A secure framework for mHealth data analytics with visualization

Document Type

Conference Proceeding

Publication Date

1-17-2017

Abstract

Mobile technology is changing the data collection and analytics in traditional healthcare practice. The distributed and real time nature of the operation brings security challenges in the gathering, processing, and analysis of personal biometrics data gathered by various wearable health monitoring devices. We present a security framework which identifies the anomalies not only based on the range of bio-metric parameters but also the history and the context. The values of the bio-metric parameters are used to construct the matrices to define the events. The matrices are de-noised using Random Matrix Theory. The correlation between different parameters is captured by the Pearson correlation. A canonical database, populated over time, of the vital signs of the patient and the values of the related bio-metric parameters through correlation network provide the history and context to detect anomalies. The security of the data collected in real time is very critical in establishing if an event is an anomaly. Our security framework ensures user authentication, confidentiality using encryption, confirms source device identity and packet level data validation. We provide a fully functional centralized visualization system to keep track of both patient and the doctors involved during any event of interest/ concern.

Identifier

85013499975 (Scopus)

ISBN

[9781509052523]

Publication Title

2016 IEEE 35th International Performance Computing and Communications Conference Ipccc 2016

External Full Text Location

https://doi.org/10.1109/PCCC.2016.7820602

This document is currently not available here.

Share

COinS