5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing systems since the introduction of 5G, but also includes a range of autonomous moving platforms, e.g., autonomous flying vehicles, autonomous underwater vehicles, autonomous surface vehicles as well as autonomous land vehicles. These platforms can be used as an effective means to connect air, space, ground, and sea mobile networks for providing a wider diversity of Internet services. Deep learning has been widely used to extract useful information from network big data for enhancing network quality-of-service and user quality-of-experience. Privacy preservation for user and network data is a burning concern in 5G heterogeneous networks due to various attacks in this environment. In this paper, we conduct an in-depth investigation on how deep learning can cope with privacy preservation issues in 5G heterogeneous networks, in terms of heterogeneous radio access networks (RANs), beyond-RAN networks, and end-to-end network slices, followed by a set of key research challenges and open issues that aim to guide future research.

5G异构网络已成为连接越来越多的物联网（IoT）设备并容纳各种垂直服务的有前途的平台。自5G推出以来，IoT不仅限于传统的传感系统，还包括一系列自主移动平台，例如，自主飞行器，自主水下器，自主水面车以及自主陆地车。这些平台可以用作连接空中，太空，地面和海上移动网络的有效手段，以提供更广泛的Internet服务。深度学习已被广​​泛用于从网络大数据中提取有用的信息，以增强网络服务质量和用户体验质量。由于此环境中的各种攻击，在5G异构网络中，保护用户和网络数据的隐私是一个迫切需要解决的问题。在本文中，我们对深度学习如何应对5G异构网络中的隐私保护问题进行了深入研究，涉及异构无线电接入网络（RAN），超出RAN网络和端到端网络切片，其后是一系列旨在指导未来研究的关键研究挑战和未解决问题。