  The exposure of location data constitutes a significant privacy risk to users
as it can lead to de-anonymization, the inference of sensitive information, and
even physical threats. In this paper we present LPAuditor, a tool that conducts
a comprehensive evaluation of the privacy loss caused by publicly available
location metadata. First, we demonstrate how our system can pinpoint users' key
locations at an unprecedented granularity by identifying their actual postal
addresses. Our experimental evaluation on Twitter data highlights the
effectiveness of our techniques which outperform prior approaches by
18.9%-91.6% for homes and 8.7%-21.8% for workplaces. Next we present a novel
exploration of automated private information inference that uncovers
"sensitive" locations that users have visited (pertaining to health, religion,
and sex/nightlife). We find that location metadata can provide additional
context to tweets and thus lead to the exposure of private information that
might not match the users' intentions.
  We further explore the mismatch between user actions and information exposure
and find that older versions of the official Twitter apps follow a
privacy-invasive policy of including precise GPS coordinates in the metadata of
tweets that users have geotagged at a coarse-grained level (e.g., city). The
implications of this exposure are further exacerbated by our finding that users
are considerably privacy-cautious in regards to exposing precise location data.
When users can explicitly select what location data is published, there is a
94.6% reduction in tweets with GPS coordinates. As part of current efforts to
give users more control over their data, LPAuditor can be adopted by major
services and offered as an auditing tool that informs users about sensitive
information they (indirectly) expose through location metadata.
