The integration of Internet of Things (IoT) sensors with edge-cloud computing for remote patient monitoring (RPM) offers transformative potential for personalized healthcare. However, this integration introduces critical challenges related to data security, patient privacy, and system scalability. In this paper, we present Zeus, a privacy-preserving and secure IoT-based healthcare data management framework for cloud-based remote patient monitoring and in-place sensor-based care. Using a real-world use case of a BodiGuide anklet – a wearable edema monitor for heart failure management, we present a reference edge-cloud architecture that supports secure data collection, transmission via edge gateways, cloud-based analytics and storage, and role-based visualization through an intuitive user interface. Zeus integrates layered security and privacy controls at each stage of the data lifecycle—from collection and transmission to visualization—through a comprehensive risk assessment based on privacy and security threat modeling. Our approach integrates privacy-preserving and security mechanisms such as pseudonymization, role-based access control and data validation to ensure ongoing compliance with healthcare standards. We empirically validate Zeus features during security analysis and privacy analysis featuring structured metrics and quantified risk heatmaps considering attack simulations involving Attempt Likelihood, and Likelihood of Successful Attack combined with Attack Impact. Finally, we perform scalability evaluations using a cloud-based testbed to demonstrate Zeus’s ability to handle increasing sensor data volumes and concurrent user loads in RPM environments.