Block-Based Privacy-Preserving Healthcare Data Ranked Retrieval in Encrypted Cloud File Systems

Area of Science:

• Health Informatics
• Cloud Computing Security
• Data Privacy

Background:

• The Internet of Medical Things (IoMT) faces challenges with efficient and private cloud-based healthcare data management.
• Existing data retrieval schemes often compromise efficiency and user privacy with large datasets.
• Securing sensitive health documents in the cloud is critical for IoMT applications.

Purpose of the Study:

• To propose a novel block-based, privacy-preserving search system for encrypted cloud healthcare data.
• To enhance the efficiency and accuracy of data retrieval in IoMT environments.
• To improve patient and researcher privacy against potential data breaches and analysis.

Main Methods:

• Development of an Efficient Encrypted Parallel Ranking (EEPR) search system.
• Implementation of a block-based parallel binary search tree structure.
• Design of a parallel retrieval algorithm tailored for the proposed data structure.

Main Results:

• Quantitative analysis using the information retention index indicates superior search performance.
• Feature vectors generated by the EEPR scheme offer enhanced privacy due to unexplainability.
• Formal security analysis confirms resistance to known background attacks and lower time complexity.

Conclusions:

• The EEPR scheme significantly improves search efficiency and accuracy for encrypted cloud healthcare data.
• The system provides robust privacy protection for sensitive patient information.
• EEPR offers a more secure and efficient solution for IoMT data management compared to existing methods.