A Literature Survey on Smart Toy-related Children's Privacy Risks

Simple item page
dc.contributor.authorFantinato, Marcelo
dc.contributor.authorAlbuquerque, Otávio De Paula
dc.contributor.authorDe Albuquerque, Anna Priscilla
dc.contributor.authorKelner, Judith
dc.contributor.authorYankson, Benjamin
dc.date.accessioned2020-01-04T07:27:09Z
dc.date.available2020-01-04T07:27:09Z
dc.date.issued2020-01-07
dc.description.abstractSmart toys have become popular as technological solutions offer a better experience for children. However, the technology used increases the risks to children's privacy, which does not seem to have become a real concern for toy makers. Most researchers in this domain are vague in defining their motivations due to lack of an expert survey to support them. We conducted a literature survey to find papers on smart toy-related children's privacy risks and mitigation solutions. We analyzed 26 papers using a taxonomy for privacy principles and preserving techniques adapted from the IoT context. Our analysis shows that some types of risks received more attention, especially (a) confidentiality, (b) use, retention and disclosure limitation, (c) authorization, (d) consent and choice, (e) openness, transparency and notice and (f) authentication. As for solutions, few were effectively presented; the vast majority related to data restriction -- (a) access control and (b) cryptographic.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.182
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63921
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning, Robotic, and Toy Computing
dc.subjectliterature review
dc.subjectliterature survey
dc.subjectprivacy
dc.subjectsecurity
dc.subjectsmart toys
dc.titleA Literature Survey on Smart Toy-related Children's Privacy Risks
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0147.pdf
Size:
287.59 KB
Format:
Adobe Portable Document Format
Download

Collections

Machine Learning, Robotic, and Toy Computing