沃利：焦虑地分享你的位置会让你离开
There goes Wally: Anonymously sharing your location gives you away
摘要：
利用当前技术，许多实体可以访问不同时空粒度级别的用户移动性轨迹。与此同时，用户经常通过不同的方式透露他们的位置，包括地理标记的社交媒体帖子和移动应用程序的使用。这种泄露通常与假名或假身份绑定，以保护个人隐私。在这项工作中，我们研究如何大规模的移动跟踪可以去匿名匿名的位置泄漏。通过挖掘全国范围内数千万用户的移动轨迹，我们的目标是了解需要多少位置泄漏才能唯一匹配一个轨迹，时空混淆如何降低匹配质量，以及泄漏的位置流行度和时间如何影响去匿名化。我们还研究了那些匿名泄漏更容易识别的个人的流动性特征。最后，通过将我们的匹配方法扩展到完整的痕迹，我们展示了大规模的人类流动性是多么的独特。我们的定量结果对用户痕迹的隐私有影响，并可作为未来移动数据管理和发布政策的指导方针。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
