The DOI matches its Crossref record.
Differential privacy‐guaranteed trajectory community identification over vehicle ad‐hoc networks
Abstract
In vehicle ad‐hoc networks (VANETs), a large number of location‐based services can be provided for users according to their movement features. Meanwhile, privacy may be leaked when users publish true trajectory data to VANETs servers. Traditional privacy‐preserving methods have some limitations, for example,k‐anonymous have to predefine the adversaries' background knowledge. Therefore, this paper proposes a new algorithm to partition and cluster trajectories into a proper community with respect to the particularity of VANETs based on differential privacy. The experimental results show that our method outperforms the suppression algorithm and partition algorithm in perspective from information loss and operation efficiency.
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Study [198] integrated exponential DP with trajectory partitioning and clustering to enhance efficiency and data utility while reducing information loss.
“Another study, [198] integrated exponential DP with trajectory partitioning and clustering to enhance efficiency and data utility while reducing information loss.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [198]
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No references could be resolved yet.
Research works printed but not linked: 15
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Cited by
Sorted by citations
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
Metadata
- Authors
- Zhou, Z., Qiao, Y., Zhu, L., Guan, J., Liu, Y., & Xu, C.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Athlone Institute of Technology (IE)
- Corresponding author
- Xu, Changqiao
- Venue
- Internet Technology Letters
- Publisher
- Wiley
- Published
- 2017-12-04
- Volume
- 1
- Issue
- 3
- ISSN
- 2476-1508 (ISSN-L)
- OpenAlex
- W2763177710
- Cited by
- 11
- References
- 0
- Citations by year
- 2023: 2 · 2021: 1 · 2020: 4 · 2019: 4
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Internet Technology Letters (publishedVersion)
- Funders
- National Natural Science Foundation of China (61372112, 61522103, 61232017)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Keywords
- differential privacy, vehicular ad hoc networks, trajectory clustering, privacy preservation, location-based services
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