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首页> 外文期刊>IEEE transactions on industrial informatics >Federated Tensor Mining for Secure Industrial Internet of Things
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Federated Tensor Mining for Secure Industrial Internet of Things

机译：联邦张力矿业以保护工业互联网

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摘要

In a vertical industry alliance, Internet of Things (IoT) deployed in different smart factories are similar. For example, most automobile manufacturers have the similar assembly lines and IoT surveillance systems. It is common to observe the industrial knowledge using deep learning and data mining methods based on the IoT data. However, some knowledge is not easy to be mined from only one factory's data because the samples are still few. If multiple factories within an alliance can gather their data together, more knowledge could be mined. However, the key concern of these factories is the data security. Existing matrix-based methods can guarantee the data security inside a factory but do not allow the data sharing among factories, and thus their mining performance is poor due to lack of correlation. To address this concern, in this article we propose the novel federated tensor mining (FTM) framework to federate multisource data together for tensor-based mining while guaranteeing the security. The key contribution of FTM is that every factory only needs to share its ciphertext data for security issue, and these ciphertexts are adequate for tensor-based knowledge mining due to its homomorphic attribution. Real-data-driven simulations demonstrate that FTM not only mines the same knowledge compared with the plaintext mining, but also is enabled to defend the attacks from distributed eavesdroppers and centralized hackers. In our typical experiment, compared with the matrix-based privacy-preserving compressive sensing (PPCS), FTM increases up to 24% on mining accuracy.
机译：在垂直行业联盟中，在不同智能工厂部署的东西（物联网）是相似的。例如，大多数汽车制造商都有类似的装配线和物联网监控系统。使用基于IOT数据的深度学习和数据挖掘方法，遵守工业知识。然而，一些知识并不容易从一个工厂的数据中开采，因为样品仍然很少。如果联盟中的多个工厂可以将其数据收集在一起，可以开采更多的知识。但是，这些工厂的关键问题是数据安全。现有的基于矩阵的方法可以保证工厂内的数据安全性，但不允许在工厂之间共享数据，因此由于缺乏相关性，它们的采矿性能较差。为了解决这一问题，在本文中，我们向联邦多源数据提出了新的联合张解人员（FTM）框架，以便在保证安全的情况下加上张富集的挖掘。 FTM的关键贡献是每个工厂只需要共享其安全问题的密文数据，并且由于其同性恋归因，这些密文是足够的张量的知识挖掘。实际数据驱动的模拟表明，与明文挖掘相比，FTM不仅可以挖掘与相同的知识，还可以启用从分布式窃听者和集中式黑客的攻击。在我们的典型实验中，与基于基于基于基于基于基于矩阵的隐私保留的压缩感测（PPC）相比，FTM在采矿精度上增加了高达24％。

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