Efficient and privacy-preserving similar electronic medical records query for large-scale ehealthcare systems

The advancements and adoption of cloud-assisted ehealthcare systems have enabled the storage of massive electronic medical records (EMRs) in the cloud for efficient and easy access. A direct benefit of EMRs is the ability of patients to search for EMRs that are similar to their own in the cloud for use as references. These similar EMRs can help a patient find appropriate medical services quickly. However, for large-scale ehealthcare systems, challenges remain with respect to ensuring the efficiency and privacy of these queries. In this study, we construct an efficient and privacy-preserving similar EMR query scheme to help patients find similar EMRs to reference in a large-scale ehealthcare system. Specifically, we propose a coarse-grained query method based on a binary decision tree to find a set of EMRs corresponding to the patient's set of medical-symptom keywords. We also design a fine-grained query method to find similar EMRs that meet the threshold set by the patient. A detailed security analysis shows that the proposed scheme is secure. The efficiency of the proposed method in a large-scale ehealthcare system is verified experimentally.

Automated summary

The advancements and adoption of cloud-assisted ehealthcare systems enable efficient and easy access to massive electronic medical records (EMRs) stored in the cloud. Patients can search for similar EMRs as references, which helps them find appropriate medical services quickly. However, ensuring the efficiency and privacy of queries remains a challenge in large-scale ehealthcare systems. This study proposes an efficient and privacy-preserving similar EMR query scheme to address this challenge and help patients find similar EMRs in a large-scale ehealthcare system.