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Security enhanced privacy-preserving data aggregation scheme for intelligent transportation system

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Abstract

The intelligent transportation system can make traffic decisions through the sensory data collected by vehicles to ensure driving safety, improve traffic efficiency and traffic environment. Thus, the system has been widely concerned by industry and academia. However, the intelligent transportation system confronts several challenges in location privacy protection and data security during data aggregation. To solve these challenges, we propose a security-enhanced privacy-preserving data aggregation scheme for the intelligent transportation system, named SEPDA. Specifically, the SEPDA scheme utilizes the Chinese Remainder Theorem, Modified Paillier Cryptosystem and T-N Threshold Sharing to protect the location privacy and information security of vehicles, and obtains the mean and variance in the data report reading and analytics process. The SEPDA also uses the threshold cryptosystem to enhance the security of the traffic management center, which can avoid single-point attacks. Meanwhile, SEPDA employs batch authentication technology to reduce authentication overhead. Detailed security analysis and performance evaluation show that the SEPDA can resist various security threats and has low computational complexity, communication overhead and communication delay.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant No. 61972438), Natural Science Foundation of Anhui Province (Grant No. 2208085QF200), Natural Science Research Project for Universities in Anhui Province (Grant Nos. KJ2021A0090, 2022AH050197 and 2022AH052367), and Wuhu Science and Technology Bureau Project (Grant No. 2023jc17).

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Authors and Affiliations

  1. School of Computer and Information, Anhui Normal University, Wuhu, 241002, China

    Kaizhong Zuo, Xixi Chu, Peng Hu, Tianjiao Ni, Tingting Jin, Fulong Chen & Zhangyi Shen

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  1. Kaizhong Zuo
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  2. Xixi Chu
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  3. Peng Hu
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Correspondence to Peng Hu.

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Zuo, K., Chu, X., Hu, P. et al. Security enhanced privacy-preserving data aggregation scheme for intelligent transportation system. J Supercomput (2024). https://doi.org/10.1007/s11227-024-05995-0

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