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

The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In IoMT, efficiency and user privacy are crucial for cloud storage and retrieval of healthcare data documents. Existing schemes, however, often suffer from inefficient retrieval and increased risk of privacy disclosure when dealing with massive data. We propose here a new Efficient Encrypted Parallel Ranking (EEPR) search system, block-based and privacy-preserved, for encrypted cloud healthcare data. We design a parallel binary search tree structure in block and propose a parallel retrieval algorithm adaptable to such a structure. A quantitative analysis through the information retention index shows that our scheme demonstrates better search performance. In addition, feature vectors generated from our scheme are difficult to be reversely analyzed due to unexplainability, enhancing privacy protection for patients and researchers. A formal security analysis shows that our EEPR scheme is resistable to known background attack, and yields a lower time complexity and significantly improves search efficiency as well as accuracy over existing schemes.

Automated summary

The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In this study, the authors propose a new Efficient Encrypted Parallel Ranking (EEPR) search system for encrypted cloud healthcare data, addressing the issues of inefficient retrieval and increased privacy risk faced by existing schemes. The proposed system demonstrates better search performance and enhanced privacy protection, and it is resistant to known background attacks, showing significantly improved time complexity and search efficiency compared to existing schemes.