Data verification using block level batch auditing on multi-cloud server

In cloud computing, storage as a service provides an on-demand, flexible data sharing across the networks. This reduces the burden of local data storage management and avoidance of resource maintenance (Hardware or software). In this paradigm, data owner loses the control of the outsourced data, once the data leaves the data owner premises. Due to this, the data on an untrusted cloud server is at risk in terms of integrity, confidentiality and availability of the outsourced data. In order to maintain the outsourced data without corruption from the internal or external adversary, an efficient data auditing verification method is required for data verification. In this paper, we propose a flexible data auditing method using block level auditing of data distributed on multiple cloud servers. This method utilizes the Computational Diffie-Hellman (CDH) and Decisional Diffie-Hellman (DDH) problem solving techniques. The performance of data verification with different sizes of data blocks on multiple servers. Compared to the existing methods of data auditing the proposed method minimizes the computation, communication and storage overheads.