Optimized and Privacy-Conscious Wearable Computing with User-Guided Access

Abstract: This privacy-conscious wearable computing model gives users contextual, real-time control over their data. In response to the increasing need for guaranteed, personalized health analytic, the suggested approach combines guaranteed multiparty computation (MPC) with multi-key fully homo-morphic encryption (MK-FHE) to alter encrypted processing without jeopardizing data confidentiality. The framework prioritizes edge-level encryption and just-in-time user accept mechanisms, ensuring secure data autonomy at every stage from learning to computation, in contrast to traditional cloud-driven subjects. This method establishes the foundation for user-centered, adaptive, and ethically sound digital health ecosystems by reducing centralized exposure and conforming to legal requirements such as GDPR and HIPAA. Keyword: Edge level encryption, Cloud-driven, Homo-morphic, Multiparty computation