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Exploring New Vista of Secured and Optimized Data Slicing for Big Data: An IOT Paradigm

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Abstract

Security and privacy are useful concerns in the context of big data. The Internet of Things (IoT) serves both to bolster and to ease security worries. IoT gadgets raise immense new security challenges, particularly with regards to things like basic framework. Be that as it may, they additionally offer approaches to help keep clients progressively secure by adding additional obstructions of safeguard to information and people. In order to sustain the integrity of data and to provide in order to implicit security for any big database, data slicing is constructive. Data slicing implicitly provides the preservation and the query performance to the database users. The sliced data are stored at servers in a distributed system to protect the data from the attackers. In this article, an intelligent and efficient model is developed to partition the polynomial data securely and to store at various servers in a distributed system. An auto-key generator spawns an encryption key to encrypt the polynomial data as a higher level security. Encrypted data is partitioned by an efficient Fast Fourier transform Technique. A novel clustering methodology entitled as Binary Reverse Clustering is introduced to optimize the performance as well as to reduce the servers’ requisition. Moreover, the novel clustering technique is compared with the traditional clustering algorithm.

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Authors and Affiliations

  1. Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR, India

    Manash Sarkar

  2. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

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  1. Manash Sarkar
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  2. Aboul Ella Hassanien
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Correspondence to Aboul Ella Hassanien.

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Appendix: Program Output

Appendix: Program Output

The Core Logic of the Formulation: The basic Core JAVA code has been developed to quantify the Binary reverse clustering technique. Jdk-6u11-windows-i586-p is used to develop the logic. The output of the program is shown in the following screen shots (Figs. 9 and 10):

Fig. 9
figure 9

Calculating the total number of cluster for 8 data slices

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Fig. 10
figure 10

Calculating the total number of cluster for 32 data slices

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Sarkar, M., Hassanien, A.E. Exploring New Vista of Secured and Optimized Data Slicing for Big Data: An IOT Paradigm. Wireless Pers Commun 116, 601–628 (2021). https://doi.org/10.1007/s11277-020-07730-3

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