A secure and privacy preserved infrastructure for VANETs based on federated learning with local differential privacy
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Study [11] addressed communication security between roadside infrastructure and vehicles by integrating FL with LDP in VANETs.
“Extending this to broader vehicle-infrastructure ecosystems, study [11] addressed communication security between roadside infrastructure and vehicles by integrating FL”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [11]
Vehicle data is perturbed locally before being shared with external infrastructures, enabling collaborative model training while mitigating gradient leakage and inference attack.
“Here, vehicle data is perturbed locally before being shared with external infrastructures, enabling collaborative model training while mitigating gradient leakage and inference attack.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [11]
Cites
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400 · see citing works
Privacy-preserving blockchain-based federated learning for traffic flow prediction
Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.003 · Cited by 339 · see citing works
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Kalidoss, L., Thouti, S., Arunachalam, R., & Ramamurthy, P. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125275 · Cited by 22 · see citing works
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“Within these domains, studies are further classified based on the PPML techniques.”
- Authors
- Batool, H., Anjum, A., Khan, A., Izzo, S., Mazzocca, C., & Jeon, G.
- Affiliations
- COMSATS University Islamabad (PK)
- Quaid-i-Azam University (PK)
- University of Derby (GB)
- University of Naples Federico II (IT)
- University of Bologna (IT)
- Incheon National University (KR)
- Corresponding author
- Jeon, Gwanggil
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 652
- Pages
- 119717
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W4387503881
- Cited by
- 89
- References
- 3
- Citations by year
- 2026: 30 · 2025: 33 · 2024: 26
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Sciences (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- vehicular ad hoc networks, federated learning, local differential privacy, privacy preservation, roadside units, gradient leakage attacks, secure data sharing, edge computing, vehicle data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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