User association logs collected from a large-scale wireless LAN record where and when a user has used the network. Such information plays an important role in wireless network research. One concern of sharing these data with other researchers, however, is that the logs pose potential privacy risks for the network users. Today, the common practice in sanitizing these data before releasing them to the public is to anonymize users’ sensitive information, such as their devices’ MAC addresses and their exact association locations. In this work, we aim to study whether such sanitization measures are sufficient to protect user privacy. By simulating an adversary’s role, we propose a novel type of correlation attack in which the adversary uses the anonymized association log to build signatures against each user, and when combined with auxiliary information, such signatures can help to identify users within the anonymized log. Using a user association log that contains more than four tho...