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Research and application of virtual user context information security strategy based on group intelligent computing

机译:基于群体智能计算的虚拟用户上下文信息安全策略的研究与应用

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This article first introduced the current technology of the privacy protection model, and analyzed their characteristics and deficiencies. Afterwards, from the point of view of revenue, the shortcomings of the traditional privacy protection model have analyzed through the group intelligent computing method. Based on this, this paper proposes a research and application of virtual user information of security strategy based on group intelligent computing, through the collection of visitor's private information historical access data, intelligent calculation of the strategy group between the visitor and the interviewee. The setting of the threshold of the visited person can protect the privacy information of the user more effectively. In this paper, the implementation flow, algorithm implementation process, and specific architecture design of the proposed virtual user of privacy protection model based on group intelligent computing are introduced respectively. The specific algorithms include PCA, BP neural network, and genetic algorithm. Finally, the proposed privacy has verified through experiments. Protection model can protect user privacy more effectively than traditional privacy protection model. In the future, we will further expand and improve the privacy protection model of virtual users based on group intelligent computing, including considering the dynamic and inconsistency of access to the privacy information, that is, accessing different private information will produce different overlay effects and parallelism. We will also study how to apply this model to actual systems such as shopping websites and social platforms, and use commercial data to evaluate the performance of the model and further improve it. (C) 2018 Elsevier B.V. All rights reserved.
机译:本文首先介绍了隐私保护模型的当前技术,并分析了它们的特点和不足。然后,从收益的角度出发,通过群智能计算方法分析了传统隐私保护模型的不足。在此基础上,通过收集访问者的私人信息历史访问数据,对访问者与被访问者之间的策略组进行智能计算,提出了一种基于群体智能计算的安全策略虚拟用户信息的研究与应用。受访者阈值的设置可以更有效地保护用户的隐私信息。本文分别介绍了基于组智能计算的虚拟隐私保护模型虚拟用户的实现流程,算法实现过程以及具体的体系结构设计。具体算法包括PCA,BP神经网络和遗传算法。最后,通过实验验证了所提出的隐私。与传统的隐私保护模型相比,保护模型可以更有效地保护用户隐私。未来,我们将基于组智能计算,进一步扩展和完善虚拟用户的隐私保护模型,包括考虑访问隐私信息的动态性和不一致性,即访问不同的私人信息会产生不同的覆盖效果和并行性。 。我们还将研究如何将此模型应用于购物网站和社交平台等实际系统,并使用商业数据评估该模型的性能并进一步加以改进。 (C)2018 Elsevier B.V.保留所有权利。

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