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ADVANCEMENTS IN PRIVACY-PRESERVING TECHNIQUES FOR BIG DATA ANALYTICS

Authors
  • Dr. Faisal Ahmed

    Research Scientist, Data Privacy and Security, International Centre for Research in Data Science, Karachi, Pakistan
Keywords:
Privacy-Preserving Techniques, Big Data Analytics, Differential Privacy, Federated Learning, Data Encryption
Abstract

Big Data analytics have revolutionized multiple sectors by extracting valuable insights from vast data pools. However, with the increasing reliance on data, privacy concerns have become paramount, particularly in industries like healthcare, finance, and e-commerce. Privacy-preserving techniques aim to protect sensitive information while enabling analytics. This paper provides an overview of recent advancements in privacy-preserving methods, including encryption techniques, differential privacy, and federated learning. We explore the strengths, challenges, and future directions of these techniques in ensuring data security and privacy without compromising analytical capabilities. The study also evaluates the regulatory landscape and the need for compliance with global privacy standards, highlighting the importance of privacy in the era of Big Data.

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References

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Sohail, N., Haque, A., Aziz, S., & Maruf, T. I. (2023). Impact of diffusion of innovation among Pakistani consumers’ adoption of halal cosmetics. *South East Asia Journal of Contemporary Business, Economics and Law, 30*(2), 39–49. ISSN 2289-1560.

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Published
2023-06-30
Section
Articles
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Copyright (c) 2023 Dr. Faisal Ahmed

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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.

How to Cite

ADVANCEMENTS IN PRIVACY-PRESERVING TECHNIQUES FOR BIG DATA ANALYTICS. (2023). Journal of Big Data Privacy Management, 1(01), 1-10. https://doi.org/10.71146/jbdpm3