research-article

Differentially private and utility-aware publication of trajectory data

Authors:

Xin YaoAuthors Info & Claims

Volume 180, Issue C

https://doi.org/10.1016/j.eswa.2021.115120

Published: 15 October 2021 Publication History

Highlights

•

Propose two trajectory merging schemes based on k-means || clustering.

•

Add bounded Staircase noise to the count of generalized trajectory to reduce error.

•

Formally prove the proposed mechanisms satisfy differential privacy.

Abstract

Trajectory data is valuable for various applications, especially for intelligent transportation systems, which hunger for plenty of trajectories. However, publishing trajectory data while respecting users’ privacy has been a long-standing challenge. Currently, the prevailing releasing solutions usually merge trajectory locations based on k-means and add unbounded noise with Laplace distribution to the count of trajectory to achieve differential privacy protection. The trajectory merging methods based on k-means have a low efficiency and unbounded noise with Laplace distribution will leak user’ privacy and suffer from serious utility loss. To solve the above two problems, we devise two differentially private and utility-aware publication methods of trajectory data. More specifically, we first propose two trajectory merging schemes based on k-means || clustering. The first one is to use k-means || clustering algorithm to cluster the location area, and all the points in the cluster are replaced by the center of cluster. The other is to utilize Staircase mechanism to perturb the cluster centers in order to improve the level of privacy protection. Afterwards, we propose a bounded Staircase noise generation algorithm to perturb the true count of generalized trajectories. We prove our proposed methods preserve differential privacy theoretically. Experimental comparison show that our proposed publication methods significantly outperform existing approaches in terms of data utility and efficiency.

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Cited By

Zhao PZhang KZhang HChen H(2025)Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125279259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125279
Takagi SXiong LKato FCao YYoshikawa M(2024)HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data SynthesizationProceedings of the VLDB Endowment10.14778/3681954.368198317:11(3058-3071)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681983
Qiu SPi DWang YLiu Y(2023)Novel trajectory privacy protection method against prediction attacksExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118870213:PAOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118870
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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 180, Issue C

Oct 2021

596 pages

ISSN:0957-4174

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 October 2021

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Cited By

Zhao PZhang KZhang HChen H(2025)Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125279259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125279
Takagi SXiong LKato FCao YYoshikawa M(2024)HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data SynthesizationProceedings of the VLDB Endowment10.14778/3681954.368198317:11(3058-3071)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681983
Qiu SPi DWang YLiu Y(2023)Novel trajectory privacy protection method against prediction attacksExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118870213:PAOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118870
Gu ZZhang KZhang G(2022)Multiparty Data Publishing via Blockchain and Differential PrivacySecurity and Communication Networks10.1155/2022/56127942022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5612794
Buchholz EAbuadbba AWang SNepal SKanhere S(2022)Reconstruction Attack on Differential Private Trajectory Protection MechanismsProceedings of the 38th Annual Computer Security Applications Conference10.1145/3564625.3564628(279-292)Online publication date: 5-Dec-2022
https://dl.acm.org/doi/10.1145/3564625.3564628

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