Symmetry-driven neural networks for secure and optimized data processing in E-government applications

Abstract

As digital governance increasingly shapes the future of public administration, the demand for secure and efficient E-government services continues to rise. Traditional neural networks, though successful across various fields, often face challenges in scalability, security, and processing speed when dealing with governmental data. This study introduces a novel framework by embedding symmetrical principles within a neural network architecture, aiming to strengthen data protection and streamline operational efficiency in E-government systems. By integrating symmetry at the architectural level, the model reduces redundant computations, leading to faster and more resilient data processing. Moreover, this approach enhances the system’s defense against adversarial threats, a critical concern for public sector applications. The proposed model specifically addresses the unique requirements of E-government platforms, focusing on secure data transmission and robust resistance to security vulnerabilities. Our experimental evaluations highlight notable improvements in processing speeds and security performance, demonstrating the model’s practical potential for Realtime public sector operations. Beyond immediate applications, this work lays a strong foundation for further research into symmetry-driven network designs, offering promising solutions to the complex challenges inherent in managing sensitive public data.