Challenges and Opportunities of Privacy-Preserving Computation Techniques in IoV  Edge Services: A Systematic Review and Meta-Analysis

Yinglong Li; Qingyan Jiang; Zishuai Hao; Weiru Liu; Tieming Chen; Jiahui Li

doi:10.1109/TITS.2026.3659435

Challenges and Opportunities of Privacy-Preserving Computation Techniques in IoV Edge Services: A Systematic Review and Meta-Analysis

Yinglong Li^*, Qingyan Jiang, Zishuai Hao, Weiru Liu, Tieming Chen^*, Jiahui Li

^*Corresponding author for this work

School of Engineering Mathematics and Technology

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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Abstract

The evolution of Internet of Vehicles (IoV) technologies, encompassing wireless communications and Artificial Intelligence (AI), has advanced the collaborative “Pedestrian-Vehicle-Road-Cloud” IoV edge services, enhancing road efficiency and driving safety. Operating in an open-edge environment with vast sensory data, IoV faces significant privacy risks from unauthorized access and data breaches. Consequently, privacy-preserving computation (PPC) is crucial for secure IoV services. This paper reviews PPC techniques in IoV edge services, exploring network characteristics and potential privacy attacks. It categorizes and evaluates techniques such as differential privacy, homomorphic encryption, and secure multi-party computation based on data security, utility, and overhead. Summarizing their pros and cons, the challenges and future research directions for IoV edge services are outlined.

Original language	English
Pages (from-to)	3843-3861
Number of pages	19
Journal	IEEE Transactions on Intelligent Transportation Systems
Volume	27
Issue number	4
DOIs	https://doi.org/10.1109/TITS.2026.3659435
Publication status	Published - 6 Feb 2026

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Publisher Copyright:
© 2026 IEEE.

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10.1109/TITS.2026.3659435

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Cite this

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title = "Challenges and Opportunities of Privacy-Preserving Computation Techniques in IoV Edge Services: A Systematic Review and Meta-Analysis",

abstract = "The evolution of Internet of Vehicles (IoV) technologies, encompassing wireless communications and Artificial Intelligence (AI), has advanced the collaborative “Pedestrian-Vehicle-Road-Cloud” IoV edge services, enhancing road efficiency and driving safety. Operating in an open-edge environment with vast sensory data, IoV faces significant privacy risks from unauthorized access and data breaches. Consequently, privacy-preserving computation (PPC) is crucial for secure IoV services. This paper reviews PPC techniques in IoV edge services, exploring network characteristics and potential privacy attacks. It categorizes and evaluates techniques such as differential privacy, homomorphic encryption, and secure multi-party computation based on data security, utility, and overhead. Summarizing their pros and cons, the challenges and future research directions for IoV edge services are outlined.",

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Challenges and Opportunities of Privacy-Preserving Computation Techniques in IoV Edge Services: A Systematic Review and Meta-Analysis. / Li, Yinglong; Jiang, Qingyan; Hao, Zishuai et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 27, No. 4, 06.02.2026, p. 3843-3861.

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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AU - Li, Jiahui

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