Reliable and Privacy-Preserving Top-k Disease Matching Schemes for E-Healthcare Systems

The integration of body sensors, cloud computing, and mobile communication technologies has significantly improved the development and availability of e-healthcare systems. In an e-healthcare system, health service providers upload real patients' clinical data and diagnostic treatments to the cloud server. Afterward, the users can submit queries with specific body sensor parameters, to obtaining pertinent k diagnostic files. The results are ranked based on ranking algorithms that match the query parameters to the ones in diagnostic files. However, privacy concerns arise while matching disease, since the clinical data and diagnostic files contain sensitive information. In this work, we propose two reliable and privacy-preserving Top-k disease matching schemes. The first scheme is constructed based on our proposed weighted Euclidean distance comparison algorithm under secure k-nearest neighbor technique to get k diagnostic files. It allows users to set different weights for each body indicator as per their needs. The second scheme is designed by comparing Euclidean distances under the modified Paillier homomorphic encryption algorithm where a superlinear sequence is used to reduce the computational and communication overhead. The user side incurs slightly higher computational costs, but the trusted party does not need to execute encryption operations. Hence, the proposed two schemes can be applied in different application scenarios. Simulations on synthetic and real data prove the efficiency of the schemes, and security analysis establishes the privacy-preservation properties.