  With current technology, a number of entities have access to user mobility
traces at different levels of spatio-temporal granularity. At the same time,
users frequently reveal their location through different means, including
geo-tagged social media posts and mobile app usage. Such leaks are often bound
to a pseudonym or a fake identity in an attempt to preserve one's privacy. In
this work, we investigate how large-scale mobility traces can de-anonymize
anonymous location leaks. By mining the country-wide mobility traces of tens of
millions of users, we aim to understand how many location leaks are required to
uniquely match a trace, how spatio-temporal obfuscation decreases the matching
quality, and how the location popularity and time of the leak influence
de-anonymization. We also study the mobility characteristics of those
individuals whose anonymous leaks are more prone to identification. Finally, by
extending our matching methodology to full traces, we show how large-scale
human mobility is highly unique. Our quantitative results have implications for
the privacy of users' traces, and may serve as a guideline for future policies
regarding the management and publication of mobility data.
