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云数据存储服务为用户提供了一种新型的数据服务模式.但数据所有者因失去对数据的直接控制,面临对不可信的云服务提供者的数据存储担忧.目前普遍采用基于同态技术的数据完整性校验算法.但这种算法在面对大数据存储中的动态数据时,出现计算开销大和校验效率受数据分块大小影响较大等问题.提出一种基于Counting Bloom Filter(CBF)的数据完整性校验算法,采用CBF作为校验元存储结构和相关的哈希运算实现动态数据的完整性校验.理论分析和模拟测试结果表明,算法在满足动态性数据校验要求的前提下,降低校验时的校验数据传输开销,减少了校验的计算时间,降低了数据分块大小的影响,极大的提高了校验能力.
Cloud data storage services provide users with a new data service model, but data owners face data storage concerns about untrusted cloud service providers due to the loss of direct control of data. Currently, the homomorphic Data integrity checking algorithm.However, this algorithm has some problems such as large computational cost and large check-up efficiency which are greatly affected by the size of data block when faced with the dynamic data in big data storage.A new algorithm based on Counting Bloom Filter (CBF ) Data integrity checking algorithm, using CBF as a check-point storage structure and related hash operations to verify the integrity of dynamic data.The theoretical analysis and simulation results show that the algorithm can meet the dynamic data validation requirements Under the premise of reducing the parity check data transmission overhead, reducing the calculation time of verification, reducing the size of the data block, greatly improving the verification capability.