Aim & Scope

The Journal of Big Data Privacy Management (JBDPM) is a peer-reviewed scholarly publication dedicated to advancing research, practice, and policy in the field of big data privacy management. The journal aims to provide a platform for academics, researchers, practitioners, and policymakers to disseminate innovative ideas, methodologies, and findings that address the complex challenges of protecting individual and organizational privacy in the age of big data.

JBDPM welcomes high-quality submissions covering, but not limited to, the following areas:

• Privacy-Preserving Data Management: Techniques, algorithms, and frameworks for ensuring data privacy in large-scale datasets.

• Machine Learning and Privacy: Applications of AI and machine learning for privacy risk prediction, anonymization, and secure data analytics.

• Data Utility vs. Privacy Trade-offs: Research exploring the balance between maintaining data utility and protecting sensitive information.

• Regulatory Compliance and Governance: Studies on global data protection regulations, cross-border data flows, and compliance strategies.

• Ethics and Public Perception: Investigations into ethical considerations, societal impacts, and public awareness of big data privacy.

• Emerging Technologies: The role of blockchain, federated learning, and other emerging technologies in enhancing data privacy.

• Industry Applications: Case studies in sectors such as healthcare, finance, marketing, tourism, and smart cities.

The journal publishes original research articles, review papers, case studies, and technical notes. By fostering interdisciplinary dialogue, JBDPM seeks to advance responsible data practices, guide policy development, and promote innovative solutions that safeguard privacy in increasingly complex digital environments.