Remote Public Data Auditing to Secure Cloud Storage

Cloud computing is a pay-as-you-go business model that offers elastic remote data storage, and computing resources have become necessary due to the emergence of big data. After data outsourcing to the cloud, cloud users lose control over data and are always concerned about data privacy and security in adopting the cloud service model. So, to ensure remote data integrity, a trusted auditor can make auditing tasks according to the users’ request, which is helpful to release auditing overheads on a user device and meaningfully improve the scalability of cloud services. Although numerous data auditing techniques have been designed with TPA so far, these techniques need to improve on data security and efficiency issues. First, these techniques cannot authenticate block indices, so the server can produce valid proof without an original data block to pass the audit process. Second, existing approaches do not include position fields, so the server can replace the tampered data block with a healthy one to pass the audit phase. To overcome these issues, this paper introduces a new public data authentication scheme, ERPDA. The proposed technique incorporated a newly designed Merkle Tree (MT) based structure, Sequence and Position-based Tree (SPT) that minimises computation complexity to find nodes in data audit and avoid data replacement attacks. The experimental outputs showed that our suggested technique is effective with the comparative data auditing techniques in computation overheads, and the security is proved under the random model.