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dc.contributor.authorAlorwu, A
dc.contributor.authorvan Berkel, N
dc.contributor.authorGoncalves, J
dc.contributor.authorOppenlaender, J
dc.contributor.authorLopez, MB
dc.contributor.authorSeetharaman, M
dc.contributor.authorHosio, S
dc.date.accessioned2020-11-26T22:53:19Z
dc.date.available2020-11-26T22:53:19Z
dc.date.issued2020-03-20
dc.identifier.citationAlorwu, A., van Berkel, N., Goncalves, J., Oppenlaender, J., Lopez, M. B., Seetharaman, M. & Hosio, S. (2020). Crowdsourcing sensitive data using public displays-opportunities, challenges, and considerations. Personal and Ubiquitous Computing, https://doi.org/10.1007/s00779-020-01375-6.
dc.identifier.issn1617-4909
dc.identifier.urihttp://hdl.handle.net/11343/252029
dc.description.abstractInteractive 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.
dc.languageEnglish
dc.publisherSpringer
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleCrowdsourcing sensitive data using public displays-opportunities, challenges, and considerations
dc.typeJournal Article
dc.identifier.doi10.1007/s00779-020-01375-6
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titlePersonal and Ubiquitous Computing
dc.rights.licensecc-by
melbourne.elementsid1444338
melbourne.contributor.authorGoncalves, Jorge
dc.identifier.eissn1617-4917
melbourne.accessrightsOpen Access


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