云存储中数据完整性的聚合盲审计方法

Aggregated privacy-preserving auditing for cloud data integrity

针对云存储中数据完整性问题,提出了一种聚合盲审计方法。利用双线性对映射的性质,在云服务器端将数据证据和标签证据加密后再合并,实现审计者在不知数据内容的情况下进行盲审计。在此基础上,设计高效的索引机制支持数据更新,使数据更新操作不会导致大量额外的计算和通信开销,实现了动态审计。同时,针对多个审计请求,设计将不同的证据聚合的方法,以支持对多所有者多云服务器多文件的批量审计,使批量审计的通信开销与审计请求的数量无关。理论分析和实验结果表明,该方法是可证明安全的,与现有的其他审计方案相比,所提的单审计和批量审计的效率分别提高了21.5%和31.8%。

To solve the problem of data integrity in cloud storage, an aggregated privacy-preserving auditing scheme was proposed. To preserve data privacy against the auditor, data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server. Furthermore, an efficient index mechanism was designed to support dynamic auditing, which could ensure that data update operations did not lead to high additional computation or communication cost. Meanwhile, an aggregation method for different proofs was designed to handle multiple auditing requests. Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files. The communication cost of batch auditing was independent of the number of auditing requests. The theoretical analysis and experimental results show that the proposed scheme is provably secure. Compared with existing auditing scheme, the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.