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About the Journal

Aims and Scope

Journal Title: Journal of Big Data Privacy Management
Abbreviated Title: J. Big Data Priv. Manag.
p-ISSN: 3006-6026
e-ISSN: 3006-6034
Frequency: Semiannual
Publisher: Kashf Institute of Development & Studies, Pattoki, Pakistan
Language: English
Country of Publication: Pakistan
Medium: Print and Online (Hybrid)

Aim

The Journal of Big Data Privacy Management aims to foster academic discourse and innovative research in the evolving domain of big data and its intersection with privacy protection. The journal is committed to addressing the theoretical, technical, ethical, legal, and policy-related challenges involved in managing privacy in large-scale data environments. By bringing together multidisciplinary perspectives, the journal provides a vital platform for scholars, industry professionals, and policy makers to share practical solutions, novel frameworks, and cutting-edge technologies for ensuring privacy in the era of big data analytics.

Scope

The Journal of Big Data Privacy Management welcomes original research articles, review papers, case studies, technical notes, and short communications in areas including but not limited to:

  • Big Data Privacy: Concepts, frameworks, and mechanisms for protecting privacy in large-scale data ecosystems.

  • Data Governance: Models and practices for managing data responsibly and ensuring compliance with privacy standards.

  • Privacy-Preserving Technologies: Development and evaluation of technologies such as differential privacy, homomorphic encryption, secure multi-party computation, federated learning, etc.

  • Regulations and Compliance: Analysis of privacy laws such as GDPR, HIPAA, and emerging global data protection regulations.

  • Ethical and Social Implications: Examination of ethical concerns related to data usage, surveillance, and individual rights.

  • Privacy in Machine Learning and AI: Techniques and tools for integrating privacy into automated systems.

  • Data Anonymization and De-identification: Methods for data masking, pseudonymization, and identity protection.

  • Cybersecurity and Privacy: Intersection of data security measures with privacy assurance.

  • Applications in Sectors: Privacy challenges and solutions in healthcare, finance, education, smart cities, IoT, etc.

  • Risk Assessment and Trust Models: Quantifying and managing risks to data privacy in big data applications.

  • Privacy Audits and Impact Assessments: Tools and methodologies to evaluate and ensure data privacy compliance.

Target Audience

This journal is intended for:

  • Academics and researchers in data science, information systems, cybersecurity, and law

  • Data privacy officers and compliance professionals

  • Policymakers and regulators

  • Software engineers and developers working in data-sensitive domains

  • Graduate and postgraduate students studying data ethics, privacy, or big data technologies

Editorial Policy

The journal follows a rigorous double-blind peer review process and adheres to the highest standards of academic publishing ethics. It encourages interdisciplinary contributions and supports open discourse on emerging issues in privacy and data protection.