International Journal of Science, Engineering and TechnologyAuthors: V.Narasimha Swamy, M.Bhuvaneswari, M.Pavan Kumar Reddy, S.Sai Sindhuri
Abstract: Cloud-based healthcare services help doctors deliver quick assessments and early diagnoses, even when patients are far from hospitals. However, sending medical data and diagnostic models to the cloud raises serious privacy concerns because sensitive patient information may not always be safe. The NAIAD framework addresses this by using encrypted kNN and secure Mahalanobis Distance calculations, allowing the cloud to process medical queries without ever seeing the actual data. It also speeds up the search process using a hierarchical encrypted index tree. While NAIAD provides good privacy and accuracy, it still has gaps in data optimization, fine-grained user control, and transparency of the results.The proposed system enhances NAIAD by focusing on smarter data preparation, stronger protection, and better verification. It filters and encrypts only meaningful medical features before outsourcing them, reducing computation and improving performance. Enhanced access control ensures that patients, doctors, and administrators can securely interact with the system while keeping sensitive records fully protected. The system also adds a result-verification mechanism so patients can confirm that the cloud processed their data honestly without modification or tampering. Additionally, the framework introduces better data organization, reduced redundancy, and improved communication efficiency—giving quicker responses during diagnosis. Security layers are strengthened to withstand modern cyber-attacks, ensuring long term trust in the system
