A Trusted Data Privacy Computing Method for Vehicular Ad Hoc Networks Based on Homomorphic Encryption and DAG Blockchain
Abstract
Recently, vehicular ad hoc networks (VANETs) have garnered significant attention in the industry, thanks to their distinctive characteristics of mobility and real-time capabilities. In order to protect the privacy of data sharing in VANETs, a privacy computing scheme based on homomorphic encryption and blockchain has been proposed. First, the Paillier algorithm of homomorphic encryption is used to encrypt the data, making it available but not invisible to ensure the security of the data. Second, the data structure of a directed acyclic graph (DAG) is applied to replace the chain structure so that it can be processed in parallel and reduce the delay time of transmission confirmation. Finally, a single point cluster is set up in the Bigchain database, and Docker virtual technology is used to simulate the high dynamic and high load environment of VANETs, so as to verify the computational efficiency and security of the proposed scheme. The experimental results show that the privacy computing scheme combining homomorphic encryption and DAG blockchain not only eliminates the need for repeated encryption and decryption in the communication process but also reduces the computational cost associated with private data. Compared with traditional chain blockchain, this scheme achieves a comprehensive delay reduction of 68.51% and shortens the average confirmation time by 85.73%, effectively meeting the high-performance requirements of VANETs.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Another study [55] proposed a privacy-preserving computing scheme for VANETs that combined PHE with directed acyclic graph (DAG) blockchain to improve privacy and performance.
“Another study [55], proposed a privacy-preserving computing scheme for VANETs that combined PHE with directed acyclic graph (DAG) blockchain to improve privacy and performance.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [55]
Cites
Performance analysis and comparison of PoW, PoS and DAG based blockchains
Cao, B., Zhang, Z., Feng, D., Zhang, S., Zhang, L., Peng, M., & Li, Y. (2020) · Digital Communications and Networks · doi:10.1016/j.dcan.2019.12.001 · Cited by 264 · see citing works
Blockchain-Empowered Space-Air-Ground Integrated Networks: Opportunities, Challenges, and Solutions
Wang, Y., Su, Z., Ni, J., Zhang, N., & Shen, X. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3131711 · Cited by 213 · see citing works
Direct Acyclic Graph-Based Ledger for Internet of Things: Performance and Security Analysis
Li, Y., Cao, B., Peng, M., Zhang, L., Zhang, L., Feng, D., & Yu, J. (2020) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2020.2991994 · Cited by 191 · see citing works
Scalable and Privacy-Preserving Data Sharing Based on Blockchain
Zheng, B., Zhu, L., Shen, M., Gao, F., Zhang, C., Li, Y., & Yang, J. (2018) · Journal of Computer Science and Technology · doi:10.1007/s11390-018-1840-5 · Cited by 144 · see citing works
Vangala, A., Das, A. K., Mitra, A., Das, S. K., & Park, Y. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3231121 · Cited by 128 · see citing works
Chen, J., Li, K., & Yu, P. S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3105682 · Cited by 116 · see citing works
Privacy Computing: Concept, Computing Framework, and Future Development Trends
Li, F., Li, H., Niu, B., & Chen, J. (2019) · Engineering · doi:10.1016/j.eng.2019.09.002 · Cited by 111 · see citing works
Jabbar, R., Fetais, N., Kharbeche, M. B. M., Krichen, M., Barkaoui, K., & Shinoy, M. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3062219 · Cited by 107 · see citing works
PPVF: Privacy-Preserving Protocol for Vehicle Feedback in Cloud-Assisted VANET
Cheng, H., Shojafar, M., Alazab, M., Tafazolli, R., & Liu, Y. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3117950 · Cited by 91 · see citing works
TollsOnly Please—Homomorphic Encryption for Toll Transponder Privacy in Internet of Vehicles
Karim, H., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056240 · Cited by 43 · see citing works
A mobile edge computing-based applications execution framework for Internet of Vehicles
Wu, L., Zhang, R., Li, Q., Ma, C., & Shi, X. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0425-6 · Cited by 27 · see citing works
Zhang, X., Li, R., Hou, W., & Zhao, H. (2021) · Security and Communication Networks · doi:10.1155/2021/9942632 · Cited by 13 · see citing works
Secure Knowledge Sharing in Internet of Vehicles: A DAG-Enabled Blockchain Framework
Chai, H., Leng, S., & Wu, F. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500503 · Cited by 11 · see citing works
A DAG-Based Reputation Mechanism for Preventing Peer Disclosure in SIoV
Li, Y., Tao, X., Zhang, X., Xu, J., Wang, Y., & Xia, W. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3189108 · Cited by 10 · see citing works
Combining blockchain and crowd-sensing for location privacy protection in Internet of vehicles
Shen, Z., Ren, F., Wang, H., Liu, P., Liu, K., & Zhang, J. (2024) · Vehicular Communications · doi:10.1016/j.vehcom.2023.100724 · Cited by 9 · see citing works
Cui, Y., Li, S., Wang, Y., & Gao, B. (2020) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci51460.2020.9338620 · Cited by 9 · see citing works
Ruijin, Z. F. W., Yucheng, Z. W. T., Zhang, W., & ZHANG, F. (2020) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.11959/j.issn.2096-109x.2020011 · Cited by 9 · see citing works
SanIdea: Exploiting Secure Blockchain-Based Access Control via Sanitizable Encryption
Jiang, P., Liu, Q., & Zhu, L. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3340066 · Cited by 8 · see citing works
Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles
Mo, Z., Zhipeng, G. A. O., Yang, Y. A. N. G., Yijing, L. I. N., Shan, S. U. N., & Zhao, C. (2022) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 20 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Dynamic Oral English Assessment System Based on Large Language Models for Learners
Yu, J., & Burhanudeen, H. (2025) · IEEE International Conference on Social Sciences and Intelligence Management (SSIM) · doi:10.3390/engproc2025098032 · Cited by 2 · see citing works
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“Additionally, managing cumulative privacy loss over multiple queries or iterations (the privacy budget) is crucial to maintaining robust privacy guarantees over time.”
Liu, T., Lu, K., Jiang, L., Xu, J. H., Li, J., Shu, F., & Han, Z. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3612992 · Cited by 1 · see citing works
Zhang, X., Hou, W., Wang, J., Li, L., & Yue, P. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.072384 · Cited by 1 · see citing works
PriVET: Privacy-Preserving and Verifiable Vehicular Energy Trading via Smart Contracts
Chang, Y., Luan, T. H., Zheng, J., Li, Y., & Su, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3659122
Applications and Challenges of Blockchain Based Internet of Vehicles
Zhao, Z., Chen, Y., Yang, H., Han, T., Zhang, H., & Yin, Y. (2025) · International Conference on Blockchain Technology and Information Security (ICBCTIS) · doi:10.1109/icbctis66509.2025.11387250
Privacy-Preserving and Collusion-Resistant Data Query Scheme for Vehicular Platoons
Ma, C., Hu, P., Ni, T., Liu, Y., Hu, L., Zuo, K., Chen, F., & Luo, Y. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3669013
Blockchain-Based Privacy Computation and Compliance Analysis Framework for Student Behavioral Data
Yu, X. (2026) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs68679.2026.11578587
Yao, W., & Hai, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3710174
Privacy Protection of Energy Transaction Data Based on Blockchain and Homomorphic Encryption
Wang, X. F., Zhang, S., Gao, T., WANG, L. C., Xu, S., Shao, W., & Wang, Q. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582628
- Authors
- Jiang, W., Tao, J., & Guan, Z.
- Affiliations
- Southeast University (CN)
- Huaqiao University (CN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2025-03-15
- Volume
- 12
- Issue
- 6
- Pages
- 6621-6632
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4404056776
- Cited by
- 9
- References
- 20
- Citations by year
- 2026: 6 · 2025: 3
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- National Natural Science Foundation of China (62372190)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- vehicular ad hoc networks, data privacy, homomorphic encryption, Paillier cryptosystem, blockchain
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 ›
Are you an author, or do you see a problem with this page? Request a review
