Volume II Issue III July-September 2022

Name of Author : Kavitha Mani

Title of the paper : An Efficient BLS Based Homomorphic Auditing Scheme with Modified PSO based Blowfish Encryption Algorithm using ML Technique for Cloud Data Security

Abstract: Cloud computing is a most imperative territory of research, companies and most of the individuals putting away their important information remotely in the cloud is increasing globe wide. Due to high volume of data, it is very difficult to give security of all data in cloud storage. Cloud storage without authentication has inaccurate data security and the outsource data not secure because of hackers. Data privacy and integrity have problems due to the encryption algorithms used in literature. In order to overcome the existing drawbacks, a novel Efficient Boneh-LynnShacham BLS Signature based Homomorphic Auditing Scheme with Modified Partial Swam OptimizationPSO based Blowfish Encryption Algorithm has been proposed. In this present work, the sensitive data has been classified into data blocks using Naïve Bayes ML Technique. The classified data is encrypted as keys by using PSO based Blowfish algorithm. This method enhance the protection capability of cloud storage by decrease the encrypted data size and computation time. The outsourced data stored in Cloud service Provider CSP. The Third Party Auditor TPA is designated for auditing process. The BLS based Homomorphic method has been utilized for auditing process between CSP and TPA. The Third party Auditor verifying the sensitivity of data by contacting Data owner. In addition, while data owner requested for stored data to achieve efficient data integrity by manipulating the classic Merkle Hash Tree construction for block tag authentication. The present proposed work give the better outcomes compared to other methods utilized.

Keywords : Cloud Computing, Data Security, Homomorphic Auditing scheme, Machine learning, Blowfish Encryption.

DOI :

Page No. : 31-48