Quantum Lamport One-time Digital Signature Cryptography for Secured Data Communication for Industrial Iot
Year: 2025
Type: article
Abstract: Cloud Computing (CC) as well as Internet of Things is broadly employed in Industrial Internet of Things. Industrial Internet of Things is a promising technology through vast amount of smart connected tools containing sensing, storage, as well as computing capacities. Digital signature lately played an increasingly significant role in security. The security is employed in IIoT to protect stored data from unauthorized user access. In today`s growing concern for security, we have developed a novel secured data communication method called Quantum Lamport One-Time Digital Signature Cryptography. It is combined with three major processes: key generation, signing, and verification during secured data communication to enhance the security level of industrial information through improved confidentiality rate, greater integrity, and less time. In a key generation, industrial data as input is considered. The keys are generated by the Cloud Server in the CC for the registered cloud users by Lamport One-Time Key Generation by using linear congruential generator. It employed to sign solitary message and be distinct from another user, therefore ensuring data confidentiality. Next, the Modular Arithmetic-based Digital Signature model is utilized to perform the signing process thereby ensuring authentication accuracy. Finally, verification is carried out for secured data communication for industrial IoT via CC. The proposed Quantum Lamport One-Time Digital Signature Cryptography has estimated by dissimilar metrics where outcomes attained confirm its efficiency in comparison to conventional solutions. It aids to enhance 18% data confidentiality, 9% data integrity, 26% authentication accuracy and reduce 18% space complexity, 35% message delivery time as compared to existing methods.
Source: Mitteilungen Klosterneuburg
DOI: 10.61586/tdl8m
Authors: Flindon W.G, Divya S.V
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4406274899