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A Synchronized Shared Key Generation Method for Maintaining End-to-End Security of Big Data Streams

机译:一种维护大数据流端到端安全的同步共享密钥生成方法

摘要

A large number of mission critical applications ranging from disaster management to smart city are built on the Internet of Things (IoT) platform by deploying a number of smart sensors in a heterogeneous environment. The key requirements of such applications are the need of near real-time stream data processing in large scale sensing networks. This trend gives birth of an area called big data stream. One of the key problems in big data stream is to ensure the end-to-end security. To address this challenge, we proposed Dynamic Prime Number Based Security Verification (DPBSV) and Dynamic Key Length Based Security Framework (DLSeF) methods for big data streams based on the shared key derived from synchronized prime numbers in our earlier works. One of the major shortcomings of these methods is that they assume synchronization of the shared key. However, the assumption does not hold when the communication between Data Stream Manager (DSM) and sensing devices is broken. To address this problem, this paper proposes an adaptive technique to synchronize the shared key without communication between sensing devices and DSM, where sensing devices obtain the shared key re-initialization properties from its neighbours. Theoretical analyses and experimental results show that the proposed technique can be integrated with our DPBSV and DLSeF methods without degrading the performance and efficiency. We observed that the proposed synchronization method also strengthens the security of the models.
机译:通过在异构环境中部署大量智能传感器,在物联网(IoT)平台上构建了从灾难管理到智能城市的大量关键任务应用程序。这种应用的关键要求是在大规模传感网络中需要近实时的流数据处理。这种趋势催生了一个称为大数据流的区域。大数据流中的关键问题之一是确保端到端的安全性。为了解决这一挑战,我们在早期工作中基于从同步素数得出的共享密钥,针对大数据流提出了基于动态素数的安全验证(DPBSV)和基于动态密钥长度的安全框架(DLSeF)方法。这些方法的主要缺点之一是它们假定共享密钥是同步的。但是,当数据流管理器(DSM)与传感设备之间的通信中断时,该假设将不成立。为了解决这个问题,本文提出了一种自适应技术来同步共享密钥,而无需在传感设备和DSM之间进行通信,传感设备从其邻居那里获取共享密钥的重新初始化属性。理论分析和实验结果表明,所提出的技术可以与我们的DPBSV和DLSeF方法集成在一起,而不会降低性能和效率。我们观察到,所提出的同步方法还增强了模型的安全性。

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