Data owners’ outsourced data on cloud data storage servers by the deduplication technique can reduce not only their own storage cost but also cloud’s. This paradigm also introduces new security issues such as the potential threat of data lost or corrupted. Data integrity verification is utilized to safeguard these data integrity. However, the cloud deduplication storage only focuses on file/chunk level to store one copy of the same data hosted by different data owners, and is not concerned with the same part of different data, e.g., a series of version files. We propose an integrity verification algorithm of different version files. The algorithm establishes the generic storage model of different version control methods to improve the universality of data verification. Then, the methods of verification tags and proofs generating are improved based on the index pointers corresponding to the storage relationship in the version groups and chained keys. Finally, the random diffusion extraction based on the random data sampling in the version group is proposed to improve the verification efficiency. The results of theoretical and experimental analysis indicate that the algorithm can achieve fast and large-scale verification for different version data.

中文翻译：

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用于云重复数据删除数据存储的版本文件的通用完整性验证算法

通过重复数据删除技术，数据所有者将数据外包到云数据存储服务器上不仅可以降低其自身的存储成本，还可以降低云的成本。此范例还引入了新的安全问题，例如数据丢失或损坏的潜在威胁。数据完整性验证用于保护这些数据完整性。但是，云重复数据删除存储仅专注于文件/块级别，以存储由不同数据所有者托管的同一数据的一个副本，而与不同数据的相同部分（例如一系列版本文件）无关。我们提出了不同版本文件的完整性验证算法。该算法建立了不同版本控制方法的通用存储模型，以提高数据验证的通用性。然后，基于版本组和链接密钥中的存储关系所对应的索引指针，改进了验证标签和证据生成的方法。最后，提出了基于版本组中随机数据采样的随机扩散提取，以提高验证效率。理论和实验分析结果表明，该算法可以对不同版本的数据进行快速，大规模的验证。