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A Novel Approach for Privacy Preservation in Blockchain Network Using Tensor Product and a Hybrid Swarm Intelligence

A Novel Approach for Privacy Preservation in Blockchain Network Using Tensor Product and a Hybrid Swarm Intelligence

Yogesh Sharma, Balamurugan Balusamy
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 20
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781799860112|DOI: 10.4018/IJMCMC.289164
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MLA

Sharma, Yogesh, and Balamurugan Balusamy. "A Novel Approach for Privacy Preservation in Blockchain Network Using Tensor Product and a Hybrid Swarm Intelligence." IJMCMC vol.12, no.4 2021: pp.52-71. http://doi.org/10.4018/IJMCMC.289164

APA

Sharma, Y. & Balusamy, B. (2021). A Novel Approach for Privacy Preservation in Blockchain Network Using Tensor Product and a Hybrid Swarm Intelligence. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 12(4), 52-71. http://doi.org/10.4018/IJMCMC.289164

Chicago

Sharma, Yogesh, and Balamurugan Balusamy. "A Novel Approach for Privacy Preservation in Blockchain Network Using Tensor Product and a Hybrid Swarm Intelligence," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 12, no.4: 52-71. http://doi.org/10.4018/IJMCMC.289164

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Abstract

Blockchain-based technique is developed for privacy protection using tensor product and a hybrid swarm intelligence-based coefficient generation. Initially, the blockchain data with mixed attributes was subjected to the privacy preservation process, in which the raw data matrix and solitude and utility (SU) coefficient were multiplied through the tensor product. Thus, the derivation of the SU coefficient, which handles both sensitive information and utility, was formulated as a searching problem. Then, the proposed algorithm was introduced to evaluate the SU coefficient. The performance of the developed technique was evaluated by means of accuracy and information loss. The achieved results have shown that the developed hybrid sward intelligence reached a maximal accuracy of 0.840 and minimal information loss of 0.159 using dataset-2 compared to the existing system.

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