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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • S. Muthuvel,
  • S. Priya,
  • K. Sampath Kumar

摘要

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.