Research on Privacy Protection of Digital Twin Intelligence Based on Big Data in 5G System Security

A Secure Federated Learning and Blockchain Framework for E-Health Threat Detection

The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.

Federated Data Engineering Frameworks for Privacy-Aware Cross-Enterprise Analytics

The growing adoption of data-driven decision-making has improved operational intelligence, predictive analytics, and strategic planning across enterprises. However, privacy regulations, data sovereignty requirements, and competitive concerns often restrict direct data sharing between organizations. Federated Data Engineering (FDE) addresses these challenges by enabling collaborative analytics without transferring sensitive raw data. This paper presents a privacy-aware Federated Data Engineering framework that integrates federated learning, distributed data engineering, and secure model aggregation for cross-enterprise analytics. The framework supports decentralized data preprocessing, feature engineering, and encrypted parameter sharing while complying with regulations such as GDPR and HIPAA. It incorporates privacy-enhancing technologies, including differential privacy, secure multi-party computation, homomorphic encryption, and blockchain-based auditing, to ensure confidentiality, integrity, and transparency. The proposed architecture also improves scalability, communication efficiency, fault tolerance, and interoperability across heterogeneous enterprise environments. Experimental evaluation demonstrates enhanced collaborative analytics with reduced privacy risks and communication overhead. The framework provides a practical foundation for secure, trustworthy, and privacy-preserving cross-enterprise data collaboration in healthcare, finance, manufacturing, and other data-intensive industries.

Data Governance and Policy Support for Secure AI-Driven Corporate Digital Transformation

The study contributes to reliable and secure computing research by showing that technical controls, organizational routines, and policy support must be integrated to enable trustworthy AI-driven transformation across firms of different sizes and sectors.

Design and Validation Framework for Multi-Level Cybersecurity and Privacy Protection in Modern Digital Infrastructures

The increasing complexity of cyber threats, interconnected technologies, and data-intensive digital services has exposed limitations in security strategies that depend on isolated controls. This study develops a multi-level cybersecurity and privacy framework that integrates complementary controls across physical, network, endpoint, application, data, identity and access management, monitoring and incident response, and human and policy domains. The framework was developed through structured synthesis of the ten cybersecurity and privacy studies reviewed in the source manuscript and alignment with established cybersecurity guidance. The revised model treats monitoring and incident response as a cross-cutting capability and privacy and governance as cross-cutting concerns. It further introduces a measurable evaluation structure based on layer-specific security indicators and an overall Multi-Level Cybersecurity Resilience Index. The framework is mapped to NIST Cybersecurity Framework 2.0, ISO/IEC 27001:2022, and Zero Trust principles. The resulting architecture provides a practical basis for coordinating preventive, detective, responsive, recovery, governance, and privacy controls. Because the source studies did not include primary empirical testing, the present manuscript does not claim empirical effectiveness; instead, it specifies a validation protocol using expert assessment and/or controlled simulation. This study contributes an integrated architectural model and a measurable evaluation approach for organizations seeking adaptive and resilient cybersecurity.

A Review Study on Security Enhancements and Privacy considerations In Industrial Internet of Things (IIoT) Applications

The Industrial Internet of Things revolutionizes industrial processes by integrating advanced sensors, communication technologies, and data analytics to enhance operational efficiency and decision-making. Security enhancements and privacy considerations in IIoT applications face several challenges. These include safeguarding vast amounts of sensitive data across complex networks, ensuring robust authentication and authorization to prevent unauthorized access, and protecting against sophisticated cyber-attacks like DDoS and man-in-the-middle attacks. This review comprehensively explores and analyses current security challenges and privacy issues inherent in IIoT systems. Machine learning algorithms play a crucial role in enhancing security for IIoT systems by detecting anomalies, predicting potential threats, and automating responses. Blockchain platforms, including Ethereum, offer decentralized and immutable solutions for securing data transactions and ensuring transparency within IIoT environments. Privacy considerations in IIoT applications involve implementing robust data protection measures and ensuring compliance with regulatory standards to safeguard sensitive information. Exploring Ethereum experimentation for IIoT applications provides an opportunity to leverage smart contracts and distributed ledger technology to enhance security, streamline operations, and protect data integrity in industrial settings. Future research in security enhancements and privacy considerations for IIoT applications could explore the integration of emerging technologies such as quantum cryptography and AI-driven threat detection.

SA-LDP-DW: a dual-protection framework for trustworthy privacy-preserving data publishing