With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks, from IoT devices and edge computing to cloud-based infrastructures. However, most of the existing surveys in B5G security focus on the performance of AI/ML models and their accuracy, but they often overlook the accountability and trustworthiness of the models' decisions. Explainable AI (XAI) methods are promising techniques that would allow system developers to identify the internal workings of AI/ML black-box models. The goal of using XAI in the security domain of B5G is to allow the decision-making processes of the security of systems to be transparent and comprehensible to stakeholders making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as RAN, zero-touch network management, E2E slicing, this survey emphasizes the role of XAI in them and the use cases that the general users would ultimately enjoy. Furthermore, we presented the lessons learned from recent efforts and future research directions on top of the currently conducted projects involving XAI.
Massive progress of mobile wireless telecommunication networks was achieved in the previous decades, with privacy enhancement in each. At present, mobile users are getting familiar with the latest 5G networks, and the discussion for the next generation of Beyond 5G (B5G)/6G networks has already been initiated. It is expected that B5G/6G will push the existing network capabilities to the next level, with higher speeds, enhanced reliability, and seamless connectivity. To make these expectations a reality, research is progressing on new technologies, architectures, and intelligence-based decision-making processes related to B5G/6G. Privacy considerations are a crucial aspect that needs further attention in such developments, as billions of people and devices will be transmitting their data through the upcoming network. This paper provides a comprehensive survey on privacy-related aspects for B5G/6G networks. First, it discusses a taxonomy of different privacy perspectives. Based on the taxonomy, the paper then conceptualizes a set of privacy goals for the B5G/6G and the challenges that appear as barriers to reaching these goals. Next, this work provides a set of solutions applicable to the proposed architecture of B5G/6G networks to mitigate the challenges. Additionally, this paper discusses the emerging field of non-personal data privacy. It also provides an overview of standardization initiatives for privacy preservation. Finally, it concludes with a roadmap of future directions and upcoming trends containing privacy-related topics, which will be an arena for new research towards privacy-enhanced B5G/6G networks. This work provides a basis for privacy aspects that will significantly impact peoples' daily lives with future networks.
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