基于混合密码体制的大数据隐匿性特征安全提取技术
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1.中国科协信息中心;2.山东科技大学

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TP393???? ??????????

基金项目:

山东省重点研发计划重大科技创新工程《面向石油行业的安全可信云与动态态势感知系统建设及应用示范》(编号:2020SO10103-00517)


Security extraction technology of big data hiding features based on hybrid cryptosystem
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1.Information Center of China Association for Science and Technology;2.Shandong University of Science and Technology

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    摘要:

    传统大数据隐匿性特征安全提取技术忽略了大数据密文的公钥及密钥封装,且大数据隐匿性特征类别混乱,导致该技术的提取精度偏低,冗余度较高。为此,提出基于混合密码体制的大数据隐匿性特征安全提取方法。通过混合密码体制中的公钥封装以及密钥封装机制生成大数据密文。根据密文内容设计对称加密方法和非对称加密方法,基于此分类隐匿性特征。利用不同类的隐匿性特征构建大数据隐秘性特征相空间,计算大数据间的关联维值,实现大数据隐匿特征的安全提取。实验结果表明,与传统方法相比,所提出的大数据隐匿特征提取方法冗余度低,大数据隐匿特征平均正确分类率高达95%,且特征安全提取误差低,验证了所提方法具有更好的应用性能。

    Abstract:

    The traditional security extraction technology of big data concealment features ignores the public key and key encapsulation of big data ciphertext, and the category of big data concealment features is chaotic, leading to the low extraction accuracy and high redundancy of this technology. Therefore, this paper proposes a security extraction method of big data concealment features based on mixed cipher system. Big data ciphertext is generated by public key encapsulation and key encapsulation mechanism in mixed cryptography. Symmetric encryption method and asymmetric encryption method are designed according to the ciphertext content, based on the hidden characteristics of the classification. The hidden feature phase space of big data is constructed by using the hidden features of different classes, and the correlation dimension value among big data is calculated to realize the safe extraction of the hidden features of big data. The experimental results show that, compared with the traditional methods, the proposed big data hidden feature extraction method has low redundancy, the average correct classification rate of big data hidden features is up to 95%, and the error of feature safe extraction is low, which verifies that the proposed method has better application performance.

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  • 收稿日期:2022-03-03
  • 最后修改日期:2022-04-26
  • 录用日期:2022-06-08
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