基于自适应Merkle哈希树的动态数据流审计方案
Dynamic data stream auditing scheme based on adaptive Merkle hash tree
杜建明 1董国芳1
作者信息
- 1. 云南民族大学电气信息工程学院,云南昆明 650504
- 折叠
摘要
针对现有数据结构不能很好支持动态大数据流及传统Merkle哈希树验证路径过长及单点故障问题,提出一种基于自适应Merkle哈希树的动态数据流审计方案.使用陷门哈希函数构建一个新的自适应Merkle哈希树数据认证结构存储动态数据流,自适应扩展树的深度.引入局部权威根节点,解决验证路径过长和单点故障问题.采用BLS签名技术和随机掩蔽技术,保证数据完整性验证过程的隐私保护.安全性分析证明了所提方案是可证明安全的,实验结果表明,所提方案能够高效实现云端数据完整性验证,确定了最佳叶子节点存储区间.
Abstract
Aiming at the problems that the existing data structure can not support the dynamic big data stream better,the tradi-tional Merkle hash tree verification path is too long and the single point is easy to fail.A new adaptive Merkle hash tree data authentication structure was constructed using trapdoor hash function to store dynamic data streams,which adaptively extended the depth of the tree.The local authoritative root node was introduced into the data authentication structure,which effectively solved the problems of too long authentication path and single point of failure.BLS signature technology and random masking technology were used to ensure the privacy protection of data integrity verification process.The security analysis proves that the proposed scheme is provable and secure,and the experimental results show that the proposed scheme can effectively realize the cloud data integrity verification,and determine the optimal storage interval of leaf nodes.
关键词
云存储/陷门哈希函数/动态数据流/BLS签名/随机掩蔽技术/隐私保护/动态更新Key words
cloud storage/trapdoor hash function/dynamic data stream/BLS signature/random masking technology/privacy preserving/dynamic update引用本文复制引用
出版年
2024
NETL
NSTL
万方数据