Fog-cloud computing promises many new vertical service areas beyond simple data communication, storing, and processing. Among them, distributed deep learning (DDL) across fog-cloud computing environment is one of the most popular applications due to its high efficiency and scalability. Compared with the centralized deep learning, DDL can provide better privacy protection with training only on sharing parameters. Nevertheless, when DDL meets fog-cloud computing, it still faces two major security challenges: 1) how to protect users’ privacy from being leaked to other internal participants in the training process and 2) how to guarantee users’ identities from being forged by external adversaries. To combat them, several approaches have been proposed via various technologies. Nevertheless, those approaches suffer from drawbacks in terms of security, efficiency, and functionality, and cannot guarantee the legitimacy of participants’ identities during training. In this article, we propose a secure and privacy-preserving DDL (SPDDL) for fog-cloud computing. Compared with the state-of-the-art works, our proposal achieves a better tradeoff between security, efficiency, and functionality. In addition, our SPDDL can guarantee the unforgeability of users’ identities against external adversaries. Extensive experimental results indicate the practical feasibility and high efficiency of our SPDDL.

中文翻译：

---

在雾云计算中实现安全和隐私保护的分布式深度学习

除简单的数据通信，存储和处理外，雾云计算还承诺了许多新的垂直服务领域。其中，跨雾云计算环境的分布式深度学习（DDL）由于其高效和可扩展性而成为最受欢迎的应用程序之一。与集中式深度学习相比，DDL仅通过共享参数方面的培训就可以提供更好的隐私保护。然而，当DDL遇到雾云计算时，它仍然面临两个主要的安全挑战：1）如何在培训过程中保护用户的隐私不泄露给其他内部参与者，以及2）如何确保用户的身份不被伪造外部对手。为了对抗它们，已经通过各种技术提出了几种方法。但是，这些方法在安全性方面存在弊端，效率和功能，并且不能保证培训期间参与者身份的合法性。在本文中，我们提出了一种用于雾云计算的安全且保留隐私的DDL（SPDDL）。与最新技术相比，我们的建议在安全性，效率和功能之间实现了更好的权衡。此外，我们的SPDDL可以保证用户身份对于外部对手的不可伪造性。大量的实验结果表明我们SPDDL的实际可行性和高效率。效率和功能。此外，我们的SPDDL可以保证用户身份对于外部对手的不可伪造性。大量的实验结果表明我们SPDDL的实际可行性和高效率。效率和功能。此外，我们的SPDDL可以保证用户身份对于外部对手的不可伪造性。大量的实验结果表明我们SPDDL的实际可行性和高效率。