Security and privacy in edge computing: a survey of electric vehicles✩

Honghao Gao; Wanqiu Huang; Yueshen Xu; Youhuizi Li

doi:10.1016/j.dcan.2025.10.003

›› 2026, Vol. 12 ›› Issue (2) :223 -235. DOI: 10.1016/j.dcan.2025.10.003

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research-article

Security and privacy in edge computing: a survey of electric vehicles^✩

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^a School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

^b College of Future Industry, Gachon University, Gyeonggi-Do 461-701, South Korea

^c College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

^d School of Computer Science and Technology, Xidian University, Xi’an, 710126, China

^e School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

^* gaohonghao@shu.edu.cn (H. Gao), huangwanqiu@zju.edu.cn (W. Huang),

ysxu@xidian.edu.cn (Y. Xu), huizi@hdu.edu.cn (Y. Li).

Honghao Gao is currently a full professor with the School of Computer Engineering and Science, Shanghai University, Shanghai, China. His research interests include Soft-ware Intelligence, Cloud/Edge Computing, and AIoT. He has more than 200 publications, including IEEE TII, IEEE T-ITS, IEEE TNNLS, IEEE TSC, IEEE TFS, etc. Prof. Gao is a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the British Computer Society (BCS), and a Member of the European Academy of Sciences and Arts (EASA).

Wanqiu Huang is currently a Ph.D candidate with the College of Computer Science and Technology, Zhejiang University, Hangzhou, China. She received her master degree from Shanghai University, Shanghai, China. Her research interests include edge comput-ing, mobile computing, and privacy protection. She is expected to obtain her Ph.D degree in 2028.

Yueshen Xu is an associate professor with the School of Computer Science and Tech-nology, Xidian University, Xi’an, China. He received his Ph.D. degree from Zhejiang Uni-versity, Hangzhou, China, and was a visiting scholar in University of Illinois at Chicago, Chicago, USA. His research focuses on edge computing, software engineering, and dis-tributed computing. He has published more than 80 papers in prestigious conference and journals such as IEEE TSC, IEEE IoT-J, IEEE TITS, IEEE TII, IEEE TVT, IEEE TNSE, IEEE TGCN, IEEE TETCI, IEEE JSTARS, FGCS, SIGKDD, WWW, ICWS, and ICSOC. He was rec-ognized as one of the “World’s Top 2% Scientists in 2023”.

Youhuizi Li is currently an associate professor with the School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China. Her research interests include edge computing and privacy protection. She received the B.E. degree in computer science from Xidian University, Xi’an, China, in 2010, and the Ph.D. degree in computer science from Wayne State University, Detroit, MI, USA, in 2016. Her research interests include edge computing and privacy protection. She was an Editor of Sustainable Com-puting: Informatics and Systems and a Reviewer of IEEE Internet of Things Journal and Mobile Networks and Applications.

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Abstract

Electric Vehicles (EVs) have developed into a complex ecosystem that includes many technical components such as task oﬄoading on mobile devices, the Internet of Vehicles (IoV), and smart grids. Moreover, Edge Computing (EC) is a technique that relocates applications and services closer to end-users. This computing paradigm has been extensively adopted across many scenarios, eﬀectively reducing the load on the cloud computing infrastructure and centralized server facilities. EVs are closely related to EC in many aspects since electric vehicles are typically supported by modern communication and Artificial Intelligence (AI) technologies, such as, sensor networks, computation oﬄoading, autonomous systems, and blockchain. However, the diversity and heterogeneity of edge devices have raised many security and privacy concerns in electric vehicles, and some complex EC scenarios make addressing these issues even more challenging. In this paper, we provide a comprehensive review of the security and privacy concerns raised by EC in EVs. First, we elaborate on the development, characteristics, and applications of EC in EVs. Next, we describe the typical architectures used to ensure the security and privacy of EC in EVs. Then, we analyze the risks and challenges related to the security and privacy of EC in EVs, focusing on several significant scenarios (e.g., oﬄoading, the IoV, and smart grids). We also discuss current research progress on the security and privacy, covering methodologies, architectures, algorithms, insights, and performance. Finally, we discuss several future challenges and issues regarding the security and privacy of EC in EVs.

Keywords

Electric vehicle / Edge computing / Security and privacy / Internet of vehicle / Smart grid

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Honghao Gao, Wanqiu Huang, Yueshen Xu, Youhuizi Li. Security and privacy in edge computing: a survey of electric vehicles^✩. , 2026, 12(2): 223-235 DOI:10.1016/j.dcan.2025.10.003

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CRediT authorship contribution statement

Honghao Gao: Writing-original draft, Supervision, Project admin-istration, Investigation, Funding acquisition, Conceptualization. Wan-qiu Huang: Writing-original draft, Visualization, Investigation, Data curation. Yueshen Xu: Writing-review & editing, Software, Method-ology, Funding acquisition, Conceptualization. Youhuizi Li: Writing-review & editing, Validation, Resources.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work is supported by the National Key R&D Program of China under grant No. 2022YFF0902500, the National Natural Science Foun-dation of China under grant No. 92367103 and No. 62472338, and the Open Foundation of Yunnan Key Laboratory of Software Engineering under grant No. 2023SE301.

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	P. Ranaweera, A.D. Jurcut, M. Liyanage, Survey on multi-access edge computing security and privacy, IEEE Commun. Surv. Tutor. 23 (2) (2021) 1078-1124.

[2]	J. Li, S. Nejati, M. Sabetzadeh, Using genetic programming to build self-adaptivity into software-defined networks, ACM Trans. Auton. Adapt. Syst. 19 (1) (2024) 2:1-2:35.

[3]	Y. Su, J. Teh, C. Chen, Optimal dispatching for AC/DC hybrid distribution systems with electric vehicles: application of cloud-edge-device cooperation, IEEE Trans. In-tell. Transp. Syst. 25 (3) (2024) 3128-3139.

[4]	S. Shirvani, Y. Baseri, A.A. Ghorbani, Evaluation framework for electric vehicle se-curity risk assessment, IEEE Trans. Intell. Transp. Syst. 25 (1) (2024) 33-56.

[5]	P. Wang, Y. Yang, Y. Xia, K. Wang, X. Zhang, S. Wang, Information maximizing adaptation network with label distribution priors for unsupervised domain adapta-tion, IEEE Trans. Multimed. 25 (2023) 6026-6039.

[6]	L. Baresi, D.Y.X. Hu, G. Quattrocchi, L. Terracciano, NEPTUNE: a comprehensive framework for managing serverless functions at the edge, ACM Trans. Auton. Adapt. Syst. 19 (1) (2024) 7:1-7:32.

[7]	ETSI Industry Specification Group (ISG) MEC, Multi-access edge computing (mec) framework and reference architecture, in: ETSI GS MEC, European Telecommunica-tions Standards Institute (ETSI), 2018.

[8]	X. Sun, N. Ansari, Edgeiot: mobile edge computing for the Internet of things, IEEE Commun. Mag. 54 (12) (2016) 22-29.

[9]	T. Suganuma, T. Oide, S. Kitagami, K. Sugawara, N. Shiratori, Multiagent-based flex-ible edge computing architecture for iot, IEEE Netw. 32 (1) (2018) 16-23.

[10]	M.S. Hossain, M.S. Islam, M.A. Rahman, A cyber range framework for emulating secure and private it/ot consumer service verticals towards 6g, IEEE Trans. Consum. Electron. 70 (2) (2024) 4709-4716.

[11]	H. Lin, S. Zeadally, Z. Chen, H. Labiod, L. Wang, A survey on computation oﬄoading modeling for edge computing, J. Netw. Comput. Appl. 169 (2020) 102781.

[12]	K. Cao, Y. Liu, G. Meng, Q. Sun, An overview on edge computing research, IEEE Access 8 (2020) 85714-85728.

[13]	C. Liu, H.F. Yang, M. Zhu, F. Wang, T. Vaa, Y. Wang, Real-time multi-task environ-mental perception system for traﬃc safety empowered by edge artificial intelligence, IEEE Trans. Intell. Transp. Syst. 25 (1) (2024) 517-531.

[14]	Y. Mao, C. You, J. Zhang, K. Huang, K.B. Letaief, A survey on mobile edge com-puting: the communication perspective, IEEE Commun. Surv. Tutor. 19 (4) (2017) 2322-2358.

[15]	Y. Xu, W. Xiao, X. Yang, R. Li, Y. Yin, Z. Jiang, Towards eﬀective semantic anno-tation for mobile and edge services for Internet-of-things ecosystems, Future Gener. Comput. Syst. 139 (2023) 64-73.

[16]	D. Liu, Z. Yan, W. Ding, M. Atiquzzaman, A survey on secure data analytics in edge computing, IEEE Internet Things J. 6 (3) (2019) 4946-4967.

[17]	K. Sadatdiynov, L. Cui, L. Zhang, J.Z. Huang, S. Salloum, M.S. Mahmud, A review of optimization methods for computation oﬄoading in edge computing networks, Digit. Commun. Netw. 9 (2) (2023) 450-461.

[18]	Z. Xu, W. Liu, J. Huang, C. Yang, J. Lu, H. Tan, Artificial intelligence for securing iot services in edge computing: a survey, Secur. Commun. Netw. 2020 (2020) 1-13.

[19]	Y. Xiao, Y. Jia, C. Liu, X. Cheng, J. Yu, W. Lv, Edge computing security: state of the art and challenges, Proc. IEEE 107 (8) (2019) 1608-1631.

[20]	H. Gao, B. Qiu, R.J.D. Barroso, W. Hussain, Y. Xu, X. Wang, Tsmae: a novel anomaly detection approach for Internet of things time series data using memory-augmented autoencoder, IEEE Trans. Netw. Sci. Eng. 10 (5) (2023) 2978-2990.

[21]	S.N. Shirazi, A. Gouglidis, A. Farshad, D. Hutchison, The extended cloud: review and analysis of mobile edge computing and fog from a security and resilience perspective, IEEE J. Sel. Areas Commun. 35 (11) (2017) 2586-2595.

[22]	J. Zhang, B. Chen, Y. Zhao, X. Cheng, F. Hu, Data security and privacy-preserving in edge computing paradigm: survey and open issues, IEEE Access 6 (2018) 18209-18237.

[23]	H. Zeyu, X. Geming, W. Zhaohang, Y. Sen, Survey on edge computing security, in: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), IEEE, 2020, pp. 96-105.

[24]	D. He, S. Chan, M. Guizani, Security in the Internet of things supported by mobile edge computing, IEEE Commun. Mag. 56 (8) (2018) 56-61.

[25]	R. Roman, J. Lopez, M. Mambo, Mobile edge computing fog et al.: a survey and analysis of security threats and challenges, Future Gener. Comput. Syst. 78 (2018) 680-698.

[26]	B. Ali, M.A. Gregory, S. Li, Multi-access edge computing architecture, data security and privacy: a review, IEEE Access 9 (2021) 18706-18721.

[27]	I. Stojmenovic, S. Wen, X. Huang, H. Luan, An overview of fog computing and its security issues, Concurr. Comput., Pract. Exp. 28 (10) (2016) 2991-3005.

[28]	H. Gao, X. Wang, W. Wei, A. Al-Dulaimi, Y. Xu, Com-ddpg: task oﬄoading based on multiagent reinforcement learning for information-communication-enhanced mobile edge computing in the Internet of vehicles, IEEE Trans. Veh. Technol. 73 (1) (2024) 348-361.

[29]	Y. Li, Y. Wang, G. Li, X. Tong, Z. Cai, Determining task assignments for candidate workers based on trajectory prediction, IEEE Trans. Mob. Comput. 24 (5) (2025) 3890-3902.

[30]	W. Wu, X. Wang, F. Zhou, K.-K. Wong, C. Li, B. Wang, Resource allocation for enhanc-ing oﬄoading security in noma-enabled mec networks, IEEE Syst. J. 15 (3) (2020) 3789-3792.

[31]	I.A. Elgendy, W. Zhang, Y.-C. Tian, K. Li, Resource allocation and computation of-floading with data security for mobile edge computing, Future Gener. Comput. Syst. 100 (2019) 531-541.

[32]	Y. Liu, W. Wang, H.-H. Chen, F. Lyu, L. Wang, W. Meng, X. Shen, Physical layer security assisted computation oﬄoading in intelligently connected vehicle networks, IEEE Trans. Wirel. Commun. 20 (6) (2021) 3555-3570.

[33]	Z. Li, H. Hu, H. Hu, B. Huang, J. Ge, V. Chang, Security and energy-aware collabora-tive task oﬄoading in d2d communication, Future Gener. Comput. Syst. 118 (2021) 358-373.

[34]	Y. Wang, W. Zhang, H. Deng, X. Li, Eﬃcient resource allocation for security-aware task oﬄoading in mec system using dvs, Electronics 11 (19) (2022) 3032.

[35]	I.A. Elgendy, A. Muthanna, M. Hammoudeh, H. Shaiba, D. Unal, M. Khayyat, Ad-vanced deep learning for resource allocation and security aware data oﬄoading in industrial mobile edge computing, Big Data 9 (4) (2021) 265-278.

[36]	B. Huang, Y. Li, Z. Li, L. Pan, S. Wang, Y. Xu, H. Hu, Security and cost-aware compu-tation oﬄoading via deep reinforcement learning in mobile edge computing, Wirel. Commun. Mob. Comput. 2019 (2019) 1-20.

[37]	J. Ren, J. Li, H. Liu, T. Qin, Task oﬄoading strategy with emergency handling and blockchain security in sdn-empowered and fog-assisted healthcare iot, Tsinghua Sci. Technol. 27 (4) (2021) 760-776.

[38]	M. Al-Mutawa, S. Mishra, Data partitioning: an approach to preserving data pri-vacy in computation oﬄoad in pervasive computing systems,in:Proceedings of the 10th ACM Symposium on QoS and Security for Wireless and Mobile Networks, ACM, 2014, pp. 51-60.

[39]	N. Dhanya, G. Kousalya, Adaptive and secure application partitioning for oﬄoading in mobile cloud computing, in: Proceedings of the Third International Symposium on Security in Computing and Communications, Springer, 2015, pp. 45-53.

[40]	X. He, R. Jin, H. Dai, Deep pds-learning for privacy-aware oﬄoading in mec-enabled iot, IEEE Internet Things J. 6 (3) (2018) 4547-4555.

[41]	X. Xu, C. He, Z. Xu, L. Qi, S. Wan, M.Z.A. Bhuiyan, Joint optimization of oﬄoading utility and privacy for edge computing enabled iot, IEEE Internet Things J. 7 (4) (2019) 2622-2629.

[42]	X. Xu, X. Liu, Z. Xu, F. Dai, X. Zhang, L. Qi, Trust-oriented iot service placement for smart cities in edge computing, IEEE Internet Things J. 7 (5) (2019) 4084-4091.

[43]	X. Xu, Y. Xue, L. Qi, Y. Yuan, X. Zhang, T. Umer, S. Wan, An edge computing-enabled computation oﬄoading method with privacy preservation for Internet of connected vehicles, Future Gener. Comput. Syst. 96 (2019) 89-100.

[44]	P. Lin, Q. Song, F.R. Yu, D. Wang, L. Guo, Task oﬄoading for wireless vr-enabled medical treatment with blockchain security using collective reinforcement learning, IEEE Internet Things J. 8 (21) (2021) 15749-15761.

[45]	M. Adhikari, A. Munusamy, A. Hazra, V.G. Menon, V. Anavangot, D. Puthal, Security in edge-centric intelligent Internet of vehicles: issues and remedies, IEEE Consum. Electron. Mag. 11 (6) (2021) 24-31.

[46]	X. Xu, Q. Huang, H. Zhu, S. Sharma, X. Zhang, L. Qi, M.Z.A. Bhuiyan, Secure ser-vice oﬄoading for Internet of vehicles in sdn-enabled mobile edge computing, IEEE Trans. Intell. Transp. Syst. 22 (6) (2020) 3720-3729.

[47]	K. Wang, X. Wang, X. Liu, A high reliable computing oﬄoading strategy using deep reinforcement learning for iovs in edge computing, J. Grid Comput. 19 (2021) 1-15.

[48]	P.K. Lahiri, D. Das, W. Mansoor, S. Banerjee, P. Chatterjee, A trustworthy blockchain based framework for impregnable iov in edge computing, in: 2020 IEEE 17th Inter-national Conference on Mobile Ad Hoc and Sensor Systems (MASS), IEEE, 2020, pp. 26-31.

[49]	X. Hou, Z. Ren, J. Wang, W. Cheng, Y. Ren, K.-C. Chen, H. Zhang, Reliable com-putation oﬄoading for edge-computing-enabled software-defined iov, IEEE Internet Things J. 7 (8) (2020) 7097-7111.

[50]	L. Cui, Z. Chen, S. Yang, Z. Ming, Q. Li, Y. Zhou, S. Chen, Q. Lu, A blockchain-based containerized edge computing platform for the Internet of vehicles, IEEE Internet Things J. 8 (4) (2020) 2395-2408.

[51]	Q. Wang, D. Gao, C.H. Foh, V.C. Leung, Reducing revocation latency in iov using edge computing and permissioned blockchain, in: ICC 2022-IEEE International Con-ference on Communications, IEEE, 2022, pp. 2768-2773.

[52]	C. Feng, K. Yu, M. Aloqaily, M. Alazab, Z. Lv, S. Mumtaz, Attribute-based encryp-tion with parallel outsourced decryption for edge intelligent iov, IEEE Trans. Veh. Technol. 69 (11) (2020) 13784-13795.

[53]	X. Xu, Q. Wu, L. Qi, W. Dou, S.-B. Tsai, M.Z.A. Bhuiyan, Trust-aware service of-floading for video surveillance in edge computing enabled Internet of vehicles, IEEE Trans. Intell. Transp. Syst. 22 (3) (2020) 1787-1796.

[54]	Q. Mei, H. Xiong, Y. Zhao, K.-H. Yeh, Toward blockchain-enabled iov with edge computing: eﬃcient and privacy-preserving vehicular communication and dynamic updating, in: 2021 IEEE Conference on Dependable and Secure Computing (DSC), IEEE, 2021, pp. 1-8.

[55]	X. Xu, W. Liu, Y. Zhang, X. Zhang, W. Dou, L. Qi, M.Z.A. Bhuiyan, Psdf: privacy-aware iov service deployment with federated learning in cloud-edge computing, ACM Trans. Intell. Syst. Technol. 13 (5) (2022) 1-22.

[56]	M. Firdaus, S. Rahmadika, K.-H. Rhee, Decentralized trusted data sharing manage-ment on Internet of vehicle edge computing (iovec) networks using consortium blockchain, Sensors 21 (7) (2021) 2410.

[57]	W. Liu, X. Xu, L. Qi, X. Zhang, W. Dou, Godeep: intelligent iov service deployment and execution with privacy preservation in cloud-edge computing, in: 2021 IEEE International Conference on Web Services (ICWS), IEEE, 2021, pp. 579-587.

[58]	S. Wang, J. Li, G. Wu, H. Chen, S. Sun, Joint optimization of task oﬄoading and resource allocation based on diﬀerential privacy in vehicular edge computing, IEEE Trans. Comput. Soc. Syst. 9 (1) (2021) 109-119.

[59]	J. Luo, H. Liu, Q. Cheng, Cooperative privacy preservation for Internet of vehicles with mobile edge computing, in: Proceedings of the 11th International Symposium on Cyberspace Safety and Security, Springer, 2019, pp. 289-303.

[60]	Q. Mei, H. Xiong, Y.-C. Chen, C.-M. Chen, Blockchain-enabled privacy-preserving authentication mechanism for transportation cps with cloud-edge computing, IEEE Trans. Eng. Manag. 71 (2022) 12463-12474.

[61]	Y. Xiao, Y. Liu, T. Li, Edge computing and blockchain for quick fake news detection in iov, Sensors 20 (16) (2020) 4360.

[62]	N.V. Abhishek, T.J. Lim, Trust-based adversary detection in edge computing assisted vehicular networks, J. Commun. Netw. 24 (4) (2022) 451-462.

[63]	X. Li, T. Chen, Q. Cheng, S. Ma, J. Ma, Smart applications in edge computing: overview on authentication and data security, IEEE Internet Things J. 8 (6) (2020) 4063-4080.

[64]	Y. Kang, S. Guo, P. Li, Y. Yang, Edge computing based privacy-preserving data aggregation scheme in smart grid, in: 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC), IEEE, 2020, pp. 1-6.

[65]	Y. Yang, J. Guo, Q. Ye, Y. Xia, P. Yang, A. Ullah, K. Muhammad, A weighted multi-feature transfer learning framework for intelligent medical decision making, Appl. Soft Comput. 105 (2021) 107242.

[66]	J. Wang, L. Wu, K.-K.R. Choo, D. He, Blockchain-based anonymous authentication with key management for smart grid edge computing infrastructure, IEEE Trans. Ind. Inform. 16 (3) (2019) 1984-1992.

[67]	K. Mahmood, X. Li, S.A. Chaudhry, H. Naqvi, S. Kumari, A.K. Sangaiah, J.J. Ro- drigues, Pairing based anonymous and secure key agreement protocol for smart grid edge computing infrastructure, Future Gener. Comput. Syst. 88 (2018) 491-500.

[68]	S.A. Chaudhry, H. Alhakami, A. Baz, F. Al-Turjman, Securing demand response management: a certificate-based access control in smart grid edge computing in-frastructure, IEEE Access 8 (2020) 101235-101243.

[69]	W. Han, Y. Xiao, Edge computing enabled non-technical loss fraud detection for big data security analytic in smart grid, J. Ambient Intell. Humaniz. Comput. 11 (4) (2020) 1697-1708.

[70]	A. Xu, Y. Jiang, J. Wu, Y. Zhang, W. Hou, H. Wen, Y. Zheng, Eﬃciency and security for edge computing assisted smart grids, in: 2019 IEEE Globecom Workshops (GC Wkshps), IEEE, 2019, pp. 1-5.

[71]	Z. Wang, D. Jiang, F. Wang, Z. Lv, R. Nowak, A polymorphic heterogeneous security architecture for edge-enabled smart grids, Sustain. Cities Soc. 67 (2021) 102661.

[72]	K. Fan, Q. Chen, R. Su, K. Zhang, H. Wang, H. Li, Y. Yang, Msiap: a dynamic search-able encryption for privacy-protection on smart grid with cloud-edge-end, IEEE Trans. Cloud Comput. 11 (2) (2023) 1170-1181.

[73]

L. Xiao, J. Cai, M. Qiu, M. Liu,A secure identity authentication protocol for edge data in smart grid environment, in: 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), IEEE, 2021, pp. 188-193.

[74]	K. Gai, Y. Wu, L. Zhu, L. Xu, Y. Zhang, Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks, IEEE Internet Things J. 6 (5) (2019) 7992-8004.