Considering the ubiquity of WiFi enabled devices and user’s mobility, location privacy within WiFi networks is one of the focus points of many researchers. Preferred Network Lists (PNL), within which devices store a list of names of previously used hotspots - Service Set Identifiers (SSIDs), is transmitted in clear by a portion of devices as a part of WiFi connection protocol. PNL has proven to be one exceptionally interesting source of private data on user’s previous whereabouts. However, since the PNL datasets are anonymized, most of the available work focuses on groups of users as opposed to one particular user.

In this paper we work towards finding the name of the person behind the device’s PNL. We introduce a novel SSID - location tag matching function, followed by an algorithm used for intersecting large PNL datasets with localization tags on Instagram social network. The algorithm enables us to match the user’s MAC address and PNL with his full name, photos and activities. We find that deanonymization of a MAC address provides serious implications for potential long term tracking. We tested our work in real life conditions on a large scale music festival. To approach the ground truth we conducted hand check tests performed by 10 testers who concluded that more than 50% of the proposed matches were correct.

考虑到支持WiFi的设备的普遍存在和用户的移动性，WiFi网络中的位置隐私是许多研究人员的重点之一。设备在其中存储了以前使用的热点的名称列表-服务集标识符（SSID）的首选网络列表（PNL）由一部分设备作为WiFi连接协议的一部分以明文方式传输。PNL已被证明是关于用户先前下落的私人数据的一个非常有趣的来源。但是，由于PNL数据集是匿名的，因此大多数可用工作集中于一组用户，而不是一个特定用户。

在本文中，我们努力寻找设备PNL背后的人的名字。我们介绍了一种新颖的SSID-位置标签匹配功能，然后介绍了一种算法，该算法用于在Instagram社交网络上将大型PNL数据集与本地化标签相交。该算法使我们能够将用户的MAC地址和PNL与他的全名，照片和活动进行匹配。我们发现，MAC地址的去匿名化对潜在的长期跟踪提供了严重的含义。我们在大型音乐节上的真实生活条件下测试了我们的作品。为了接近地面真相，我们进行了由10名测试人员进行的手工检查测试，这些测试人员得出结论，提议的比赛中有50％以上是正确的。