ADVANCEMENTS IN PRIVACY-PRESERVING TECHNIQUES FOR BIG DATA ANALYTICS
- Authors
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Dr. Faisal Ahmed
Research Scientist, Data Privacy and Security, International Centre for Research in Data Science, Karachi, Pakistan
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- Keywords:
- Privacy-Preserving Techniques, Big Data Analytics, Differential Privacy, Federated Learning, Data Encryption
- Abstract
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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
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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
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Copyright (c) 2023 Dr. Faisal Ahmed

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