Decentralized Privacy-Preserving Fair Exchange Scheme for V2G Based on Blockchain
Abstract
With the fast development of the electric vehicle (EV) technology, EVs are expected to be the mainstream in future. The large number of EVs facilitate development of the emerging vehicle-to-grid (V2G) technology, which realizes two-way electricity flows between EVs and the power grid. How to achieve fairness and privacy for EVs during electricity/service exchanges remains a challenging problem for V2G. In this article, we propose a privacy-preserving fair exchange schemeV2GExfor V2G based on the blockchain. V2GEx is composed of an extended blockchain that supports zero-knowledge funds, a fair exchange smart contract based on the hashchain micropayment mechanism, and a privacy-preserving protocol for V2G. We further propose a simpler and more efficient scheme called Uni-V2GEx, which preserves privacy for only one party. We also provide a rigorous security proof under the universal composability (UC) model to prove V2GEx's security. To evaluate its efficiency, we implement V2GEx and conduct comprehensive experiments to test its performance in terms of computation cost and processing delay. The experiment results show that V2GEx is highly efficient in that verification of V2GEx transactions costs only 20 ms and the average transaction processing latency is around 6 seconds in a 200-node blockchain network.
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?
A privacy-preserving energy trading scheme in [143] used blockchain and ZKP to ensure confidentiality in energy transaction between EVs and the power grid.
“To further optimize resources and transactions, a privacy-preserving energy trading scheme in [143] used blockchain and ZKP, to ensure confidentiality in energy transaction between EVs and the power grid.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [143]
Cites
ZebraLancer: Private and Anonymous Crowdsourcing System atop Open Blockchain
Lu, Y., Tang, Q., & Wang, G. (2018) · 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00087 · Cited by 224 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
- Authors
- Wan, Z., Zhang, T., Liu, W., Wang, M., & Zhu, L.
- Affiliations
- Shandong University (CN)
- Beijing Institute of Technology (CN)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2022-07-01
- Volume
- 19
- Issue
- 4
- Pages
- 2442-2456
- ISSN
- 1545-5971 (ISSN-L)
- 1941-0018
- 2160-9209
- OpenAlex
- W3130918512
- Cited by
- 44
- References
- 1
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 12 · 2023: 10 · 2022: 5 · 2021: 1
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- Funders
- Natural Science Foundation of Shandong Province (ZR2020ZD01, ZR2019MF007)
- National Natural Science Foundation of China (61972229)
- Fundamental Research Funds of Shandong University
- National Key Research and Development Program of China (2019YFE0118400)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Keywords
- vehicle-to-grid, blockchain, fair exchange, privacy preservation, electric vehicles, smart contracts, transaction verification
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