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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Secure Provenance Transmission for Streaming Data
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Secure Provenance Transmission for Streaming Data

机译:流媒体数据的安全来源传输

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摘要

Many application domains, such as real-time financial analysis, e-healthcare systems, sensor networks, are characterized by continuous data streaming from multiple sources and through intermediate processing by multiple aggregators. Keeping track of data provenance in such highly dynamic context is an important requirement, since data provenance is a key factor in assessing data trustworthiness which is crucial for many applications. Provenance management for streaming data requires addressing several challenges, including the assurance of high processing throughput, low bandwidth consumption, storage efficiency and secure transmission. In this paper, we propose a novel approach to securely transmit provenance for streaming data (focusing on sensor network) by embedding provenance into the interpacket timing domain while addressing the above mentioned issues. As provenance is hidden in another host-medium, our solution can be conceptualized as watermarking technique. However, unlike traditional watermarking approaches, we embed provenance over the interpacket delays (IPDs) rather than in the sensor data themselves, hence avoiding the problem of data degradation due to watermarking. Provenance is extracted by the data receiver utilizing an optimal threshold-based mechanism which minimizes the probability of provenance decoding errors. The resiliency of the scheme against outside and inside attackers is established through an extensive security analysis. Experiments show that our technique can recover provenance up to a certain level against perturbations to inter-packet timing characteristics.
机译:许多应用领域(例如实时财务分析,电子医疗系统,传感器网络)的特征是,来自多个源的连续数据流以及经过多个聚合器的中间处理。在如此高度动态的环境中跟踪数据来源是一个重要的要求,因为数据来源是评估数据可信赖性的关键因素,这对于许多应用程序而言至关重要。流数据的源管理需要解决几个挑战,包括确保高处理吞吐量,低带宽消耗,存储效率和安全传输。在本文中,我们提出了一种新颖的方法,通过将来源嵌入到分组间定时域中来解决上述问题,从而安全地传输流数据的来源(关注传感器网络)。由于来源隐藏在另一个宿主介质中,因此我们的解决方案可以概念化为水印技术。但是,与传统的水印方法不同,我们在包间延迟(IPD)上而不是在传感器数据本身中嵌入源,从而避免了由于水印引起的数据降级的问题。数据接收器利用基于阈值的最佳机制提取出源,该机制将源解码错误的可能性降到最低。通过广泛的安全分析,可以确定该方案针对外部和内部攻击者的弹性。实验表明,我们的技术可以将源恢复到一定水平,以防止干扰分组间定时特性。

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