Journal Articles (All Issues)

BLOCKCHAIN BASED SECURE PERCEPTRON NEURAL BONEH LYNN SHACHAM AND SCHULZE VOTING FOR CLOUD COMPUTING ENVIRONMENT

Authors

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

Abstract

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.

References

    [1] Amrita Jyoti, R. K. Chauhan, “A blockchain and smart contract-based data provenance collection and storing in cloud environment”, Wireless Networks, Springer, Mar 2022 [Blockchain and Smart Contrast-based Data Provenance (BSCDP)] [2] Hui Xie, Zhengyuan Zhang, Qi Zhang, Shengjun Wei, Changzhen Hu, “HBRSS: Providing high-secure data communication and manipulation in insecure cloud environments”, Computer Communications, Elsevier, Mar 2021 [Homomorphic Block Ring Security System (HBRSS)] [3] Gagangeet Singh Aujla, Amritpal Singhy, Maninderpal Singhz, Sumit Sharmax, Neeraj Kumar, and Kim-Kwang Raymond Choo, “BloCkEd: Blockchain-based Secure Data Processing Framework in Edge Envisioned V2X Environment”, IEEE Transactions on Vehicular Technology, Oct 2019 [4] Debabrata Samanta, Ahmed H. Alahmadi, Karthikeyan M. P. Mohammad Zubair Khan, Amit Banerjee, Gowtam Kumar Dalapati, Seeram Ramakrishna, “Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture”, IEEE ACC ess, Jul 2021 [5] Dai Meiling, Xu Siya, Shao Sujie, Guo Shaoyong, Qiu Xuesong and Xiong Ao, “Blockchain-Based Reliable Fog-Cloud Service Solution for IIoT”, Chinese Journal of Electronics, March 2021 [6] Zia Ullah, Basit Raza, Habib Shah, Shahzad Khan, Han, Abdul Waheed, “Towards Blockchain-Based Secure Storage and Trusted Data Sharing Scheme for IoT Environment”, IEEE Access, Mar 2022 [7] Abdul Rehman, LIU Jian, Muhammad Qasim Yasin and LI Keqiu, “Securing Cloud Storage by Remote Data Integrity Check with Secured Key Generation”, Chinese Journal of Electronics, May 2021 [8] Vaishnavi Moorthy, Revathi Venkataraman, T. Rama Rao, “Security and privacy attacks during data communication in Software Defined Mobile Clouds”, Computer Communications, Elsevier, Feb 2020 [9] Youcef Ould-Yahi, Samia Bouzefrane, Hanifa Bouchene, Soumya Banerjee, “A data-owner centric privacy model with blockchain and adapted attribute-based encryption for internet-of-things and cloud environment”, International Journal of Information and Computer Security, Inderscience, May 2022 [10] Dilip Venkata Kumar Vengala, D. Kavitha, A. P. Siva Kumar, “Secure data transmission on a distributed cloud server with the help of HMCA and data encryption using optimized CP-ABE-ECC ”, Cluster Computing, Springer, Apr 2020 [11] S. Sridhar, S Smys, “Hybrid RSAECC Based Secure Communication in Mobile Cloud Environment”, Wireless Personal Communications, Springer, Nov 2019 [12] Balasubramanian Prabhu Kavin, Sannasi Ganapathy, U. Kanimozhi, Arputharaj Kannan, “An Enhanced Security Framework for Secured Data Storage and Communications in Cloud Using ECC , Access Control and LDSA”, Wireless Personal Communications, Springer, Jun 2020 [13] Ishu Gupta, Ashutosh Kumar Singh, Chung-Nan Lee, Rajkumar Buyya, “Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions”, IEEE Access, Jul 2022 [14] Wenjuan Li, Jiyi Wu, Jian Cao, Nan Chen, Qifei Zhang and Rajkumar Buyya, “Blockchain-based trust management in Cloud Computing systems: a taxonomy, review and future directions”, Journal of Cloud Computing: Advances, Systems and Applications, Springer, Oct 2021 [15] Pan Yang, Naixue Xiong, Jingli Ren, “Data Security and Privacy Protection for Cloud Storage: A Survey”, IEEE Access, Jul 2020 [16] Ch. V. N. U. Bharathi Murthy, M. Lawanya Shri, Seifedine Kadry, Sangsoon Lim, “Blockchain Based CC: Architecture and Research Challenges”, IEEE Access, Nov 2020 [17] Mohamed Amine Ferrag, Leandros Maglaras,” DeepCoin: A Novel Deep learning and Blockchain-based Energy Exchange Framework for Smart Grids”, IEEE Transactions on Engineering Management, November 2020 [18] Maryam Ataei Nezhad, Hamid Barati, Ali Barati, “An Authentication‑Based Secure Data Aggregation Method in Internet of Things”, Journal of Grid Computing, Springer, Jul 2022 [19] S. Immaculate Shyla, S. S. Sujatha, “Efficient secure data retrieval on cloud using multi‑stage authentication and optimized blowfish algorithm”, Journal of Ambient Intelligence and Humanized Computing, Springer, Jan 2021 [20] Yushu Zhang, Ping Wang, Liming Fang, Xing He, Hao Han, and Bing Chen, “Secure Transmission of Compressed Sampling Data Using Edge Clouds”, IEEE Transactions on Industrial Informatics, Jul 2019

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Published

2022-10-30

Issue

Vol. 41 No. 10 (2022)