THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING PRIVACY MANAGEMENT IN BIG DATA

• Dr. Nadia Khan

  Institute of Information Technology, National University of Sciences and Technology (NUST), Islamabad, Pakistan Big Data Analytics, Privacy-Preserving Techniques, Artificial Intelligence.

As the volume of data generated globally continues to surge, the challenge of managing and protecting personal information has become increasingly complex. Big data systems, which aggregate vast amounts of sensitive data, require advanced privacy management mechanisms to ensure that individuals' data remains secure and anonymous. Artificial Intelligence (AI) and Machine Learning (ML) offer promising solutions to address these privacy challenges. This article explores the role of AI and ML in enhancing privacy management in big data environments, with a focus on privacy-preserving techniques such as differential privacy, federated learning, and blockchain. The paper also discusses the potential of AI and ML to automate privacy risk detection, optimize data anonymization, and ensure compliance with regulatory frameworks. By providing an in-depth analysis of existing methods and emerging trends, this article outlines how AI and ML can be integrated into privacy management strategies for big data systems.

Narayanan, A., & Shmatikov, V. (2008). Robust De-anonymization of Large Datasets. IEEE Symposium on Security and Privacy, 111-125.

Dwork, C. (2006). Differential Privacy. International Colloquium on Automata, Languages and Programming, 1-12.

Zyskind, G., Nathan, O., & Pentland, A. (2015). Decentralizing Privacy: Using Blockchain to Protect Personal Data. Proceedings of the IEEE Symposium on Security and Privacy, 1-15.

Shokri, R., et al. (2017). Privacy-Preserving Machine Learning: Threats and Solutions. IEEE Transactions on Knowledge and Data Engineering, 29(4), 1-12.

Kitchin, R. (2016). Data and the City: The Emergence of Big Data, Smart Cities, and Urban Informatics. Journal of Urban Technology, 23(4), 1-24.

Cavoukian, A. (2012). Privacy by Design: The 7 Foundational Principles. Information and Privacy Commissioner of Ontario.

Greenleaf, G. (2014). Global Data Privacy Laws: A 2014 Update. International Data Privacy Law, 4(2), 87-104.

Zwitter, A. (2014). Big Data Ethics. Big Data & Society, 1(2), 1-16.

Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A Practical Guide. Springer.

Jagadish, H. V. (2015). Big Data Privacy: A Survey. ACM Computing Surveys, 48(3), 1-31.

Acquisti, A., et al. (2016). The Economics of Personal Data and Privacy: A Survey. Journal of Economic Literature, 54(2), 443-493.

Pentland, A. (2012). Privacy in the Age of Big Data. Harvard Business Review, 90(9), 1-10.

Fernandes, D., et al. (2017). Encryption and Privacy in Big Data Analytics. IEEE Transactions on Dependable and Secure Computing, 14(2), 195-210.

Taddeo, M., & Floridi, L. (2018). The Ethics of Artificial Intelligence: A Critical Analysis. Ethics and Information Technology, 20(3), 1-11.

Christidis, K., & Devetsikiotis, M. (2016). Blockchains and Smart Contracts for the Internet of Things. IEEE Access, 4, 2292-2303.

Katal, A., et al. (2013). Privacy Preservation in Big Data: Challenges and Solutions. International Journal of Computer Science and Information Security, 11(10), 17-25.

Dib, F., et al. (2017). Data Leakage Prevention in Big Data Environments. Journal of Information Security and Applications, 31, 1-12.

Greitzer, F., & Frincke, D. (2010). The Insider Threat to Information Systems. Computers & Security, 29(1), 75-88.

Martin, K., et al. (2019). Data Privacy in Smart Cities: Challenges and Solutions. Smart Cities, 2(1), 19-34.

Acquisti, A., & Grossklags, J. (2015). Privacy in the Age of Big Data. International Journal of Communication, 9, 1-18.

Ahmad, N. R. (2024). AI-enabled public governance in developing states: Service delivery gains, accountability risks, and a practical risk-based regulatory model. https://doi.org/10.52152/wja5db40

• PDF
• Abstract video

Copyright (c) 2024 Dr. Nadia Khan

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in the Journal of Big Data Privacy Management are distributed under the terms of an open-access license, permitting unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Authors retain copyright of their work and grant the journal the right of first publication. The journal promotes transparency, accessibility, and global dissemination of research findings.