Securing Machine‐Type Communications: A Survey on Privacy Threats and Countermeasures

Machine‐type communication (MTC) is a fundamental enabler of the Internet of Things (IoT) and emerging 5G/6G networks, supporting massive deployments of heterogeneous and resource‐constrained devices. However, large‐scale data collection, persistent connectivity, and limited device capabilities introduce critical privacy challenges that are not adequately addressed by conventional security mechanisms designed for human‐centric or homogeneous networks. This survey presents a comprehensive analysis of privacy threats and countermeasures in MTC networks. Using a domain‐based approach aligned with the ETSI M2M architecture, we examine privacy vulnerabilities and attacks across the device, network, and application domains. We further provide a structured classification of privacy‐preserving solutions, encompassing identity protection, data confidentiality, and behavioral obfuscation, and compare them in terms of effectiveness and deployment feasibility. Finally, we identify open challenges and research directions for scalable and lightweight privacy protection in massive MTC environments and propose a cybersecurity framework that integrates technical, regulatory, and operational considerations to support trustworthy MTC systems.