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Cloud storage is an incipient technology in today's world. Lack of security in cloud environment is one of the primary challenges faced these days. This scenario poses new security issues and it forms the crux of the current work. The current study proposes Secure Interactional Proof System (SIPS) to address this challenge. This methodology has a few key essential components listed herewith to strengthen the security such as authentication, confidentiality, access control, integrity and the group of components such as AVK Scheme (Access List, Verifier and Key Generator). It is challenging for every user to prove their identity to the verifier who maintains the access list. Verification is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication process. Here, RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric encryption. Various methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS protocol. The proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality, authentication and access control. Stochastic Timed Petri (STPN) Net evaluation tool was used to verify and prove the formal analysis of SIPS methodology. This evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.

Data mining is the process of extracting knowledge from the huge amount of data. The data can be stored in databases and information repositories. Data mining task can be divided into two models descriptive and predictive model. In Predictive model we can predict the values from different set of sample data, they are classified into three types such as classification, regression and time series. Descriptive model enables us to determine patterns in a sample data and sub-divided into clustering, summarization and association rules. Clustering creates group of classes based on the patterns and relationship between the data. There are different types of clustering algorithms partition, density based algorithm. In this paper we are analysing and comparing the various clustering algorithm by using WEKA tool to find out which algorithm will be more comfortable for the users.

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