AI for Sustainable Data Protection and Fair Algorithmic Management in Environmental Regulation

TL;DR

This paper explores how AI can enhance cryptographic techniques like homomorphic encryption and multi-party computation to improve data protection and fairness in environmental regulation, addressing cyber threats and algorithmic bias.

Contribution

It evaluates AI-driven enhancements to cryptographic methods for sustainable, fair, and secure environmental data management, filling a research gap at the intersection of AI, cyber laws, and environmental regulation.

Findings

AI-driven dynamic key management improves security.

Adaptive encryption schemes enhance data privacy.

Optimized protocols increase computational efficiency.

Abstract

Integration of AI into environmental regulation represents a significant advancement in data management. It offers promising results in both data protection plus algorithmic fairness. This research addresses the critical need for sustainable data protection in the era of ever evolving cyber threats. Traditional encryption methods face limitations in handling the dynamic nature of environmental data. This necessitates the exploration of advanced cryptographic techniques. The objective of this study is to evaluate how AI can enhance these techniques to ensure robust data protection while facilitating fair algorithmic management. The methodology involves a comprehensive review of current advancements in AI-enhanced homomorphic encryption (HE) and multi-party computation (MPC). It is coupled with an analysis of how these techniques can be applied to environmental data regulation. Key findings indicate that AI-driven dynamic key management, adaptive encryption schemes, and optimized computational efficiency in HE, alongside AI-enhanced protocol optimization and fault mitigation in MPC, significantly improve the security of environmental data processing. These findings highlight a crucial research gap in the intersection of AI, cyber laws, and environmental regulation, particularly in terms of addressing algorithmic bias, transparency, and accountability. The implications of this research underscore the need for stricter cyber laws. Also, the development of comprehensive regulations to safeguard sensitive environmental data. Future efforts should focus on refining AI systems to balance security with privacy and ensuring that regulatory frameworks can adapt to technological advancements. This study provides a foundation for future research aimed at achieving secure sustainable environmental data management through AI innovations.