There goes Wally: Anonymously sharing your location gives you away
This addresses privacy risks for users who share location data anonymously, revealing vulnerabilities in current practices, though it is incremental in building on existing de-anonymization research.
The study investigated how large-scale mobility traces can de-anonymize anonymous location leaks, finding that human mobility is highly unique and quantifying the conditions under which identification occurs, such as the number of leaks required and the impact of spatio-temporal obfuscation.
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.