A secure technique for unstructured big data using clustering method

Abstract

Analyzing the multi-dimensional data in faster way is an important and basic aspect in any clustering mechanism. At the same time the clustering mechanism should provide a security structure to protect data loss by preventing distinguish security attacks and transmission errors. Also, to control the data size, security overheads and data loss due to data and time overheads during clustering of unstructured and uncertain big data, a data compression system is also needed. Among the various researches, the current literature fails to suggest any integrated technique to solve these issues in a combinatorial way. Henceforth, this research provides a new compact clustering technique which prevents data loss by including SDES encryption technique to protect security attacks, a new error control scheme to address any number of transmission errors and Huffman compression to control the data size and extra security overheads. Apart from that, the faster execution of proposed integrated technique reduces the time overhead. The experimental result shows, it offers higher data integrity by producing lower percentage of Information Loss, higher SNR and compression ratio. Furthermore, the capacity to produce higher Throughputs and low Cyclomatic Complexity shows its time efficiencies.