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EXPLORING PRIVACY-ENHANCING CRYPTOGRAPHIC TECHNIQUES FOR BIG DATA

Authors
  • Dr. Muhammad Ali Khan

    Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan.
Keywords:
Privacy-enhancing cryptography, Big data security, Homomorphic encryption, Secure multi-party computation (SMPC)
Abstract

The exponential growth of big data has revolutionized industries and research, offering unprecedented opportunities for analysis and decision-making. However, with this expansion comes a critical concern over the privacy and security of sensitive information. Privacy-enhancing cryptographic techniques (PECTs) play a pivotal role in addressing these challenges by enabling secure data analysis without compromising individual privacy. This article explores various PECTs, including homomorphic encryption, secure multi-party computation (SMPC), and differential privacy, evaluating their effectiveness in ensuring the confidentiality, integrity, and utility of big data. Additionally, the integration of these techniques with emerging technologies such as machine learning and blockchain is discussed. The paper concludes by examining the current challenges and the future prospects of privacy-preserving cryptographic methods in the era of big data.

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References

Sohail, N., Haque, A., & Ahmad Suki, A. B. (2022). The influence of knowledge, innovation compatibility and social influence on adoption of halal cosmetics: An empirical evidence from Pakistan. Journal of Innovative Research and Publications, 2(5), 1–17.

Lip, T. K., Abd Wahab, S., Yusoff, T., Sohail, N., Muthaya, K., Abdulsamad, A., & Al-refaei, A. A.-A. (2025). Entrepreneurial knowledge and sustainable SMEs performance: The mediation role of strategic opportunity recognition. Advances in Social Sciences Research Journal, 12(4), 24–42. https://doi.org/10.14738/assrj.1204.18550

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Published
2023-12-31
Section
Articles
License
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

EXPLORING PRIVACY-ENHANCING CRYPTOGRAPHIC TECHNIQUES FOR BIG DATA. (2023). Journal of Big Data Privacy Management, 1(02), 93-105. https://doi.org/10.71146/jbdpm14