A secure framework for mHealth data analytics with visualization

Denise Ferebee; Vivek Shandilya; Chase Wu; Janet Ricks; David Agular; Karyn Cole; Byron Ray; Aukii Franklin; Candice Titon; Zongmin Wang

doi:10.1109/PCCC.2016.7820602

A secure framework for mHealth data analytics with visualization

Denise Ferebee, Vivek Shandilya, Chase Wu, Janet Ricks, David Agular, Karyn Cole, Byron Ray, Aukii Franklin, Candice Titon, Zongmin Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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.

Original language	English (US)
Title of host publication	2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509052523
DOIs	https://doi.org/10.1109/PCCC.2016.7820602
State	Published - Jan 17 2017
Event	35th IEEE International Performance Computing and Communications Conference, IPCCC 2016 - Las Vegas, United States Duration: Dec 9 2016 → Dec 11 2016

Publication series

Name	2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016

Other

Other	35th IEEE International Performance Computing and Communications Conference, IPCCC 2016
Country/Territory	United States
City	Las Vegas
Period	12/9/16 → 12/11/16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Keywords

Arduino
biometrics
correlation
cyber security
heart rhythm

Access to Document

10.1109/PCCC.2016.7820602

Cite this

Ferebee, D., Shandilya, V., Wu, C., Ricks, J., Agular, D., Cole, K., Ray, B., Franklin, A., Titon, C., & Wang, Z. (2017). A secure framework for mHealth data analytics with visualization. In 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016 Article 7820602 (2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PCCC.2016.7820602

Ferebee, Denise ; Shandilya, Vivek ; Wu, Chase et al. / A secure framework for mHealth data analytics with visualization. 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016).

@inproceedings{dee4d638ea8547098e7cfe5d30a2d3ae,

title = "A secure framework for mHealth data analytics with visualization",

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.",

keywords = "Arduino, biometrics, correlation, cyber security, heart rhythm",

author = "Denise Ferebee and Vivek Shandilya and Chase Wu and Janet Ricks and David Agular and Karyn Cole and Byron Ray and Aukii Franklin and Candice Titon and Zongmin Wang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 35th IEEE International Performance Computing and Communications Conference, IPCCC 2016 ; Conference date: 09-12-2016 Through 11-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/PCCC.2016.7820602",

language = "English (US)",

series = "2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016",

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Ferebee, D, Shandilya, V, Wu, C, Ricks, J, Agular, D, Cole, K, Ray, B, Franklin, A, Titon, C & Wang, Z 2017, A secure framework for mHealth data analytics with visualization. in 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016., 7820602, 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016, Institute of Electrical and Electronics Engineers Inc., 35th IEEE International Performance Computing and Communications Conference, IPCCC 2016, Las Vegas, United States, 12/9/16. https://doi.org/10.1109/PCCC.2016.7820602

A secure framework for mHealth data analytics with visualization. / Ferebee, Denise; Shandilya, Vivek; Wu, Chase et al.
2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820602 (2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Ferebee, Denise

AU - Shandilya, Vivek

AU - Wu, Chase

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AU - Agular, David

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AU - Ray, Byron

AU - Franklin, Aukii

AU - Titon, Candice

AU - Wang, Zongmin

PY - 2017/1/17

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N2 - 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.

AB - 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.

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KW - heart rhythm

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T3 - 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016

BT - 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 35th IEEE International Performance Computing and Communications Conference, IPCCC 2016

Y2 - 9 December 2016 through 11 December 2016

ER -

Ferebee D, Shandilya V, Wu C, Ricks J, Agular D, Cole K et al. A secure framework for mHealth data analytics with visualization. In 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820602. (2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016). doi: 10.1109/PCCC.2016.7820602

A secure framework for mHealth data analytics with visualization

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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