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Popularity-based multiple-replica cloud storage integrity auditing for big data
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AI
DOI：10.1016/j.future.2024.107534.png)
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Abstract
En 中文
Multiple-replica cloud storage is employed to store multiple replicas of the user's data in different cloud servers, which remarkably enhances the data availability. To ensure data replicas are correctly stored in cloud servers, multiple-replica cloud storage integrity auditing is proposed. Nevertheless, storing multiple replicas in cloud is not always necessary for all data, which reduces the storage efficiency for big data. In this paper, we consider a new problem of how to make a tradeoff between data availability and storage efficiency for cloud storage integrity auditing. When a file changes from an unpopular file to a popular one, this file will not be viewed as an important file any more. If we continue to store multiple replicas of the file for good availability, it will waste a lot of storage resources in big data scenario. Therefore, the tradeoff between data availability and storage efficiency is a significant issue. We propose a novel scheme called popularity-based multiple- replica cloud storage integrity auditing scheme. We introduce popularity into the multiple-replica cloud storage integrity auditing scheme to intelligently depict the data importance. For unpopular cloud data (important data), we adopt multiple-replica cloud storage technique to store them. In contrast, we only store a single replica for popular cloud data (unimportant data). Our proposed scheme can smoothly perform the auditing task for both unpopular cloud data and popular cloud data. As a result, it makes a nice balance between data availability and storage efficiency of cloud storage integrity auditing for big data. Furthermore, we discuss how to support the possible changes in data popularity after dynamic operations. We prove the security and make performance analysis for the proposed scheme.
Keywords:
Cloud computing
Big data
Cloud storage security
Integrity auditing
Data popularity
Journal
F
IF:
6.1
Papers:
6.8K
Citations:
2.3W