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Achieving secure big data collection based on trust evaluation and true data discovery
Computers & Security ( IF 5.6 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.cose.2020.101937
Denglong Lv , Shibing Zhu

Abstract Data collection is an important process in the life cycle of big data processing. It is the key part that must be completed first in all kinds of data applications, which determines the results of data analysis and application service quality. However, untrusted data sources and transmission links expose the data collection process to attacks and malicious threats such as counterfeiting, replay, and denial of service, and ultimately lead to untrustworthy data. In order to cope with the threat of data collection process and ensure data quality, this paper proposes trust evaluation scheme for data security collection based on wireless sensor network, one of the data collection applications, including direct trust, recommendation trust, link trust, and backhaul trust. Meanwhile, in order to realize the dynamic update of the trust of the data sources, a true data discovery and trust dynamic update mechanism based on ω-FCM (Weight Fuzzy C-Mean) algorithm is proposed. The results of a large number of simulation experiments show that the proposed scheme, model and algorithm can effectively evaluate the trust of data sources and ensure the authenticity of the collected data.

中文翻译:

基于信任评估和真实数据发现,实现安全的大数据采集

摘要 数据采集是大数据处理生命周期中的一个重要过程。它是各类数据应用中必须首先完成的关键部分,它决定了数据分析的结果和应用服务质量。但是,不可信的数据源和传输链路使数据采集过程暴露在伪造、重放、拒绝服务等攻击和恶意威胁之下,最终导致数据不可信。为了应对数据采集过程中的威胁,保证数据质量,本文提出了基于无线传感器网络的数据安全采集信任评估方案,数据采集应用之一,包括直接信任、推荐信任、链路信任和回程信任。同时,为了实现数据源信任的动态更新,提出了一种基于ω-FCM(Weight Fuzzy C-Mean)算法的真实数据发现和信任动态更新机制。大量仿真实验结果表明,所提出的方案、模型和算法能够有效评估数据源的可信度,保证采集数据的真实性。
更新日期:2020-09-01
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