Journal Articles (All Issues)



P.M. Pazhani Selvam, Dr. S.S. Sujatha, Dr. K.K. Thanammal

Keyword Cloud computing, Blockchain, Machine Learning, Boneh–Lynn–Shacham, Schulze Voting Consensus Perceptron, Neural Network


In today’s world, the transaction through the cloud computing environment has experienced considerable awareness and constructs several applications that can significantly operates large amounts of records on an increasing demand globally. As a consequence of these enormous developments, the uneasiness elevates in terms of security threats in the cloud computing environment. Thus, we need to identify a robust solution that can maintain the confidentiality and authenticity of data while ensuring appropriate services. Recently, blockchain technology handles these issues on numerous platforms via Machine Learning techniques to share transaction records by storing the immutably. However, the traditional blockchain architecture gives rise to numerous issues as a consequence of constrained potentialities, therefore compromising transaction latency and communication cost. Also with the increasing size of blockchain, data flow also increases and also affecting data confidentiality. Hence, to overcome these challenges, in this work, a novel method called, Blockchain-based Boneh Lynn Schulze Voting and Perceptron Neural (BBLSV-PN) secured data communication in cloud computing environment is proposed. In the BBLSV-PN method, the registration of a cloud data owner is performed to enable legitimate access control thus ensuring traceability. Moreover, the communications take place in the form of transactions of blockchain via Cloud Server in cloud computing environment. We propose a lightweight consensus mechanism using Boneh–Lynn–Shacham-based Transaction Block Generation and Schulze Voting Consensus-based Block Validation to ensure that each cloud data owner is in control of its block. The proposed method also handles the secure data communication between cloud data owner and cloud user in cloud computing environment by employing Perceptron Neural Network-based secure communication model. The proposed method has been validated in a simulated CloudSim environment and the results are promising in terms of numerous metrics. Numerical validations have also been provided in context of transaction latency, communication cost and data confidentiality rate.


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Vol. 41 No. 10 (2022)