Privacy Preserving Lightweight Cryptography Scheme for Clustered Vehicular Adhoc Networks

Privacy Preserving Lightweight Cryptography Scheme for Clustered Vehicular Adhoc Networks
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© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Shaji K.A.Theodore, K. Rajiv Gandhi, V. Palanisamy
DOI : 10.14445/22315381/IJETT-V70I7P203

How to Cite?

Shaji K.A.Theodore, K. Rajiv Gandhi, V. Palanisamy , "Privacy Preserving Lightweight Cryptography Scheme for Clustered Vehicular Adhoc Networks" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 24-31, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P203

Abstract
Vehicular ad hoc network (VANET) is typically used in intelligent transportation systems (ITS), which permits the interchange of traffic information amongst close atmosphere and vehicles to establish an efficient driving experience. Security and privacy are becoming a difficult problem that prevails from the safety requirements of the VANET. Some of the specific leakage of vehicle details like route data may lead to severe effects, and thus, privacy-preserving protocols were required to improve security in VANET. This article introduces a novel Sunflower Optimization with Privacy Preserving Lightweight Cryptography (SFO-PPLWC) scheme for Clustered VANET. The major goal of the SFO-PPLWC technique is to cluster the vehicles and enable secure data transmission using the LWC approach. The proposed SFO-PPLWC model comprises a threestage process: weighted cluster scheme, encryption, and optimal key generation. Primarily, the presented SFO-PPLWC model comprises a weighted clustering scheme (WCS) model to group the vehicles. Besides, the Tiny Encryption Algorithm (TEA) is employed to transmit the data securely. Moreover, the SFO algorithm is exploited to choose the keys related to the TEA model optimally. The experimental outcome analysis of the SFO-PPLWC model is tested and compared with existing models. The simulation results highlighted the enhancements of the SFO-PPLWC model over recent models.

Keywords
VANET, Clustering, Privacy preserving, Lightweight cryptography, Security, Metaheuristics.

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