Abstract
Continuous health monitoring system (CHMS) are a collection of networked sensing devices that continuously monitor a user who is carrying them. The sensors can be worn by the user (e.g., fitbit or jawbone) or be part of a device that user carries (e.g., smartphones). Trustworthy operation is essential for CHMS due to the sensitive nature of the information they collect and the wireless transmission of the data to a sink/basestation entity for transport to a medical cloud for long term storage in a patient health record (PHR). In this regard, in the past we have proposed a scheme known as Physiological signal-based Key Agreement (PKA) to enable plug-n-play (i.e., transparent to the user in terms of configuration or setup) information security between wearable sensors that had access to same physiological signals (e.g., ECG, PPG). In this paper, we present Physiology-based System-wide Information Security (PySIS), which uses the concept of generative models (which generate synthetic physiological signals for a user) to extend PKA to enable end-To-end information security in CHMS from the sensors to the PHR. The crucial difference is that now we do not need to have access to the same physiological signals at both ends for our protocol to work. In addition, if PySIS fails and data leakage occurs in the system, we also propose a logging mechanism to perform forensic analysis of the system.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538604342 |
| DOIs | |
| State | Published - Jun 26 2018 |
| Event | 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - San Francisco, United States Duration: Apr 4 2017 → Apr 8 2017 |
Other
| Other | 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 |
|---|---|
| Country | United States |
| City | San Francisco |
| Period | 4/4/17 → 4/8/17 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Information Systems and Management
- Energy Engineering and Power Technology
- Safety, Risk, Reliability and Quality
- Urban Studies
Cite this
A cyber-physical approach to trustworthy operation of health monitoring systems. / Venkatasubramanian, Krishna K.; Banerjee, Ayan; Gupta, Sandeep; Walls, Robert J.
2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A cyber-physical approach to trustworthy operation of health monitoring systems
AU - Venkatasubramanian, Krishna K.
AU - Banerjee, Ayan
AU - Gupta, Sandeep
AU - Walls, Robert J.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Continuous health monitoring system (CHMS) are a collection of networked sensing devices that continuously monitor a user who is carrying them. The sensors can be worn by the user (e.g., fitbit or jawbone) or be part of a device that user carries (e.g., smartphones). Trustworthy operation is essential for CHMS due to the sensitive nature of the information they collect and the wireless transmission of the data to a sink/basestation entity for transport to a medical cloud for long term storage in a patient health record (PHR). In this regard, in the past we have proposed a scheme known as Physiological signal-based Key Agreement (PKA) to enable plug-n-play (i.e., transparent to the user in terms of configuration or setup) information security between wearable sensors that had access to same physiological signals (e.g., ECG, PPG). In this paper, we present Physiology-based System-wide Information Security (PySIS), which uses the concept of generative models (which generate synthetic physiological signals for a user) to extend PKA to enable end-To-end information security in CHMS from the sensors to the PHR. The crucial difference is that now we do not need to have access to the same physiological signals at both ends for our protocol to work. In addition, if PySIS fails and data leakage occurs in the system, we also propose a logging mechanism to perform forensic analysis of the system.
AB - Continuous health monitoring system (CHMS) are a collection of networked sensing devices that continuously monitor a user who is carrying them. The sensors can be worn by the user (e.g., fitbit or jawbone) or be part of a device that user carries (e.g., smartphones). Trustworthy operation is essential for CHMS due to the sensitive nature of the information they collect and the wireless transmission of the data to a sink/basestation entity for transport to a medical cloud for long term storage in a patient health record (PHR). In this regard, in the past we have proposed a scheme known as Physiological signal-based Key Agreement (PKA) to enable plug-n-play (i.e., transparent to the user in terms of configuration or setup) information security between wearable sensors that had access to same physiological signals (e.g., ECG, PPG). In this paper, we present Physiology-based System-wide Information Security (PySIS), which uses the concept of generative models (which generate synthetic physiological signals for a user) to extend PKA to enable end-To-end information security in CHMS from the sensors to the PHR. The crucial difference is that now we do not need to have access to the same physiological signals at both ends for our protocol to work. In addition, if PySIS fails and data leakage occurs in the system, we also propose a logging mechanism to perform forensic analysis of the system.
UR - http://www.scopus.com/inward/record.url?scp=85050224990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050224990&partnerID=8YFLogxK
U2 - 10.1109/UIC-ATC.2017.8397609
DO - 10.1109/UIC-ATC.2017.8397609
M3 - Conference contribution
AN - SCOPUS:85050224990
SP - 1
EP - 6
BT - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -