Real-time Secure Health Surveillance for Smarter Health Communities

Pervasive healthcare services with smart decision making capability and ubiquitous communication technologies can forge future smart communities. Real-time health surveillance for early detection of life-threatening diseases through advanced sensing and communication technology can provide better treatment, reduce medical expenses and save lives of community residents (i.e., patients). However, the assurance of data privacy is the prime concern for such smart health technologies. This research aims to describe a privacy-preserving cloud-based system for real-time health surveillance through change detection of multiple vital health signs of smart community members. Vital signs data generated from IoT-enabled wearable devices are processed in real-time in a cloud environment. This article focuses on the development of a predictive model for the smart community considering the sensitivity of data processing in a third-party environment (e.g., cloud computing). We developed a vital sign change detection system using Holt's linear trend method (to enable prediction of data with trends) where fully homomorphic encryption is adapted to perform computations on an encrypted domain that can ensure data privacy. Moreover, to reduce the overhead of the fully homomorphic encryption method over large medical data we introduced a parallel approach for encrypted computations using a MapReduce algorithm of Apache Hadoop. We demonstrated the proposed model by evaluating some case studies for different vital signs of patients. The accuracy and efficiency of the implementation demonstrate the effectiveness of the proposed model for building a smart community.