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Differential Privacy in Cognitive Radio Networks: A Comprehensive Survey
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Cognitive radio integration risks exposing EV mobility trends through raw spectrum usage data.
“Cognitive radio integration, though promising for dynamic spectrum allocation, risks exposing EV mobility trends through raw spectrum usage data [140].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [140]
The ‘Cognitive Radio Based Vehicular Networks’ section (p. 23) says CR-based vehicular networks face privacy risks from real-time reporting and that sensing and analysis can expose vehicle locations. The Introduction (p. 1) supports the dynamic spectrum-allocation benefit. The cited passages do not specifically address electric vehicles, mobility trends, or exposure through raw spectrum-usage data. (LLM, conf=0.86)
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Sattler, F., Muller, K.-R., & Samek, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015958 · Cited by 1,355
Hassan, M. U., Rehmani, M. H., & Chen, J. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.060 · Cited by 520
Self-citationTowards Fair and Privacy-Preserving Federated Deep Models
Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2996273 · Cited by 250 · cited 1×
“Research works have shown that this integration can also cause privacy leakage, and even in some worst case scenarios adversarial federated learning models can be used by adversaries to learn private information from users [176], [177].”
BackgroundCognitive Radio Techniques Under Practical Imperfections: A Survey
Sharma, S. K., Bogale, T. E., Chatzinotas, S., Ottersten, B., Le, L. B., & Wang, X. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2452414 · Cited by 221
A comprehensive survey on authentication and privacy-preserving schemes in VANETs
Mundhe, P., Verma, S., & Venkatesan, S. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100411 · Cited by 108
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Cited by
Sorted by citations
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
“It also incorporated order-preserving encryption and non-interactive zero-knowledge proof (ZKP) to prevent location forgery.”
Metadata
- Authors
- Ul Hassan, M., Rehmani, M. H., Rehan, M., & Chen, J.
- Affiliations
- Munster Technological University (IE)
- COMSATS University Islamabad (PK)
- Swinburne University of Technology (AU)
- Corresponding author
- Rehmani, Mubashir Husain
- Rehan, Maaz
- Chen, Jinjun
- Venue
- Cognitive Computation
- Published
- 2022-01-28
- Volume
- 14
- Issue
- 2
- Pages
- 475-510
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W4220757524
- Cited by
- 19
- References
- 5
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 3
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Cognitive Computation (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Keywords
- differential privacy, cognitive radio networks, privacy preservation, privacy leakage, spectrum sharing, dynamic spectrum access, personally identifiable information, spectrum sensing, privacy-preserving data sharing
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