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PRIVACY THREATS IN BIG DATA: IDENTIFYING AND MITIGATING VULNERABILITIES IN DATA-DRIVEN SYSTEMS

Authors
  • Dr. Ayesha Siddiqui

    Department of Computer Science, COMSATS University Islamabad, Pakistan.
Keywords:
Big Data Privacy, Data Vulnerabilities, Privacy-Preserving Technologies, Data-Driven Systems Security
Abstract

The explosive growth of big data ecosystems has revolutionized the way organizations collect, process, and utilize information. However, it has also escalated privacy vulnerabilities in unprecedented ways. This study examines the major privacy threats inherent in big data environments and explores mitigation strategies tailored for data-driven systems. Key vulnerabilities such as re-identification risks, unauthorized access, third-party data misuse, and inference attacks are analyzed. The paper further presents frameworks and technologies, including differential privacy and blockchain, to safeguard sensitive information. Through a critical review of existing literature and graphical insights, this article aims to contribute to a more resilient big data security landscape.

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Published
2024-06-30
Section
Articles
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Copyright (c) 2024 Dr. Ayesha Siddiqui

Creative Commons License

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

PRIVACY THREATS IN BIG DATA: IDENTIFYING AND MITIGATING VULNERABILITIES IN DATA-DRIVEN SYSTEMS. (2024). Journal of Big Data Privacy Management, 2(1), 42-49. https://doi.org/10.71146/jbdpm22