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首页> 外文期刊>Future generation computer systems >Lightweight, Divide-and-Conquer privacy-preserving data aggregation in fog computing
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Lightweight, Divide-and-Conquer privacy-preserving data aggregation in fog computing

机译：轻量级，灭薄，征服隐私保留数据聚集

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摘要

With the increasing popularity of the Internet of Things' (IoT) and fog computing paradigm, aggregating IoT data considering privacy concerns over fog networks can be seen as one of the biggest security challenges. Numerous schemes address this problem. However, most of the existing schemes and their associated methods are heavyweight, facing issues related to performance overhead. Furthermore, performing data aggregation at a single aggregator fog node causes an overly computational burden on the node, which results in high latency, degraded reliability and scalability leading to a single point of failure risks. To fill these gaps, this paper presents a lightweight, Divide-and-Conquer privacy-preserving data aggregation scheme in fog computing to improve data privacy, data processing, and storage capabilities. Particularly, we design a data division strategy based on the Level of Privacy (LoP) defined by data owners. The data division strategy not only effectively divides data according to LoP and distributes it among participating fog nodes for aggregation and storage processing, but also reduces computational and memory overhead in the processing simultaneously. Moreover, we perform a privacy analysis of our scheme and perform comprehensive experiments to compare it with other traditional schemes to evaluate performance efficiency. The results demonstrate that our scheme can efficiently achieve data privacy in fog computing and outperforms the other schemes in computational and memory costs.
机译：随着事物互联网的普及（物联网）和雾计算范式，考虑到雾网络的隐私问题，聚合IOT数据可以被视为最大的安全挑战之一。众多计划解决了这个问题。但是，大多数现有方案及其相关方法都是重量级，面临与性能开销相关的问题。此外，在单个聚合器雾节点上执行数据聚合会导致节点上的过度计算负担，这导致高延迟，降级的可靠性和可伸缩性，导致单点故障风险。为了填补这些差距，本文介绍了雾计算中的轻量级，鸿沟和征服隐私保留数据聚合方案，以改善数据隐私，数据处理和存储功能。特别是，我们根据数据所有者定义的隐私级别（循点）设计数据划分策略。数据划分策略不仅有效地将数据划分为循环并在参与的雾节点之间分配聚合和存储处理，但也可以在同时处理处理中减少计算和存储器开销。此外，我们对我们的计划进行了隐私分析，并进行了全面的实验，以将其与其他传统方案进行比较以评估性能效率。结果表明，我们的计划可以有效地实现雾计算中的数据隐私，并且在计算和内存成本中优于其他方案。

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  • 来源
    《Future generation computer systems》 |2021年第6期|188-199|共12页
  • 作者

    Kinza Sarwar; Sira Yongchareon; Jian Yu; Saeed ur Rehman;

  • 作者单位

    School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand;

    School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand;

    School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand;

    College of Science and Engineering Flinders University Adelaide Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data aggregation; Fog computing; IoT; Privacy;

    机译：数据聚合;雾计算;IOT;隐私;

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