EDSRPPC: An efficient data storage and retrieval through personalization and prediction in cloud

In cloud computing, remote based massive and scalable data storage services are provided to the users. Data storage and retrieval services need to be modernized for secure communication in the cloud. So, in this paper, user centric personalization and prediction based searching techniques have been proposed to improve the data storage and retrieval services in cloud. Personalization is the concept of categorizing the data based on the user needs and prediction, where the relevant files needed by a user are retrieved based on the user requirement. Personalization is achieved by categorizing the user's files storage. The middleware is used as a support to the data owner, to perform all the necessary encryption and hashing required for secure storage. The data in the cloud server is partitioned into clusters based on the category and similarity. When a user requests for a file, the middleware receives a set of similar files from the cloud server from which the most relevant file is predicted and returned to the user based on the search log and access rights of the user by boosting and bagging techniques. The performance of the EDSRPPC system is measured based on the rate of relevancy of the returned files and also the time required for retrieval of the files from the cloud storage. The experimental result proves that EDSRPPC system as an efficient personalization and prediction framework for data storage and retrieval.