CAMPS: Efficient and privacy-preserving medical primary diagnosis over outsourced cloud

Abstract With the flourishing of ubiquitous healthcare and cloud computing technologies, medical primary diagnosis system, which forms a critical capability to link big data analysis technologies with medical knowledge, has shown great potential in improving the quality of healthcare services. However, it still faces many severe challenges on both users’ medical privacy and intellectual property of healthcare service providers, which deters the wide adoption of medical primary diagnosis system. In this paper, we propose an effi c ient and priv a cy-preserving m edical p rimary diagno s is framework (CAMPS). Within CAMPS framework, the precise diagnosis models are outsourced to the cloud server in an encrypted manner, and users can access accurate medical primary diagnosis service timely without divulging their medical data. Specifically, based on partially decryption and secure comparison techniques, a special fast secure two-party vector dominance scheme over ciphertext is proposed, with which CAMPS achieves privacy preservation of user’s query and the diagnosis result, as well as the confidentiality of diagnosis models in the outsourced cloud server. Through extensive analysis, we show that CAMPS can ensure that users’ medical data and healthcare service provider’s diagnosis model are kept confidential, and has significantly reduce computation and communication overhead. In addition, performance evaluations via implementing CAMPS demonstrate its effectiveness in term of the real environment.