Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Abstract

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices. The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing healthcare systems.