An Integrated Privacy Preserving Cybersecurity for Distributed IoT, Edge and Cloud Environments

The increasing convergence of Internet of Things, edge and cloud computing has transformed modern digital ecosystems but has also introduced significant cybersecurity challenges related to data privacy, trust management and regulatory compliance. Existing centralized security frameworks often rely on extensive data aggregation, creating privacy vulnerabilities and limiting their effectiveness in highly distributed environments. To address these limitations, this study presents a unified privacy preserving cybersecurity framework that integrates federated learning, differential privacy, blockchain based trust management and explainable artificial intelligence (XAI). A distributed prototype was implemented by deploying collaborative intrusion detection nodes across simulated IoT, edge and cloud infrastructures. Federated learning enables participating nodes to jointly train intrusion detection models without exposing locally generated data, while differential privacy protects model updates against potential information leakage. Blockchain technology establishes a decentralized and tamper resistant trust mechanism that strengthens auditability and collaboration among participating entities. Furthermore, explainable AI provides interpretable security insights that enables analysts to understand and validate model predictions with greater confidence. The proposed framework was evaluated using benchmark intrusion detection datasets under distributed cyberattack scenarios. Experimental findings demonstrate that the framework consistently achieves high intrusion detection performance along with preserving data privacy, maintaining trusted collaboration and improving the transparency of security decisions. These results highlight the potential of integrating decentralized learning, privacy preserving mechanisms, blockchain enabled trust and explainable intelligence to develop secure, scalable and trustworthy cybersecurity solutions for next generation distributed computing environments.