A Self-Adaptive Audit Method of Data Integrity in the Cloud Storage

As an important issue of cloud storage security, data integrity checking has attracted a lot of attention from academia and industry. In order to verify data integrity in the cloud, the researchers have proposed many public audit schemes for data integrity. However, most of the existing schemes are inefficient and waste much computing resource because they adopt fixed parameters for auditing all the files. In other words, they have not considered the issue of coordinating and auditing the large-scale files. In order to improve the audit efficiency of the system, we propose a self-adaptive provable data possession (SA-PDP), which uses a self-adaptive algorithm to adjust the audit tasks for different files and manage the tasks by the audit queues. By the quantitative analysis of the audit requirements of files, it can dynamically adjust the audit plans, which guarantees the dynamic matching between the audit requirements of files and the execution strength of audit plans. In order to enhance the flexibility of updating audit plans, SA-PDP designs two different update algorithms of audit plans on the basis of different initiators. The active update algorithm ensures that the audit system has high coverage rate while the lazy update algorithm can make the audit system timely meet the audit requirements of files. Experimental results show that SA-PDP can reduce more than 50% of the total audit time than the traditional method. And SA-PDP effectively increases the number of audit files in the audit system. Compared with the traditional audit method, SA-PDP can improve the standard-reaching rate of audit plans by more than 30%.