An Efficient Data Auditing Protocol With a Novel Sampling Verification Algorithm

Abstract

Currently, data auditing is an important means to check the integrity of the data stored on the cloud. Existing data auditing schemes have done a lot of work in terms of functionality, implementation, and security. As we can see, all of these schemes are based on the auditing framework proposed by Ateniese et al., which uses random sampling to probabilistically check whether the data is integrity or not. But multiple repeated sampling may lead to an intersection between the challenge sets selected each time, reducing the probability of detection, and the time for detecting corrupted blocks will be indefinite. If the corrupted data is not found for a long time, the data remedial measures will become invalid, resulting in permanent data loss. To address the above problem, we designed an efficient sampling verification algorithm. Based on this algorithm, the auditing scheme is further optimized, and a dynamic auditing function has been developed for the scheme to facilitate data owners to update data. Through the security analysis, our scheme is soundness. The experiment shows that our sampling verification can detect corrupted blocks faster than other auditing schemes.

Real-Time AWS Resource Surveillance with Reporting and Dashboard Solution

“…[An Efficient Data Auditing Protocol With a Novel Sampling Verification Algorithm] The paper [3] elucidates that existing data auditing schemes, following Ateniese etal. 's framework, face challenges like repeated sampling leading to detection delays and data loss risk.…”