Predicting location-sharing privacy preferences in social network applications

Gregory Bigwood, Fehmi Ben Abdesslem, Tristan Henderson

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 $40\%$.
Original languageEnglish
Title of host publicationProceedings of the First Workshop on recent advances in behavior prediction and pro-active pervasive computing (AwareCast)
Publication statusPublished - 19 Jun 2012


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