R&D: Enabling Online/Offline Remote Data Auditing for Secure Cloud Storage

Cluster Computing has published an article written by Qingqing Gan, Xiaoming Wang, Jianwei Li, Jiajun Yan, and Suyu Li, Department of Computer Science, Jinan University, Guangzhou, 510632, China.

Abstract: “The notion of Remote Data Auditing (RDA) has been put forward to achieve the integrity verification for cloud data. However, most of the existing RDA techniques suffer from heavy computational overhead or security attacks. To address this challenge, we introduce an online/offline remote data auditing (OORDA) framework that defines the data auditing process as online and offline phases. Then a concrete OORDA scheme is proposed to ensure secure integrity checking for cloud data. Since some expensive computations are performed offline in advance, the online computational cost can be greatly reduced, which can well solve the performance bottleneck for auditing large-scale data on the auditor. Based on the Computational Diffie–Hellman problem, the proposed OORDA scheme is provably secure in the random oracle model. Performance analysis confirms that the proposed scheme has the optimized efficiency compared with existing schemes, where the average online computational cost can be improved by more than 68% during auditing. Subsequently, a Batch OORDA scheme is presented to support batch data auditing operations, which can reduce the number of pairing operations to constant size at the online phase, independent of the number of data owners. Furthermore, we discuss that the designed file block tag and auditing process can be applied to other related fields, such as verifiable keyword search, verifiable SQL query.“