Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)

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A Sensitivity-Driven Framework for Privacy-Preserving Big Data Publishing: The CAG+RAG Approach

Authors
K. Rajeshwar Rao1, *, Durgesh Nandan2, S. Satyanarayana3
1Department of AI & ML, School of Engineering, Malla Reddy University, Hyderabad (MRUH), Hyderabad, Telangana, India
2School of CS & AI, SR University, Warangal, Telangana, 506371, India
3Department of Artificial Intelligence & Machine Learning, Malla Reddy University, Hyderabad, Telangana, India
*Corresponding author. Email: drrajeshwarrao@mallareddyuniversity.ac.in
Corresponding Author
K. Rajeshwar Rao
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-978-0_17How to use a DOI?
Keywords
Privacy Preservation; Big Data; Sensitivity Analysis; Context-Aware Generalization; Risk-Aware Generation; Data Publishing
Abstract

The exponential growth of big data has created unprecedented opportunities for data-driven insights while simultaneously raising critical privacy concerns. This paper presents a novel framework combining Context-Aware Generalization (CAG) with Risk-Aware Generation (RAG) for privacy-preserving data publishing based on sensitivity levels. Our approach addresses the challenge of maintaining data utility while ensuring robust privacy protection in large-scale datasets. We introduce the Sensitivity-Aware Privacy Preservation (SAPP) algorithm that dynamically adjusts anonymization techniques based on contextual sensitivity analysis. Experimental results on real-world datasets demonstrate that our CAG+RAG framework achieves superior performance with 23% better privacy protection and 18% higher data utility compared to existing methods. The framework successfully handles datasets with over 10 million records while maintaining sub-linear computational complexity.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
Series
Advances in Engineering Research
Publication Date
31 December 2025
ISBN
978-94-6463-978-0
ISSN
2352-5401
DOI
10.2991/978-94-6463-978-0_17How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - K. Rajeshwar Rao
AU  - Durgesh Nandan
AU  - S. Satyanarayana
PY  - 2025
DA  - 2025/12/31
TI  - A Sensitivity-Driven Framework for Privacy-Preserving Big Data Publishing: The CAG+RAG Approach
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 189
EP  - 198
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_17
DO  - 10.2991/978-94-6463-978-0_17
ID  - Rao2025
ER  -