Privacy Preserving Cybersecurity Framework for Next Generation Digital Systems

The rapid adoption of intelligent and autonomous digital systems across IoT, edge, and cloud environments has significantly increased the complexity of cybersecurity threats, while existing security solutions struggle to ensure scalability, data privacy, trust, and regulatory compliance. Centralized AI-based security models expose sensitive data, incur high communication overhead, and lack transparency, limiting their suitability for next-generation autonomous ecosystems. To address these challenges, this paper proposes a unified, privacy-preserving, and AI-driven cybersecurity framework that integrates federated learning for decentralized threat detection, differential privacy for data confidentiality, blockchain for secure trust management, and explainable artificial intelligence to enhance transparency and accountability. The proposed framework enables collaborative and autonomous security intelligence without sharing raw data. Experimental evaluation using benchmark cybersecurity datasets and simulated attack scenarios demonstrates improved intrusion detection accuracy, reduced communication overhead, enhanced privacy protection, and better interpretability compared to existing approaches. The framework is applicable to a wide range of intelligent digital ecosystems, including smart cities, healthcare IoT, autonomous vehicles, industrial automation, and cloud edge infrastructures, offering a scalable, trustworthy, and regulation-compliant cybersecurity solution for future autonomous systems.