Design of a multi-layered privacy-preserving architecture for secure medical data exchange in cloud environments

Cloud-based e-health systems make medical data widely accessible for treatment, teleconsultation, and analytics; however, the moment clinical records are pushed to an external cloud, they are exposed to a combination of threats: curious insiders, colluding storage providers, and silent data tampering. Most existing attribute-based encryption (ABE) or standalone anomaly detection solutions address these threats in isolation. To address this gap, this work presents a unified, lifecycle-oriented security framework that integrates Server-Aided Revocable Attribute-Based Encryption (SR-ABE) to enforce fine-grained, revocable access control without requiring the re-encryption of historical data. The cryptographic layer is further strengthened through haze optimization-guided key generation, which enhances key-space exploration and randomness during the encryption process. Data integrity is ensured using a SHA-256-based verification mechanism applied at both the storage and access stages. In addition, an intelligent monitoring layer based on a heterogeneous Mixed Graph Neural Network (MGNN) model interacts with patterns among users, devices, and resources to enable continuous event-driven anomaly detection within the system"s operational latency bounds. Under this integrated design, the proposed model attains 99.1% training accuracy and 96.7% testing accuracy, converges with very low errors (training loss 0.0126, testing loss 0.0357), executes faster than prior approaches with an execution time of 10.4 ms at 50 epochs, sustains a high service throughput of 852-901 kbps even when multiple users are active, operates with reduced energy consumption of 0.214 J, maintains low access latency of 6.6 ms, and raises core security indicators to 99.12% data confidentiality and 98.53% data integrity. This shows that combining SR-ABE, HO-based key generation, cryptographic integrity checks, and MGNN-driven surveillance in a single pipeline delivers both stronger privacy guarantees and better runtime performance than existing cloud e-health security schemes.