基于改进索引散列树的动态大数据审核方法

为了解决公共审核和数据动态性问题,以及降低计算成本,提出一种改进的索引散列树的数据审核方法。该方法在经典Merkle散列树MHT(Merkle Hash Tree)和BLS签名的基础上,提出一种改进的MHT。增加节点字段,修改MHT树的节点信息,使每个节点信息均包含存储数据块的哈希值和相对索引。通过时间戳字段与MHT的根节点相关联,以提供数据新鲜性。数据分析表明,该方法的不当操作检测概率较高,具有较好的安全性。与其他方法相比,该方法的计算成本较低,服务器和审核者的总体时间复杂度为O(n),验证了对MHT的改进行之有效。

DYNAMIC BIG DATA AUDIT METHOD BASED ON IMPROVED INDEX MERKLE HASH TREE

To solve the problem of public audit and data dynamics,and to reduce the computational cost,we proposed a data audit method based on improved index Merkle hash tree.We put forward an improved MHT on the basis of the classical Merkle hash tree(MHT)and the BLS signature.We added the node field,and modified the node information of MHT tree so that each node information contained the hash value and relative index of the stored data block.The timestamp field was associated with the root node of MHT to provide data freshness.Data analysis shows that the proposed method has higher detection probability and better security.Compared with other methods,the proposed method has lower computational cost,and the overall time complexity of server and reviewer is O(n),which verifies the effectiveness of the MHT improvement.