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Cloud computing becomes more widely prevailed storage for outsourced data which may contain more sensitive information such as credit card numbers, passwords, e-mails, personal health records etc. As the data owners cannot risk their unencrypted outsourced data so as the cloud servers. The cloud server may fail to keep up the integrity of the cloud data due to hacking or entry of unauthorized entities. While searching the data in the cloud the attackers prefer the keyword which is not secured properly. The existing technique resolves the optimization complexities in ranked keyword search and its effective utilization of remotely stored encrypted cloud data. But it limits the further optimizations of the search results by preventing cloud server to interact with cloud users to maintain the integrity of actual owner’s keyword and the data associated with it. The aim is to define a framework which enhances the accuracy of the ranked keyword search by secured machine learning, which does not affect the data integrity. Introducing new and interactive access permissions allows only specific group of people to guide the search engine. This technique lists the exact or necessary search results for any encrypted keyword. Due to this learning the privacy of the keyword does not get to be violated because, the owner of the encrypted keyword has some lists of users to whom only the machine should learn for secured and improved search results.

S.Saravanan, Arivarasan. I

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