A ROBUST AND SCALABLE SECURITY MODEL FOR EYE DISEASE DATA BASED ON CRYPTOGRAPHIC AND STEGANOGRAPHIC INTEGRATION

Abstract

In In today’s digital age, protecting the privacy and security of medical data has become increasingly important, especially in the healthcare sector where sensitive information is frequently stored and exchanged in electronic form. This study proposes a hybrid method for securing eye disease data by combining cryptography and steganography, with the goal of providing stronger protection than traditional single-layer security approaches. Eye disease datasets often contain both personal identification details and critical medical information, which makes them highly vulnerable to risks such as data breaches, identity theft, and unauthorized access. By using cryptographic techniques to convert data into an unreadable format and steganographic methods to hide that encrypted data within ordinary digital files, this research introduces a reliable two-level security framework. The proposed system ensures that even if data is intercepted, it remains both hidden and protected. This dual approach significantly reduces the likelihood of sensitive information being exposed or misused. In addition, the method is designed to maintain data integrity during transmission and storage. The framework also considers efficiency, ensuring that security enhancements do not lead to excessive computational overhead. Another important aspect of this study is its focus on medical images related to eye diseases, which require special handling due to their diagnostic importance. The system is capable of securely embedding patient data within these images without affecting their quality or usability. Furthermore, the approach addresses common challenges such as secure key management and resistance to common cyberattacks. The hybrid model also improves trust between patients and healthcare providers by ensuring that confidential information is handled responsibly. It can be integrated into existing healthcare systems with minimal modifications, making it practical for real-world adoption. Additionally, the framework supports secure data sharing among medical professionals, which is essential for collaborative diagnosis and treatment. The research highlights the importance of combining multiple security techniques to overcome the limitations of individual methods. It also opens new possibilities for future enhancements, such as incorporating artificial intelligence to further strengthen data protection. Overall, this study provides a comprehensive and effective solution for safeguarding sensitive ophthalmic data in modern healthcare environments.