Ensuring Secure Data Environment in IoT-Based Remote Monitoring

In the era of IoT-enabled healthcare that ensure the secure and efficient data handling is vital for the Remote Patient Monitoring (RPM) systems. The paper proposes a hybrid security framework that integrates the Homomorphic Encryption (HE) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with the recurrent neural networks (RNNs) in order to preserve the data privacy while enabling an accurate health classification. The system employs the RNN for initial predictions and offloads the encrypted data to a centralized server to perform the deeper analysis. Experimental validation using the MIMIC-III Clinical Dataset shows the model that achieves of 85 % classification and demonstrates stable learning dynamics. This framework ensures a strong balance between data privacy, computational efficiency, and the real-time responsiveness, that make it well-suited for the deployment in a real-world IoT-based RPM environment.