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Using Freenet as a Broker for Multi-Party Data Exchange in IoT for Health

Using Freenet as a Broker for Multi-Party Data Exchange in IoT for Health

Emmanuel Benoist (Bern University of Applied Sciences, Switzerland) and Jan Sliwa (Bern University of Applied Sciences, Switzerland)
Copyright: © 2020 |Pages: 13
DOI: 10.4018/978-1-7998-2444-2.ch009
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Abstract

Smart, networked medical devices play a rapidly growing role in healthcare. Those devices and their data have to be integrated into the healthcare system. There are several reasons to reuse those data for well-defined purposes by well-defined partners; this reuse should be controlled by the patient and not depend on the manufacturer infrastructure. Different stakeholders have an understandable reason to access those data under the control of the patient. The authors propose an architecture of a decentralized data broker that receives the data streams from the devices and redistributes them securely to legitimate recipients. This broker is based on the peer-to-peer network Freenet. This network has been defined to be censorship resistant and to protect the privacy of persons sharing data. This covers the needs for protection expected from a secure data broker. The patient can directly define which of the stakeholders will receive which information and the information is encrypted in a way that only that partner can read it.
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An overview of the Internet of Things can be found in (Al-Fuqaha et al. 2015), with the focus on the middleware (Razzaque et al. 2015). An overview of IoT systems for healthcare is given in (Islam et al. 2015), (Yuehong et al. 2016) and (N. Zhu et al. 2015). The privacy and security issues are discussed in (Arias et al. 2015), (Moosavi et al. 2016), (Roman, Zhou, and Lopez 2013), (Singh et al. 2015) and (Weber 2010). An ethical view on the fair sharing of information is given in (Shilton et al. 2009). Possible architecture solutions are discussed in (Catarinucci et al. 2015), (M. S. Hossain and Muhammad 2016), (Paschou et al. 2013) and (T. Zhu et al. 2017). Stream processing and event detection is presented in (Ma et al. 2014).

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