Role-Based Access Control, Secure Multi-Party Computation, and Hierarchical Identity-Based Encryption: Combining AI to Improve Mobile Healthcare Security

Background Patient data is sensitive and its demand for privacy creates specific security challenges for mobile healthcare systems. The research explores the secure and scalable applications of AI-based strategies in combination with Hierarchical Identity-Based Encryption (HIBE) and Role-Based Access Control (RBAC), Selection of Participants using Secure Multi-Party Computation (SMC) for mass-used mobile health (mHealth) apps. Methods HIBE and SMC We utilize hierarchical identity-based encryption (HIBE) to encrypt data at different access levels in the hierarchy, role-based access control (RBAC) to retrieve permitted roles concerning user identities along with privacy-preserving collaborative data processing employing secure multi-party computation (SMC). With the help of AI, each layer is optimized which also makes the used framework much secure and efficient in dynamic settings related to mHealth. Objectives Develop a comprehensive secure multi-party AI-enabled system to enhance mHealth data privacy, restrict access to authorized entities only, optimize role-based permissions of the data, and perform private multiparty computation on the data Results The combined AI-integrated framework is more effective than previous models achieving 94% accuracy, and 93% efficiency along with increased recall and precision showing the highlights of secure data access, privacy, and role management for mobile healthcare. Conclusion The framework is a concrete, well-performing, safe solution to mobile healthcare since it establishes a trade-off between access control and privacy of the data versus collaborative processing, thus enhancing AI potential for secure mHealth applications.