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DeepPAR and DeepDPA: Privacy Preserving and Asynchronous Deep Learning for Industrial IoT

机译：Deeppar和Deepdpa：工业物联网保护和异步深度学习

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摘要

Industrial Internet of Things (IIoT) is significant of building powerful industrial systems and applications. Deep learning has provided a promising opportunity to extract useful knowledge by utilizing vast amounts of data in IIoT. However, lacking of massive public datasets will lead to low performance and overfitting of the learned model. Therefore, the federated deep learning over distributed datasets has been proposed. Whereas, it inevitably introduces some new security challenges, i.e., disclosing participant's data privacy. However, existing methods cannot guarantee each participant's data privacy in a learning group. In this article, we propose two privacy-preserving asynchronous deep learning schemes [privacy-preserving and asynchronous deep learning via re-encryption (DeepPAR) and dynamic privacy-preserving and asynchronous deep learning (DeepDPA)]. Compared to the state-of-the-art work, DeepPAR protects each participant's input privacy while preserving dynamic update secrecy inherently. Meanwhile, DeepDPA enables to guarantee backward secrecy of group participants in a lightweight manner. Security analysis and performance evaluations on real dataset show that our proposed schemes are secure, efficient, and effective.
机译：工业互联网（IIT）是建立强大的工业系统和应用的重要性。深入学习提供了一个有希望的机会，通过利用IIOT中的大量数据来提取有用的知识。然而，缺乏大规模的公共数据集将导致学习模型的性能低和过度舒适。因此，提出了通过分布式数据集的联合深度学习。虽然，它不可避免地介绍了一些新的安全挑战，即，披露参与者的数据隐私。但是，现有方法不能保证每个参与者在学习组中的数据隐私。在本文中，我们提出了两个隐私保留的异步深度学习方案[通过重新加密（Deeppar）和动态隐私保留和异步深度学习（Deepdpa）]的隐私保留和异步深度学习。与最先进的工作相比，DeepPar保护每个参与者的输入隐私，同时保留固有的动态更新保密。与此同时，Deepdpa能够以轻量级方式保证集团参与者的落后保密。 Real DataSet上的安全分析和绩效评估表明，我们的提出方案是安全，高效和有效的。

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