A survey of local differential privacy for securing internet of vehicles
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A few studies focused on specific PPML techniques within IoV applications, such as HE in VANETs, local-DP for securing IoV systems, BC-PPML in IoV, blockchain-enabled FL in IoV, blockchain for IoV, and FL in CAVs and ITS applications.
“A few studies focused on specific PPML techniques within IoV applications, such HE in vehicular ad hoc networks (VANETs) [132], local-DP for securing IoV systems [191], BC-PPML in IoV [56], blockchain-enabled FL in IoV [13], blockchain for IoV (discussed some aspects of BC-PPML) [151], and FL in CAVs [18] and ITS applications [185, 186].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [191]
DP noise injection reduces privacy leakage but degrades model accuracy.
“For example, DP noise injection reduces privacy leakage but degrades model accuracy, necessitating adaptive budget allocation strategies tailored to non-IID settings.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [191]
The privacy budget (ε) governs the trade-off between privacy and utility: smaller ε values strengthen privacy but degrade model accuracy.
“The privacy budget ( 𝜖 ) governs the trade-off between privacy and utility: smaller 𝜖 values strengthen privacy but degrade model accuracy [48, 191].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [191]
Cites
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Erlingsson, Ú., Pihur, V., & Korolova, A. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660348 · Cited by 1,838 · see citing works
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861 · see citing works
Optimal schemes for discrete distribution estimation under locally differential privacy
Ye, M., & Barg, A. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2018.2809790 · Cited by 158 · see citing works
Flagged first, then by citations
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Local Differential Privacy-Based Federated Learning for Internet of Things
Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037194 · Cited by 456 · see citing works
Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
Kaltakis, K., Polyzi, P., Drosatos, G., & Rantos, K. (2021) · Applied Sciences · doi:10.3390/app11219792 · Cited by 25 · see citing works
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“As IoV systems handle vast amounts of sensitive and private data, the need for PPML solutions has become increasingly critical on both the user and server sides.”
Influential
- Authors
- Zhao, P., Zhang, G., Wan, S., Liu, G., & Umer, T.
- Affiliations
- Donghua University (CN)
- Zhongnan University of Economics and Law (CN)
- Huazhong University of Science and Technology (CN)
- COMSATS University Islamabad (PK)
- Corresponding author
- Wan, Shaohua
- Venue
- The Journal of Supercomputing
- Published
- 2019-12-09
- Volume
- 76
- Issue
- 11
- Pages
- 8391-8412
- ISSN
- 0920-8542 (ISSN-L)
- 1573-0484
- OpenAlex
- W2996626025
- Cited by
- 66
- References
- 3
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 11 · 2023: 11 · 2022: 7 · 2021: 12 · 2020: 6
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Supercomputing (publishedVersion)
- 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
- local differential privacy, Internet of Vehicles, vehicle data privacy, privacy preservation, differential privacy, untrusted third party, privacy-utility trade-off, computational cost, connected vehicles
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