University of Oulu

Alorwu, A., van Berkel, N., Goncalves, J. et al. Crowdsourcing sensitive data using public displays—opportunities, challenges, and considerations. Pers Ubiquit Comput (2020). https://doi.org/10.1007/s00779-020-01375-6

Crowdsourcing sensitive data using public displays : opportunities, challenges, and considerations

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Author: Alorwu, Andy1; van Berkel, Niels2; Goncalves, Jorge3;
Organizations: 1University of Oulu, Oulu, Finland
2Aalborg University, Aalborg, Denmark
3The University of Melbourne, Melbourne, Australia
4VTT Technical Research Centre of Finland, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022030722198
Language: English
Published: Springer Nature, 2020
Publish Date: 2022-03-07
Description:

Abstract

Interactive public displays are versatile two-way interfaces between the digital world and passersby. They can convey information and harvest purposeful data from their users. Surprisingly little work has exploited public displays for collecting tagged data that might be useful beyond a single application. In this work, we set to fill this gap and present two studies: (1) a field study where we investigated collecting biometrically tagged video-selfies using public kiosk-sized screens, and (2) an online narrative transportation study that further elicited rich qualitative insights on key emerging aspects from the first study. In the first study, a 61-day deployment resulted in 199 video-selfies with consent to leverage the videos in any non-profit research. The field study indicates that people are willing to donate even highly sensitive data about themselves in public. The subsequent online narrative transportation study provides a deeper understanding of a variety of issues arising from the first study that can be leveraged in the future design of such systems. The two studies combined in this article pave the way forward towards a vision where volunteers can, should they so choose, ethically and serendipitously help unleash advances in data-driven areas such as computer vision and machine learning in health care.

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Series: Personal and ubiquitous computing
ISSN: 1617-4909
ISSN-E: 1617-4917
ISSN-L: 1617-4909
Volume: In press
DOI: 10.1007/s00779-020-01375-6
OADOI: https://oadoi.org/10.1007/s00779-020-01375-6
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: Open access funding provided by University of Oulu including Oulu University Hospital. Part of the work was carried out with the support of Biocenter Oulu institute, the ICON spearhead project, and the GenZ profiling theme, funded by the Academy of Finland, both at the University of Oulu.
Copyright information: © 2020, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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