Tuovinen L, Smeaton AF (2022) Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial. PLoS ONE 17(4): e0265997. https://doi.org/10.1371/journal.pone.0265997
Privacy-aware sharing and collaborative analysis of personal wellness data : process model, domain ontology, software system and user trial
|Author:||Tuovinen, Lauri1; Smeaton, Alan F.2|
1Biomimetics and Intelligent Systems Group, University of Oulu, Oulu, Finland
2Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin, Ireland
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022042530182
Public Library of Science,
|Publish Date:|| 2022-04-25
Personal wellness data collected using wearable devices is a valuable resource, potentially containing knowledge that goes beyond what the device and its the associated software application can tell the user. However, extracting such knowledge from the data requires expertise that an average user cannot be expected to have. To overcome this problem, the data owner could collaborate with a data analysis expert; for such a collaboration to succeed, the collaborators need to be able to find one another, communicate with one another and share datasets and analysis results with one another. In this paper we presents a process model for such collaborations, a domain ontology and software system developed to support the process, and the results of a user trial demonstrating collaborative analysis of sleep data. Unlike existing collaborative data analytics tools, the process and software have been specifically designed with the non-expert data owner in mind, enabling them to control their data and protect their privacy by selecting the data to be shared on a case-by-case basis. Theoretical analysis and empirical results suggest that the process and its implementation are valid as a proof of concept.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
LT was funded by the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie actions, grant agreement number 746837. http://ec.europa.eu/research/mariecurieactions/ AS is partly-supported by Science Foundation Ireland (SFI), grant number SFI/12/RC/2289\_P2, co-funded by the European Regional Development Fund. https://www.sfi.ie/ https://ec.europa.eu/regional_policy/en/funding/erdf/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2022 Tuovinen, Smeaton. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.