PRIVACY CONCERNS IN BIG DATA-DRIVEN MACHINE LEARNING: ADDRESSING THE RISKS OF PERSONALIZATION

• Dr. Haris Raza

  Department of Data Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan

With the increasing reliance on big data-driven machine learning (ML) systems, privacy concerns have become a significant challenge, particularly when it comes to the personalization of services. These personalized systems utilize vast amounts of personal and sensitive data, increasing the risk of privacy breaches, data misuse, and unintended consequences. This article investigates the privacy risks inherent in personalization processes, explores existing privacy protection measures, and proposes strategies to safeguard personal information while maintaining the benefits of machine learning applications. The paper also presents a discussion on the ethical implications of using personalized ML models and highlights the need for robust privacy regulations and technological solutions to ensure data protection.

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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