PRIVACY PRESERVING MEDICAL DATA SHARING WITH BIOMETRIC AUTHENTICATION AND ENCRYPTION

The security of healthcare data is of utmost importance, as conventional password-based authentication is susceptible to attacks, theft, and human errors. To combat these issues, this paper presents a privacy-protecting model that combines biometric authentication based on facial recognition (Grassmann algorithm) with Advanced Encryption Standard (AES) for safe sharing of medical data.The system only provides access to authorized individuals to sensitive documents by applying accurate biometric verification and secure encryption during storage and transmission.

 The suggested architecture employs a cloud-based system to ensure controlled and secure data exchange between healthcare organizations. By applying facial recognition rather than passwords and AES encryption, the system is significantly enhanced with respect to preventing unauthorized access and data leakage. Experimental deployment shows that the technique is viable, with an observation that it is possible to maximize data privacy without compromising on usability. Scalability enhancement and integration with current healthcare systems are directions of future research to render it more adoptable.

 

Keywords: Biometric Authentication,Facial Recognition,Grassmann Algorithm,AES Encryption,Medical Data Security,Privacy-Preserving Framework,Cloud-Based Data Sharing,Healthcare Cybersecurity,Access Control Secure ,Data Transmission

 Biometric Authentication Facial Recognition Grassmann Algorithm AES Encryption Medical Data Security Privacy-Preserving Framework Cloud-Based Data Sharing Healthcare Cybersecurity Access Control Secure Data Transmission