EPSMD: An Efficient Privacy-Preserving Sensor Data Monitoring and Online Diagnosis System

Xiangyu Wang, Jianfeng Ma, Yinbin Miao, Ruikang Yang, Yijia Chang

Abstract: With the development of Mobile Healthcare Monitoring Network (MHMN), patients' personal data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enable clinicians to make proper decisions by utilizing data mining technique. However, the sensitive data privacy is still a major concern. In this paper, we first propose an Efficient Privacy-preserving Sensor data Monitoring and online Diagnosis (EPSMD) system for outsourced computing, then furnish an improved Multidimensional Range Query Technique (MRQT) to gain a broad range of applications in practice. In addition, a privacy-preserving naive Bayesian classifier based on MRQT is designed to protect patients' data in data mining and online diagnosis efficiently. Security analysis proves that patients' data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in data monitoring and disease pre-diagnosis, respectively.