What question did this study set out to answer?

The research aims to enhance secure authentication and data sharing in Vehicular Ad Hoc Networks (VANETs) using advanced algorithms and encryption methods.

June 4, 2026Open Access

Blockchain-enabled secure authentication and privacy-preserving information sharing in VANETs using adaptive echo state networks and dual trapdoor homomorphic encryption

Puntos clave

The research aims to enhance secure authentication and data sharing in Vehicular Ad Hoc Networks (VANETs) using advanced algorithms and encryption methods.
Developed a blockchain-driven approach for secure data transmission in VANETs.
Utilized Adaptive Echo State Network (AESNet) and dual trapdoor homomorphic encryption method (DTHE-OMK) for authentication.
Employed Position Updated Enhanced Frilled Lizard Optimization (PUE-FLO) to optimize AESNet parameters for improved accuracy.
Observed authentication accuracy improvements with CWO-AESNet at 6.323%, TOT-AESNet at 5.828%, WOA-AESNet at 8.353%, and FLO-AESNet at 6.323%.
Demonstrated DNN accuracy improvements of 7.838%, LSTM at 4.729%, GRU at 4.248%, and ESNet at 4.729%.
Concluded that the proposed method enhances both the integrity and safety of information sharing in VANETs.

Resumen

Abstract A blockchain-driven approach is presented to ensure robust authentication and private data distribution in Vehicular Ad Hoc Networks (VANETs). A primary objective to develop a robust authentication procedure using an Adaptive Echo State Network (AESNet) and a dual trapdoor homomorphic encryption method (DTHE-OMK) to enhance secure data transmission in VANETs. This work employs a combination of optimization strategies, including the Position Updated Enhanced Frilled Lizard Optimization (PUE-FLO) algorithm, to fine-tune the parameters of the AESNet for node authentication. The authentication analysis on accuracy provided results in the range of 6.323% of CWO-AESNet, 5.828% of TOT-AESNet, 8.353% of WOA-AESNet, 6.323% of FLO-AESNet for the optimization algorithms and 7.838% of DNN, 4.729% of LSTM, 4.248% of GRU, and 4.729% of ESNet for various literature models, respectively. This work concludes that the developed approach improves both integrity and safety of information shared across VANETs.

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