URLSight: Profiling Mobile Users via Large-scale Internet Metadata Analytics

In the era of the mobile Internet, the problem of personal information leakage is becoming more and more serious. It is possible that third-parities (e.g., advertising companies, telecom operators) can easily obtain private user information by analyzing the Internet traffic flows to (or from) users. In this paper, in order to examine the degree of information leakage via the URLs, we propose an efficient Internet metadata analytics framework called URLSight, which attempts to profile user behaviors by simply analyzing the Internet metadata embedded in the URLs. Our analysis is based on a large dataset which contains one-month URL logs generated by around 100 thousand mobile users. After parsing and contextualizing URLs, URLSight can extract all the key-value pairs and perform co-occurrence processing to eliminate information redundancy. We also implement the URLSight framework on the Apache Spark platform to improve its performance. The results show that URLSight can effectively extract user privacy from URLs. Last, we also discuss a few practical approaches to defend against URL information leakage.