SST: Privacy Preserving for Semantic Trajectories

Han, Pin-I; Tsai, Hsiao-Ping

doi:10.1109/mdm.2015.18

Cited by 19 publications

(9 citation statements)

References 17 publications

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“…In [14], the average information loss is defined as the shrink of the probability that an object can be determined in a certain position. [107] evaluates point-level information loss based on the translation ratio which is the percentage of modified points in each trajectory after anonymization.…”

Section: The Quality Of Datamentioning

confidence: 99%

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

Jin

Hua

Francia

et al. 2022

IEEE Trans. Knowl. Data Eng.

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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.

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Section: The Quality Of Datamentioning

confidence: 99%

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

Jin

Hua

Francia

et al. 2022

IEEE Trans. Knowl. Data Eng.

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“…The main idea was to forbid the data release of sensitive location information to protect the individual trajectory privacy of users. Aimed to balance the data utility and k-anonymity trajectory privacy, Han et al [30] proposed a semantic space translation algorithm (SST) which provided different levels of privacy protection for different locations. Hwang et al [31] introduced a novel time-obfuscated technique which breaks the sequence of the query issuing time to protect users' trajectory privacy.…”

Section: Trajectory Privacy Preservingmentioning

confidence: 99%

DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks

Zhu

Liu

et al. 2021

IEEE Trans. Serv. Comput.

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Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis, we prove that DP-LTOD scheme can guarantee-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

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“…There have been many centralized approaches for trajectory anonymization. Most of them [9,10,13,20,[22][23][24] output anonymized trajectories in the form of cloaking regions or centers of clusters. For example, in Reference [10], the spatial-temporal cloaking technique is applied to generate cloaking regions covering segments of trajectories.…”

Section: Privacy Preserving Trajectory Publishingmentioning

confidence: 99%

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

Ward

Lin

Madria

2020

ACM/IMS Trans. Data Sci.

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With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, then they would be valuable assets to various service providers to explore business opportunities, to study commuter behavior for better transport management, which in turn benefits the general public for day-today commuting. However, there are two major concerns that considerably limit the availability and the usage of these trajectory datasets. The first is the threat to individual privacy, as users' trajectories may be misused to discover sensitive information, such as home locations, their children's school locations, or social information like habits or relationships. The other concern is the ability to analyze the exabytes of location data in a timely manner. Although there have been trajectory anonymization approaches proposed in the past to mitigate privacy concerns. None of these prior works address the scalability issue, since it is a newly occurring problem brought by the significantly increasing adoption of location-based services. In this article, we conquer these two challenges by designing a novel parallel trajectory anonymization algorithm that achieves scalability, strong privacy protection, and high utility rate of the anonymized trajectory datasets. We have conducted extensive experiments using MapReduce and Spark on real maps with different topologies, and our results prove both effectiveness and efficiency when compared with the centralized approaches. CCS Concepts: • Security and privacy → Social aspects of security and privacy; Privacy protections; • Computing methodologies → MapReduce algorithms; Massively parallel algorithms;

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SST: Privacy Preserving for Semantic Trajectories

Cited by 19 publications

References 17 publications

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

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

DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

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