu ◽  
Caimei Wang ◽  
Yan Xiong ◽  
Huihua Xia ◽  
Wenchao Huang ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Trajectory Data

An efficient method for privacy-preserving trajectory data publishing based on data partitioning

2019 ◽  
Vol 76 (7) ◽  
pp. 5276-5300 ◽  
Author(s):  
Songyuan Li ◽  
Hong Shen ◽  
Yingpeng Sang ◽  
Hui Tian
Keyword(s):  
Efficient Method ◽  
Privacy Preserving ◽  
Data Partitioning ◽  
Data Publishing ◽  
Trajectory Data

scholarly journals Privacy-Preserving Trajectory Data Publishing by Dynamic Anonymization with Bounded Distortion

2021 ◽  
Vol 10 (2) ◽  
pp. 78
Author(s):  
Songyuan Li ◽  
Hui Tian ◽  
Hong Shen ◽  
Yingpeng Sang
Keyword(s):  
Privacy Preservation ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Published Data ◽  
Trajectory Data ◽  
Data Utilization ◽  
Data Set ◽  
Guangzhou City ◽  
Bounded Distortion ◽  
Privacy Breaches

Publication of trajectory data that contain rich information of vehicles in the dimensions of time and space (location) enables online monitoring and supervision of vehicles in motion and offline traffic analysis for various management tasks. However, it also provides security holes for privacy breaches as exposing individual’s privacy information to public may results in attacks threatening individual’s safety. Therefore, increased attention has been made recently on the privacy protection of trajectory data publishing. However, existing methods, such as generalization via anonymization and suppression via randomization, achieve protection by modifying the original trajectory to form a publishable trajectory, which results in significant data distortion and hence a low data utility. In this work, we propose a trajectory privacy-preserving method called dynamic anonymization with bounded distortion. In our method, individual trajectories in the original trajectory set are mixed in a localized manner to form synthetic trajectory data set with a bounded distortion for publishing, which can protect the privacy of location information associated with individuals in the trajectory data set and ensure a guaranteed utility of the published data both individually and collectively. Through experiments conducted on real trajectory data of Guangzhou City Taxi statistics, we evaluate the performance of our proposed method and compare it with the existing mainstream methods in terms of privacy preservation against attacks and trajectory data utilization. The results show that our proposed method achieves better performance on data utilization than the existing methods using globally static anonymization, without trading off the data security against attacks.

scholarly journals A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

2021 ◽  
Author(s):  
Fengmei Jin ◽  
Wen Hua ◽  
Matteo Francia ◽  
Pingfu Chao ◽  
Maria Orlowska ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Traffic Management ◽  
Real Life ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Data Publishing ◽  
Sensitive Information ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
The Right

<div>Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.</div>

scholarly journals Privacy-preserving trajectory data publishing by local suppression

2013 ◽  
Vol 231 ◽  
pp. 83-97 ◽  
Author(s):  
Rui Chen ◽  
Benjamin C.M. Fung ◽  
Noman Mohammed ◽  
Bipin C. Desai ◽  
Ke Wang
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Trajectory Data
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