scholarly journals Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Shuai Wang ◽  
Chunyi Chen ◽  
Guijie Zhang
Keyword(s):  
Privacy Protection ◽  
Location Based Services ◽  
Data Sets ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Privacy Requirements ◽  
Protection Scheme

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.

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 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>

An efficient trajectory-clustering algorithm based on an index tree

2011 ◽  
Vol 34 (7) ◽  
pp. 850-861 ◽  
Author(s):  
Guan Yuan ◽  
Shixiong Xia ◽  
Lei Zhang ◽  
Yong Zhou ◽  
Cheng Ji
Keyword(s):  
Radio Frequency Identification ◽  
Clustering Algorithm ◽  
Real Data ◽  
Structural Similarity ◽  
Location Based Services ◽  
Similarity Function ◽  
Data Sets ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Index Tree

With the development of location-based services, such as the Global Positioning System and Radio Frequency Identification, a great deal of trajectory data can be collected. Therefore, how to mine knowledge from these data has become an attractive topic. In this paper, we propose an efficient trajectory-clustering algorithm based on an index tree. Firstly, an index tree is proposed to store trajectories and their similarity matrix, with which trajectories can be retrieved efficiently; secondly, a new conception of trajectory structure is introduced to analyse both the internal and external features of trajectories; then, trajectories are partitioned into trajectory segments according to their corners; furthermore, the similarity between every trajectory segment pairs is compared by presenting the structural similarity function; finally, trajectory segments are grouped into different clusters according to their location in the different levels of the index tree. Experimental results on real data sets demonstrate not only the efficiency and effectiveness of our algorithm, but also the great flexibility that feature sensitivity can be adjusted by different parameters, and the cluster results are more practically significant.

scholarly journals Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Bennati ◽  
Aleksandra Kovacevic
Keyword(s):  
Service Providers ◽  
Flow Analysis ◽  
Location Based Services ◽  
Location Based Service ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Imperfect Knowledge ◽  
Spatio Temporal ◽  
Definition Of ◽  
Privacy Risks

AbstractMobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue, we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target’s home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.

scholarly journals Differential Privacy Location Protection Scheme Based on Hilbert Curve

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Wang ◽  
Feng Wang ◽  
Hongtao Li
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Differential Privacy ◽  
Dimensional Space ◽  
Location Based Services ◽  
Data Availability ◽  
Location Information ◽  
Hilbert Curve ◽  
One Dimensional ◽  
Protection Scheme

Location-based services (LBS) applications provide convenience for people’s life and work, but the collection of location information may expose users’ privacy. Since these collected data contain much private information about users, a privacy protection scheme for location information is an impending need. In this paper, a protection scheme DPL-Hc is proposed. Firstly, the users’ location on the map is mapped into one-dimensional space by using Hilbert curve mapping technology. Then, the Laplace noise is added to the location information of one-dimensional space for perturbation, which considers more than 70% of the nonlocation information of users; meanwhile, the disturbance effect is achieved by adding noise. Finally, the disturbed location is submitted to the service provider as the users’ real location to protect the users’ location privacy. Theoretical analysis and simulation results show that the proposed scheme can protect the users’ location privacy without the trusted third party effectively. It has advantages in data availability, the degree of privacy protection, and the generation time of anonymous data sets, basically achieving the balance between privacy protection and service quality.

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