Implementation of ID-based Audit Protocols to Enhance Security and Productivity

Abstract

Cloud storage has gained increasing popularity, as it helps cloud users arbitrarily store and access the related outsourced data. Numerous public audit buildings have been presented to ensure data transparency. However, modern developments have mostly been constructed on the public key infrastructure. To achieve data integrity, the auditor must first authenticate the legality of the public key certificate, which adds to an immense workload for the auditor, in order to ensure that data integrity is accomplished. The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server. One of the main problems for individuals, though, is how to detect data integrity on a term where people have a backup of local files. Meanwhile, a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved. In this work, a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols. The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity. The idea has already been proved safe; its safety is very relevant to the traditional presumption of the Computational Diffie-Hellman security assumption.