Data Classifier for Encryption in Cloud

The aim of this project is to provide the cloud security infrastructure with a security policy such that the data encryption takes place as per need of data and provide suitable security to data based on the security needs of data with resource optimization in mind, as most of the data stored in cloud database would not be a classier document. Some of, or a high percentage of, data must be no encryption required-data. A model is needed which would not only classify the data but also handle the security concerns of data according to class which could be based on any security parameter, security criteria, or a set of conditions which are to be decided in accordance with the cloud service provider .On the basis of these predefined security criteria classes are formed and eventually various types of policies can be applied to the classes for which the cloud service provider agrees and is capable of. The data have different values and characteristics that must be identified before sending to cloud severs. As per the literature review so far the model present is a combination of K-Nearest Neighbor (K-NN) classifier and the Rivest, Shamir and Adelman (RSA) algorithm ,where data is classi?ed into sensitive and non sensitive data ,only the sensitive data is encrypted using RSA algorithm. In a cloud server, the data are stored in two ways. First encrypt the received data and store on cloud servers. Second store data on the cloud servers without encryption. As the literature states, after implementing this model it is found that the confidentiality level of data is increased and this model is proved to be more cost and memory friendly for the users as well as for the cloud services providers. This classi?cation technique uses binary number of class. The sensitivity of data can vary from more than just two levels. The data can grow two large to handle by K-NN and better technique exists to handle large data sets. For encryption any outsource algorithm would do more appropriate for cloud environment. This project proposes a model with more number of target classes which can handle the sensitivity of data more precisely and satisfy the need of security architecture. The model uses decision tree induction algorithm for classifier and the encryption algorithm can be used in varieties with a single class with no encryption. The classified data is then loaded into the servers into different data centres. The model aims at classi?cation of data on basis of various security parameter or cloud policies