TGM: A Generative Mechanism for Publishing Trajectories With Differential Privacy

Ghane, Soheila; Kulik, Lars; Ramamohanarao, Kotagiri

doi:10.1109/jiot.2019.2943719

Cited by 40 publications

(16 citation statements)

References 23 publications

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“…Unfortunately, the computation is expensive, and it is hard to protect location privacy in real-time. Ghane et al [42] present a trajectory generative mechanism (TGM), which is the first mechanism that captures the stay locations in trajectories. It encodes the trajectories as a graphical generation and generates the trajectories of arbitrary length.…”

Section: B Differential Privacy Trajectory Protectionmentioning

confidence: 99%

A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

Cai

Lyu

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The locations and users' information can be shared and interacted in the IoV (Internet of Vehicles), which provides sufficient data for traffic deployment and behavior pattern analysis. However, privacy issues had become more severe since personal or sensitive information is inclined to be revealed in a big data environment. In this work, a novel differential privacybased algorithm named DPTD (Differentially Private Trajectory Database) is proposed for trajectory database releasing. Firstly, a 3-dimensional generalized trajectory dataset is established by considering the time factor. Then, the trajectory space is divided into several planes through the timestamps, and the set of the locations on each plane is further processed by clustering and generalizing to re-form new trajectories, that is, the trajectories to be released. This method is quite favorable to prefix-tree releasing because the spatiotemporal characteristics of the trajectories can be captured and spareness problem is fixed. Besides, a Markov assumption-based prediction method is suggested in order to reduce the cost of adding noise. Unlike the traditional method that the noise is added layer by layer, the noise is only added to the odd layers based on the prediction through spatiotemporal correlation, saving approximately 50% of the privacy budget. Theoretical analysis and experimental results show that the proposed algorithm has better data availability than the compared algorithms while guaranteeing the expected privacy level.

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Section: B Differential Privacy Trajectory Protectionmentioning

confidence: 99%

A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

Cai

Lyu

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Finally, the perturbed trajectory is synthesized according to the obfuscated prefix tree to protect the spatial correlation contained within the trajectory. In the literature [7], the authors presented a differential private method called TGM for publishing trajectories. In TGM, they partitioned the geographical space and constructed a prefix sequence graph to model the spatial transfer features between grids in trajectories.…”

Section: Related Workmentioning

confidence: 99%

“…Literature Limitations Methods without considering the correlation Suppression [4] and bounded perturbation [22] Kanonymity and its derivation [3,23,24]differential privacy [25][26][27][28][29][30] Face combination attacks and background knowledge attacks, and do not consider the privacy leakage caused by trajectory correlations Methods considering correlations within a single trajectory [6][7][8]31] Do not consider the privacy leakage caused by trajectory correlations…”

Section: Categorymentioning

confidence: 99%

“…According to the constraint condition in formula (7), when the constraint condition is satisfied, the trajectory correlation between the solution P C of user u in equation ( 6) and the perturbed cell trajectories of other users can be reduced. Namely, the security for the output of PSO-DP would be increased.…”

Section: 42mentioning

confidence: 99%

“…ere are many privacy-preserving methods for trajectories, and these approaches can be summarized into three categories: methods without considering protection correlation [3][4][5], mechanisms considering correlations within a single trajectory [6][7][8] and approaches considering the trajectory correlations [9][10][11]. For the previous two categories, correlation between trajectories is not taken into consideration in these methods, causing the trajectories perturbed by these approaches to still face privacy leakage risks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations

Zhu

Xie

2022

Security and Communication Networks

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With the prevailing use of smartphones and location-based services, vast amounts of trajectory data are collected and used for many applications. When trajectory data are sent to a third-party research institute for analytical applications, the privacy of users would be severely disclosed. For example, the relationship between users will be revealed from the correlation between trajectories. In this paper, we propose a method for releasing trajectory datasets without revealing correlation between trajectories, called RDPT. In RDPT, we first quantify the trajectory correlation and convert the problem of protecting trajectory correlations into reducing the trajectory similarities of users and preserving the utility of the perturbed trajectories. Based on the insight, we model a multi-objective optimization problem and solve the problem with the particle swarm optimization algorithm modified to satisfy differential privacy. Then we generate synthetic trajectories and the correlations between trajectories are reduced. We conduct extensive experiments on three real trajectory datasets. The experimental results show that RDPT achieves almost equivalent data utility to and better privacy than the existing methods.

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Differentially Private Linear Regression Analysis via Truncating Technique

Liu

Wang

et al. 2021

Lecture Notes in Computer Science

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TGM: A Generative Mechanism for Publishing Trajectories With Differential Privacy

Cited by 40 publications

References 23 publications

A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations

Differentially Private Linear Regression Analysis via Truncating Technique

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