Are Those Steps Worth Your Privacy? Fitness-Tracker Users' Perceptions of Privacy and Utility

Détails

Ressource 1Télécharger: Velykoivanenko2021IMWUT.pdf (7147.60 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY-ND 4.0
ID Serval
serval:BIB_117985B0DD0D
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Are Those Steps Worth Your Privacy? Fitness-Tracker Users' Perceptions of Privacy and Utility
Périodique
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Auteur(s)
Velykoivanenko Lev, Salehzadeh Niksirat Kavous, Zufferey Noé, Humbert Mathias, Huguenin Kévin, Cherubini Mauro
ISSN
2474-9567 (electronic)
Statut éditorial
Publié
Date de publication
12/2021
Peer-reviewed
Oui
Volume
5
Numéro
4
Pages
181:1-181:41
Langue
anglais
Résumé
Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users’ knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users’ mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants’ lack of understanding and common misconceptions about how the Fitbit ecosystem works.
Données de la recherche
Open Access
Oui
APC
700 USD
Financement(s)
Fonds national suisse / Projets / 200021_178978
Autre / CYD-C-2020007
Création de la notice
01/11/2021 21:28
Dernière modification de la notice
03/11/2021 7:08
Données d'usage