Enabling privacy-preserving multi-level attribute based medical service recommendation in eHealthcare systems

Medical service recommendation is as an essential component of eHealthcare systems, and has received widespread attention in recent years. In medical systems, users can send demands to medical server, which then recommends the suitable doctors based on the demands. In the existing medical service recommendation scheme, although users can send the basic demands to get medical service recommendation, users cannot set the attributes of demands that are more concerned according to their own preferences or personalized demands, so as to get the accurate personalized medical service. In addition, due to the sensitivity of the users’ information, guaranteeing the privacy throughout the recommendation process without sacrificing the accuracy is still challenging. In this paper, we propose a privacy-preserving multi-level attribute based medical service recommendation scheme. This work considers multi-level attributes to fully describe users’ demand information, and users’ concerned attributes are considered to achieve personalized medical service recommendation. We design two algorithms to keep user’s demands secret, and recommend doctors in a privacy-preserving way. Detailed analysis proves that the proposed scheme can achieve the desired security prosperities. Performance evaluations through extensive experiments also demonstrate the efficiency of our scheme.