LEVERAGING HOMOMORPHIC ENCRYPTION FOR PRIVACY-PRESERVING BIG DATA ANALYSIS

• Dr. Faizan Ali

  Department of Data Science, University of Engineering and Technology (UET), Lahore, Pakistan.

With the exponential growth of Big Data, preserving the privacy and security of sensitive information during analysis has become a critical concern. Traditional encryption methods often hinder data usability, making it difficult to analyze encrypted data without first decrypting it. Homomorphic encryption (HE) offers a promising solution, enabling computations to be performed directly on encrypted data, thereby preserving privacy. This article explores the application of homomorphic encryption in Big Data analysis, focusing on its potential to maintain data confidentiality while enabling complex analytical tasks. We examine various HE schemes, their strengths, limitations, and their integration into Big Data ecosystems. Additionally, we discuss real-world applications, challenges, and future research directions in adopting homomorphic encryption for large-scale, privacy-preserving data analytics.

Gentry, C. (2009). "A Fully Homomorphic Encryption Scheme." Proceedings of the 41st Annual ACM Symposium on Theory of Computing.

Paillier, P. (1999). "Public-Key Cryptosystems Based on Composite Degree Residuosity Classes." EUROCRYPT 1999.

Gentry, C., & Halevi, S. (2011). "Implementing Gentry’s Fully Homomorphic Encryption Scheme." ACM Transactions on Computational Logic.

Shamir, A. (1979). "How to Share a Secret." Communications of the ACM.

Miele, A., & Brackney, M. (2017). "Efficient Privacy-Preserving Big Data Analysis." IEEE Transactions on Knowledge and Data Engineering.

Zhang, W., & Zhang, Z. (2018). "Efficient Privacy-Preserving Big Data Computations Using Homomorphic Encryption." Journal of Cryptography.

Smart, N. P., & Vercauteren, F. (2014). "Fully Homomorphic Encryption with Concrete Efficiency Improvements." Proceedings of the International Conference on Cryptology.

Chabanne, H., & Gascuel, P. (2013). "Homomorphic Encryption for Privacy-Preserving Computations." IEEE Access.

Kim, H., & Wang, X. (2016). "Scalable Homomorphic Encryption Scheme for Big Data Security." Proceedings of the IEEE International Conference on Big Data.

Brakerski, Z., & Vaikuntanathan, V. (2014). "Efficient Fully Homomorphic Encryption from (Standard) LWE." Proceedings of the IEEE Symposium on Security and Privacy.

Nguyen, D. K., & O’Neill, M. (2020). "Challenges in Scaling Homomorphic Encryption for Large-Scale Big Data." Journal of Big Data.

Liu, Y., & Li, Y. (2019). "Practical Implementation of Privacy-Preserving Big Data Analytics Using Homomorphic Encryption." Proceedings of the International Conference on Data Science and Advanced Analytics.

Li, X., & Liu, F. (2020). "Homomorphic Encryption for Secure Machine Learning in Big Data Analytics." Journal of Information Security and Applications.

Zhang, S., & Zhang, T. (2017). "Privacy-Preserving Data Analysis Using Homomorphic Encryption in Cloud Computing." Journal of Cloud Computing: Advances, Systems, and Applications.

Popa, R. A., & Andreeva, G. (2016). "Secure Data Sharing with Homomorphic Encryption in Cloud Environments." Proceedings of the IEEE Symposium on Security and Privacy.

Gentry, C., & Ramzan, Z. (2016). "Computationally Efficient Fully Homomorphic Encryption." Journal of Cryptographic Engineering.

Li, H., & Liu, W. (2018). "Data Privacy in the Internet of Things Using Homomorphic Encryption." International Journal of Information Security.

Shokri, R., & Shmatikov, V. (2017). "Privacy-Preserving Big Data Mining with Homomorphic Encryption." IEEE Transactions on Privacy and Security.

Acar, A., & Neves, R. (2019). "Privacy by Design in Big Data: Homomorphic Encryption for Secure Analytics." Proceedings of the ACM Workshop on Data Privacy.

Swaminathan, K., & Joshi, A. (2018). "Homomorphic Encryption for Big Data Analytics: A Survey of Challenges and Future Directions." Big Data Research.

Ahmad, N. R. (2025). Human–AI collaboration in knowledge work: Productivity, errors, and ethical risk. Journal of Humanities and Social Sciences. https://doi.org/10.52152/6q2p9250

Ahmad, N. R. (2025). Urban water service delivery in emerging economies: Fiscal sustainability, cost recovery, and governance performance. International Journal of Business and Economic Analysis. https://doi.org/10.24088/IJBEA-2025-103005

• PDF
• abstract video

Copyright (c) 2024 Dr. Faizan Ali

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.