多源海量隐私大数据可靠性访问权限安全认证  

Multi-source Massive Privacy Big Data Reliability Access Right Security Authentication

作　　者：曹敏 曹东朗 CAO Min;CAO Dong-lang(Department of Network Security,Shanxi Police College,Taiyuan Shanxi 030400,China;Shanxi University,Taiyuan Shanxi 030400,China)

机构地区：[1]山西警察学院网络安全保卫系,山西太原030400 [2]山西大学,山西太原030400

出　　处：《计算机仿真》2024年第5期395-399,共5页Computer Simulation

基　　金：2022年党的十九届六中全会及省第十二次党代会专项课题(2022YD165)。

摘　　要：多源数据的复杂性以及大数据传播速度快等特点,会导致隐私数据易被不正当的攻击手段窃取,且安全认证难度较大,从而导致大数据安全性降低。为解决上述问题,提出一种多源海量隐私大数据可靠性访问权限安全认证方法。采用区块链技术检测海量多源隐私数据中的恶意节点,考虑到恶意节点在多源网络中受较多不确定因素影响,为提升节点检测信用度,设置影响因素判定范围用作恶意节点判断标准。结合指纹魔方算法针对检测后的多源隐私大数据设定安全机制,确保多源隐私大数据的安全性。利用访问控制协议方法认证用户身份的合法性及数据的可靠性访问,实现隐私大数据可靠性访问权限安全认证。实验结果表明,所提方法在安全性测试中可以有效抵抗全部攻击,且认证时间开销低于4.7ms,验证了上述方法的可靠性强、实用性高。The complexity of multi-source data and the fast propagation of big data may lead to privacy data being easily stolen by improper attacks,even a reduction of big data security.In this paper,a security authentication method for reliable access rights of multi-source massive private big data was proposed.Firstly,blockchain technology was used to detect malicious nodes in massive multi-source private data.Considering that malicious nodes were affected by many uncertain factors in multi-source networks,the determination range of influencing factors was set as the judgment standard for malicious nodes in order to improve the credibility of node detection.Combined with the fingerprint magic cube algorithm,a security mechanism was established for the detected multi-source private big data,thus ensuring the security of data.Moreover,the access control protocol was used to authenticate the user identity and the reliable access of data.Finally,the security authentication of reliable access rights for private big data was achieved.Experimental results show that the proposed method can effectively resist all attacks in the security test,and only needs less than 4.7ms for the authentication.Therefore,the method is reliable and practical.

关 键 词：隐私大数据 访问权限安全认证 区块链技术 安全机制 恶意节点检测 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]