Dynamic access control model for privacy preserving personalized healthcare in cloud environment

Area of Science:

• Cloud Computing
• Data Privacy
• Healthcare Informatics

Background:

• Securing patient data in cloud environments is crucial due to potential unauthorized access.
• Dynamic authorization is necessary as personal health data is increasingly stored via wearable devices in the cloud.

Purpose of the Study:

• To propose a novel dynamic access control model for enhancing personal healthcare data privacy in cloud storage.
• To address the limitations of static access control in dynamic cloud-based healthcare systems.

Main Methods:

• Developed a dynamic access control model incorporating context-aware authorization.
• Defined access permissions based on changing context information and access conditions, independent of static roles.
• Utilized real-life personalized healthcare system scenarios for model validation.

Main Results:

• The proposed model dynamically adjusts access permissions based on contextual factors.
• Demonstrated that access control varies even for users with the same role, depending on context.
• Validated the model's effectiveness in providing correct access decisions in simulated healthcare scenarios.

Conclusions:

• The dynamic access control model effectively enhances personal healthcare data privacy in cloud environments.
• Contextual information is a key factor in achieving granular and adaptive access control for sensitive health data.
• The proposed approach offers a robust solution for managing data access in the era of connected healthcare devices.