Abstract
The prevalence of social network sites and smartphones has led to many
people sharing their locations with others. Privacy concerns are
seldom addressed by these services; the default privacy settings may
be either too restrictive or too lax, resulting in under-exposure or
over-exposure of location information.
One mechanism for alleviating over-sharing is through personalised privacy
settings that automatically change according to users' predicted preferences.
In this paper, we use data collected from a location-sharing user study
($N=80$) to investigate whether users' willingness to share their
locations can be predicted. We find that while default settings match actual
users' preferences only $68\%$ of the time, machine-learning classifiers can
predict up to $85\%$ of users' preferences. Using these predictions instead of
default settings would reduce the over-exposed location information by $40\%$.
people sharing their locations with others. Privacy concerns are
seldom addressed by these services; the default privacy settings may
be either too restrictive or too lax, resulting in under-exposure or
over-exposure of location information.
One mechanism for alleviating over-sharing is through personalised privacy
settings that automatically change according to users' predicted preferences.
In this paper, we use data collected from a location-sharing user study
($N=80$) to investigate whether users' willingness to share their
locations can be predicted. We find that while default settings match actual
users' preferences only $68\%$ of the time, machine-learning classifiers can
predict up to $85\%$ of users' preferences. Using these predictions instead of
default settings would reduce the over-exposed location information by $40\%$.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the First Workshop on recent advances in behavior prediction and pro-active pervasive computing (AwareCast) |
| Publication status | Published - 19 Jun 2012 |