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With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous networks, mobile broadband users are generating massive volumes of data that undergo fast processing and computing to obtain actionable insights. While analyzing this huge amount of data typically involves machine and deep learning-based data-driven Artificial Intelligence (AI) models, a key challenge arises in terms of providing privacy assurances for user-generated data. Even though data-driven techniques have been widely utilized for network traffic analysis and other network management tasks, researchers have also identified that applying AI techniques may often lead to severe privacy concerns. Therefore, the concept of privacy-preserving data-driven learning models has recently emerged as a hot area of research to facilitate model training on large-scale datasets while guaranteeing privacy along with the security of the data. In this paper, we first demonstrate the research gap in this domain, followed by a tutorial-oriented review of data-driven models, which can be potentially mapped to privacy-preserving techniques. Then, we provide preliminaries of a number of privacy-preserving techniques (e.g., differential privacy, functional encryption, Homomorphic encryption, secure multi-party computation, and federated learning) that can be potentially adopted for emerging communication networks. The provided preliminaries enable us to showcase the subset of data-driven privacy-preserving models, which are gaining traction in emerging communication network systems. We provide a number of relevant networking use cases, ranging from the B5G core and Radio Access Networks (RANs) to semantic communications, adopting privacy-preserving data-driven models. Based on the lessons learned from the pertinent use cases, we also identify several open research challenges and hint toward possible solutions. 

The advancement of 5G and NextG networks through Open Radio Access Network (O-RAN) architecture marks a transformative shift towards more virtualized, modular, and disaggregated configurations. A critical component within this O-RAN architecture is the RAN Intelligent Controller (RIC), which facilitates the management and control of the RAN through sophisticated machine learning-driven software microservices known as xApps. These xApps rely on accessing a diverse range of sensitive data from RAN and User Equipment (UE), stored in the near Real-Time RIC (Near-RT RIC) database. The inherent nature of this shared, multi-vendor, and open environment significantly raises the risk of unauthorized sensitive RAN/UE data exposure. In response to these privacy concerns, this paper proposes a privacy-preserving zero-trust RIC (dubbed as, ZT-RIC) framework that preserves RAN/UE data privacy within the RIC platform (i.e., shared RIC database, xApp, and E2 interface). The underlying idea is to employ a computationally efficient cryptographic technique called Inner Product Functional Encryption (IPFE) to encrypt the RAN/UE data at the base station, thus, preventing data leaks over the E2 interface and shared RIC database. Furthermore, ZT-RIC customizes the xApp’s inference model by leveraging the inner product operations on encrypted data supported by IPFE to enable xApp to make accurate inferences without data exposure. For evaluation purposes, we leverage a state-of-the-art InterClass xApp, which utilizes RAN key performance metrics (KPMs) to identify jamming signals within the wireless network. Prototyping on an LTE/5G O-RAN testbed demonstrates that ZT-RIC not only ensures data privacy/confidentiality but also guarantees a desired model accuracy, evidenced by a 97.9% accuracy in detecting jamming signals as well as meeting stringent sub-second timing requirement with a round-trip time (RTT) of 0.527