Optimized Big Data Storage and Security in Cloud Computing using Advanced Encryption Techniques

Abstract

Cloud security and effective cloud distribution across the network are the primary issues with cloud computing, but data security is a critical challenge for contemporary society. Advanced encryption techniques are used to secure sensitive data and guarantee privacy in a distributed setting significantly enhancing storage and big data security in cloud computing. Therefore, the input data is collected from the Big Data Derby Dataset. Then, lossless compression is applied using the Burrows-Wheeler Transform (BWT) model to minimize redundancy and enhance cloud-based data accessibility. The lossless compressed data is fed into an Enhanced Rivest-Shamir-Adleman Algorithm (ERSA) to improve data encryption and decryption performance as an optimized Modified Binary Banyan Tree Optimization (BBTO) algorithm is utilized to minimize the time involved for encryption and decryption processes. The proposed Big Data storage and security in cloud computing using advanced encryption techniques (BDSS-CC-AET) technique is implemented on the Python platform, and its performance is expected to outperform existing methods with an estimated high throughput (78mbps), high packet delivery ratio (93%) and low encryption time (381ms). Overall, the proposed BDSS-CC-AET is expected to demonstrate superior effectiveness in big data storage and security in cloud computing.