Nelufule, NthatheniSiphambili, NokuthabaShadung, Lesiba D2026-06-292026-06-292026-05http://hdl.handle.net/10204/14828The disaggregated, multi-vendor architecture of Open Radio Access Networks (O-RAN) in 6G, promises an unprecedented flexibility through the AI-native intelligence, and cost efficiency. However, these benefits also introduce challenges such as severe privacy and security risks which include the model inversion, data poisoning, and unauthorized access across distributed edge nodes; mainly Open Radio Unit (O-RU), Open Distributed Unit (O-DU), Open Centralized Unit (O-CU), and RAN Intelligent Controllers (RICs). In this paper, a systematic review based on the PRISMA framework was used to synthesize 42 peer-reviewed articles published between 2020 and 2026, particularly on the privacy-preserving AI techniques, Federated Learning (FL), Differential Privacy (DP), Secure Multi-Party Computation (SMPC), and emerging hybrid technologies applied to 6G O-RAN environments. The key research findings revealed that the combination of the Zero trust Architecture (ZTA) and FL can achieve up to 32% energy savings and Near-RT compliance, while the combination of DP and FL helps to secure the RIC and FBMP, and the Intrusion Detection System (IDS) helps to enable lightweight Multi-Party Computation (MPC). The notion of introducing a three-way FL, DP and SMPC integration for O-RAN remains unexplored, and this work bridges this gap, by introducing Privacy-Preserving (PrivSev), which is a novel layered hybrid framework that applies lightweight DP at the edge clients and threshold SMPC at the Non-RT RIC. The projected performance of the proposed framework tested against the reviewed benchmarks promises a 92% baseline accuracy retention.Fulltexten6GOpen RANFederated learningSecure Multi-Party ComputationPrivacy-Preserving AIZero-Trust ArchitectureConference Presentationn/a