The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Privacy-Preserved Federated Learning for Autonomous Driving
Abstract
In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of concern. The privacy problem is even more severe in autonomous driving business than the other businesses in VEC such as ordinary navigation. Federated learning (FL), which is a privacy-preserved strategy proposed by Google, has become a hot trend to solve the privacy problem in many fields including VEC. Therefore, we introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter only with the help of MEC server. Moreover, different from the common assumption of honest MEC server and honest vehicle in former studies, we take the malicious MEC servers and malicious vehicles into account. First, we consider honest-but-curious MEC server and malicious vehicles and propose a traceable identity-based privacy preserving scheme to protect the vehicular message privacy where improved Dijk-Gentry-Halevi-Vaikutanathan (DGHV) algorithm is proposed and a blockchain-based Reputation-based Incentive Autonomous Driving Mechanism (RIADM) is adopted. Further, when the case comes to the non-credibility of both parties where semi-honest MEC server and malicious vehicles are considered, we propose an anonymous identity-based privacy preserving scheme to protect the identity privacy of vehicles with Zero-Knowledge Proof (ZKP). Based on the simulation of virtual autonomous driving based on real-world road images, it is verified that our proposes scheme can reduce 73.7 % training loss of autonomous driving, increase the accuracy to around 5.55 % while keeps effective privacy of message and identity under the threat of dishonest MEC server and vehicles.
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Cites
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Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
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Cited by
Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhao, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3297996 · Cited by 96
Batool, H., Anjum, A., Khan, A., Izzo, S., Mazzocca, C., & Jeon, G. (2024) · Information Sciences · doi:10.1016/j.ins.2023.119717 · Cited by 89
Papadopoulos, C., Kollias, K.-F., & Fragulis, G. F. (2024) · Future Internet · doi:10.3390/fi16110415 · Cited by 35
Secure and Efficient Blockchain-Based Federated Learning Approach for VANETs
Asad, M., Shaukat, S., Javanmardi, E., Nakazato, J., Bao, N., & Tsukada, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3322221 · Cited by 32
Privacy-Encoded Federated Learning Against Gradient-Based Data Reconstruction Attacks
Liu, H., Li, B., Gao, C., Xie, P., & Zhao, C. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3309095 · Cited by 27
Metadata
- Authors
- Li, Y., Tao, X., Zhang, X., Liu, J., & Xu, J.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-07
- Volume
- 23
- Issue
- 7
- Pages
- 8423-8434
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3175663678
- Cited by
- 259
- References
- 37
- FWCI
- 13.32
- Type
- journal article
- Language
- en
- Open access
- closed
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