Dynamic Service Centric Sensitivity-Based Risk Analysis Model in Secure Cloud

A substantial amount of research has been done to enforce data security and access restriction, in order to achieve higher privacy preservation. This article focused on identifying service threats and analyzing the risk at service and data level towards the security of cloud, using a new approach, said as Dynamic Service Centric Sensitivity-based Risk Analysis Model (DSSRAM). Meta-data are classified under different sensitivity class. The method first manages the Metadata, towards the risk analysis, and measures its Targeted Score (TS). The same has been used for data indexing. Based on the result of risk analysis, the user profile and taxonomy are generated. On the other side, the access restriction is performed by enforcing CFR model. To measure the Service Centric Trust Measure (SCTM), the Coverage-Feature-Response model examines the user behavior using different parameters. Based on SCTM, access restrictions are performed. The method not just analyses the risk at feature level, but also analyses how the services are targeted to perform different threats. Experimental results, the DSSRAM shows the overall performance analysis with varying users and number of features. Analyzing on varying users compared with Access Restriction, its performance is 95% high, and with Metadata management is 95.33% high. False ratio in access restriction is 6% less, and time complexity in access restriction is 22.66% less. The proposed method enhances performance in access restriction, data security, and Meta data management.