Real-Time Hierarchical Sensitivity Measure-Based Access Restriction for Efficient Data Retrieval in Cloud

摘要

The problem of data retrieval from cloud environment has been discussed in many articles. However, restricting the cloud users from illegal access is identified as a challenging issue. Toward the problem of access restriction, different attribute-based approaches have been discussed earlier but suffer to achieve higher performance in data retrieval also. To solve this issue, an efficient real-time hierarchical sensitivity measure-based access restriction and data retrieval algorithm is presented in this paper. The method classifies the cloud data into different classes according to their importance, and for each user taxonomy of access has been maintained. At the query time, the method estimates hierarchical sensitivity support (HSS) according to the data items being accessed. Based on the HSS on each level, a cumulative data retrieval support (CDRS) is estimated. Estimated CDRS value has been used to perform access restriction and data retrieval. The proposed method improves the performance of data retrieval and access restriction.