Abstract
In this work, we consider ubiquitous health data generated from wearable sensors in a Ubiquitous Health Monitoring System (UHMS) and examine how these data can be used within privacy- preserving distributed statistical analysis. To this end, we propose a secure multi-party computation based on a privacy-preserving cryptographic protocol that accepts as input current or archived values of users’ wearable sensors. We describe a prototype implementation of the proposed solution with a community of independent personal agents and present preliminary results that confirm the viability of the approach.
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Drosatos, G., Efraimidis, P.S. (2011). Privacy-Preserving Statistical Analysis on Ubiquitous Health Data. In: Furnell, S., Lambrinoudakis, C., Pernul, G. (eds) Trust, Privacy and Security in Digital Business. TrustBus 2011. Lecture Notes in Computer Science, vol 6863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22890-2_3
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DOI: https://doi.org/10.1007/978-3-642-22890-2_3
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