CREDO: Efficient and privacy-preserving multi-level medical pre-diagnosis based on ML-kNN

Abstract With the promotion of online medical pre-diagnosis system, more and more research has begun to pay attention to the issue of privacy, and existing privacy-preserving schemes are designed for single-label data. However, medical users may infect many different diseases at the same time, it is necessary to take multi-label instances into account. In this paper, we propose an efficient and privacy-preserving multi-level medical pre-diagnosis scheme, called CREDO, which based on multi-label k-nearest-neighbors (ML-kNN). With CREDO, medical users can ensure their sensitive health information secure, and service provider can provide high-efficiency service without revealing pre-diagnosis model data. Specifically, service provider first narrows down the scope of medical instances needed to be calculated based on k-means clustering, then provides service for medical users based on ML-kNN classification. The query vector is encrypted before being sent out and directly operated in the service provider, meanwhile, the pre-diagnosis result can only be achieved by the medical user. Through extensive analysis, we show that CREDO can resist multifarious known security threats, and has much lower computation complexity than the compared scheme. Moreover, performance evaluations based on a real medical dataset demonstrate that our proposed scheme is highly efficient in terms of computation and communication overhead.