Hybrid model for safeguarding data and addressing ethical concerns using homomorphic encryption and generative AI (Gen AI)

DOI

10.2991/978-94-6463-738-0_2How to use a DOI?

Keywords

Cybersecurity; Generative AI; Homomorphic encryption; Ethical concerns

Abstract

We introduces a hybrid model that combines homomorphic encryption and Generative AI (Gen AI) to address the growing security and ethical concerns surrounding modern data protection. In today’s data-driven world, maintaining the confidentiality, integrity, and privacy of sensitive information is more critical than ever. Traditional security measures, while effective, often fail to keep up with the increasing complexity of cyber threats and the rising demand for ethical data handling practices. This innovative solution offers a comprehensive approach by integrating cutting-edge technologies to meet both security and privacy needs.

The system operates by continuously monitoring network ports to detect potential real-time threats. By analyzing traffic and identifying suspicious activity, it proactively categorizes critical data and ensures that it is securely stored on external drives. This added layer of protection minimizes the risks of unauthorized access, data breaches, and other security vulnerabilities.

Homomorphic encryption plays a vital role in this model by allowing data to remain secure during updates or processing without exposing the underlying information. This form of encryption enables computations to be performed on encrypted data, ensuring that sensitive data remains private even in untrusted environments. Meanwhile, Generative AI assists in prioritizing and adapting the handling of data based on privacy requirements. It dynamically evaluates and responds to changing privacy needs, optimizing data protection based on context.

By combining homomorphic encryption with Generative AI, this hybrid model offers a robust and adaptive solution to safeguarding sensitive information. It ensures compliance with ethical data handling practices while providing a scalable, future-proof approach to modern data protection.

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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TY  - CONF
AU  - N. Sandeep Chaitanya
AU  - V. Virinchi Reddy
AU  - V. Haneesh Reddy
AU  - K. Sai Kiran
AU  - U. Raghavendra
PY  - 2025
DA  - 2025/06/22
TI  - Hybrid model for safeguarding data and addressing ethical concerns using homomorphic encryption and generative AI (Gen AI)
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 3
EP  - 16
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_2
DO  - 10.2991/978-94-6463-738-0_2
ID  - Chaitanya2025
ER  -