簇态量子模糊哈希与隐蔽信息搜索(英文)

Cluster states quantum fuzzy hashing and covert information search

提出一种采用经典-量子ε-universal哈希类的簇态量子模糊哈希构造方法.传统哈希与模糊哈希算法不能有效抵抗量子攻击.通过采用diamond范数方法,构建了一种哈希函数类最优子集并且提供信息论意义上的更优安全性.基于量子簇态独特的物理级单向计算属性,相应算法更接近于物理可实现.进一步,构造了一种在信息安全与生物特征识别方面的隐蔽信息搜索策略.该生物识别搜索算法基于簇态量子ε-universal模糊哈希构建.该策略能有效抵抗量子算法攻击,确保数据存储安全,并降低了计算复杂度.相比于其他类似策略,此算法具有更精简的结构,理论分析表明此算法具有较高的识别效率与更好的数据安全性.

A novel construction method of cluster states quantum fuzzy hash utilizing classical-quantum ε-universal hashing family was proposed. Traditional hash and fuzzy hash algorithm are not effective against quantum attacks. Utilizing the method of diamond norm, an optimal subset of hash functions family was constructed and better security on the sense of information theory was guaranteed. Based on the unique physical one-way computation properties of quantum cluster states, the corresponding algorithm is more close to the physical realization. And then, a novel scheme of covert information search in the field of information security and biometrics was presented. The biometrics search algorithm is constructed from the cluster states quantum ε-universal fuzzy hashing. The algorithm can effectively resist the quantum algorithm attack, ensure data storage security and reduce the complexity. Compared to others similar schemes, the algorithm has a more simplified structure. Theoretical analysis shows that the algorithm has higher recognition efficiency and better data security.