Design a hybrid Optimization and Homomorphic Encryption for Securing Data in a Cloud Environment

Abstract

Cloud Computing (CC) is denoted as web-based computing that offers devices or users a shared pool of information, resources, or software. It permits small companies and end-users for making the use of different computational resources such as software, storage, and processing ability offered via other companies. But the main problem in CC is data security because of malware and attacks. So this paper developed a novel Hybrid Bat and Cuckoo-based Pallier Homomorphic Encryption (HBC-PHE) scheme for enhancing the data security of the cloud from malware and attacks. Initially, collected datasets are stored in the cloud using the python tool, and collected datasets are transferred into the developed HBC-PHE framework. At first, generate the key for each dataset and separate the private key for all datasets. Moreover, convert the plain text into ciphertext using the bat and cuckoo fitness function in PHE. Finally, cloud-stored data are encrypted successfully and the attained performance outcomes of the developed framework are associated with other existing techniques in terms of confidential rate, decryption time, encryption time, efficiency, and throughput. Additionally, the developed model gained a throughput of 654Kbps, decryption time of 0.05ms, encryption time of 0.08ms, and efficiency of 98.34% for 500kb. As well, the designed model gained a confidential rate of 98.7% and a computation time of 0.03s for using a 500 kb.