Data integrity and secured data storage for performance enhancement in cloud environment

Abstract

In recent years, cloud computing has experienced enormous growth in popularity. Customers can benefit from this new paradigm by outsourcing their processing and storage needs to public suppliers and paying for the services they utilise. Unfortunately, outsourcing data to a cloud service provider presents fresh problems in terms of data security and accuracy. For the protection of outsourced data, numerous data integrity models and methodologies have been put out through the years. A large amount of research has been done on identifying challenges in cloud, with most proposed solutions relying on the Cloud Service Providers (CSP) for data investigations. CSPs are entrusted with the collection and processing of data on cloud. The research work is aimed at mitigating the sole dependency on the CSP for processing the data and protecting them. The proposed models focus on providing data integrity of the log data without compromising the efficiency of the system. The log data is extracted, classified and processed to further layers on the model, preserving integrity through propagated chain of log blocks. In order to preserve the privacy of data while searching, a Minimized Deterministic Finite Automata (mDFA) based encrypted search mechanism is proposed. The model utilizes mDFA key stamp generation and searching techniques that preserves privacy and at the same time proves to be secured against the Keyword Guessing Attacks (KGA). The proposed model regulates the way the user wants to search and return results in an effective manner. newline