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Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications

Open access
Al Ameen, Moshaddique, Liu, Jingwei, Kwak, Kyungsup·2012·Journal of Medical Systems

Abstract

The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.

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Metadata

Authors
Al Ameen, M., Liu, J., & Kwak, K.
Venue
Journal of Medical Systems
Published
2010-03-12
Volume
36
Issue
1
Pages
93-101
ISSN
  • 0148-5598 (ISSN-L)
  • 1573-689X
OpenAlex
W2088588705
Cited by
651
References
0
FWCI
10.38
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications. https://app.cochat.ai/papers/10.1007/s10916-010-9449-4
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Al Ameen, M., Liu, J., & Kwak, K. (2012). Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications. Journal of Medical Systems. https://doi.org/10.1007/s10916-010-9449-4

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.