Abstract

The rapid growth of distributed computing paradigms, such as the Internet of Things (IoT), edge computing, cloud computing and cyber-physical systems, has made privacy-preserving anomaly detection a pressing research challenge. This paper presents a systematic literature review, conducted following the PRISMA 2020 guidelines, of 50 studies published between 2018 and 2026 that combine machine-learning-based anomaly detection with privacy-enhancing technologies. We organise the literature along four axes: detection models (Isolation Forest, autoencoders, one-class SVM, graph neural networks and transformers), learning paradigms (centralized versus federated learning), privacy mechanisms (homomorphic encryption, differential privacy, secure multi-party computation and zero-knowledge proofs), and integrity mechanisms based on blockchain. The reviewed applications span IoT security, healthcare, finance, industrial control, V2X networks, the metaverse and supply-chain management. Synthesising the reported evidence, the review finds that federated learning combined with the lightweight Isolation Forest is the approach most frequently associated with a favourable trade-off between detection quality, privacy protection and computational cost on resource-constrained edge devices, while hybrid designs that add differential privacy or homomorphic encryption offer stronger formal guarantees at a measurable cost in accuracy and latency.