无线网络用户的Wi-Fi指纹匿名化研究

Study on Wi-Fi Fingerprint Anonymization for Users in Wireless Networks

如今,上亿的Wi-Fi热点被广泛部署,用于给人们提供Wi-Fi连网服务。为了加快Wi-Fi连接的速度,移动设备会发送探测请求帧来发现附近的无线热点,并且保存曾经连接过的AP的SSID,即首选网络列表(PNL)。已有研究表明,由探测请求帧发出的SSID构成的Wi-Fi指纹会泄露用户的隐私信息。基于对现实情况中Wi-Fi指纹所造成的隐私泄露程度的分析,提出了数据驱动的隐私保护方案。首先,针对4个城市中2700万用户连接400万Wi-Fi热点的行为进行了测量研究,并证明了在很多场景下Wi-Fi指纹都可以用来区分用户。基于对Wi-Fi指纹中SSID语义信息的研究,可以推断出这些用户的身份信息(如工作信息)。其次,提出了一种基于协同过滤的启发式方法,它通过给用户的PNL中添加伪SSID来模糊其信息,并使得附近的人彼此之间的PNL与Wi-Fi指纹都更加相似。最后,基于真实的Wi-Fi连接数据验证了上述策略的有效性,实验结果表明,修改PNL不仅能保护用户隐私,而且能保证快速的Wi-Fi连接。

Billions of Wi-Fi assess points（APs）have been deployed to provide wireless connection to people with different kinds of mobile devices.To accelerate the speed of Wi-Fi connection,mobile devices will send probe requests to discover nearby Wi-Fi APs,and maintain previously connected network lists（PNLs）of APs.Previous studies show that the Wi-Fi fingerprints that consist of probed SSIDs individually will leak private information of users.This paper investigated the privacy caused by the Wi-Fi fingerprints in the wild,and provided a data-driven solution to protect privacy.First,measurement studies were carried out based on 27 million users associating with 4 million Wi-Fi APs in 4 cities,and it was revealed that Wi-Fi fingerprints can be used to identify users in a wide range of Wi-Fi scenarios.Based on semantic mining and analysis of SSIDs in Wi-Fi fingerprints,this paper further inferred demographic information of identified users（e.g.,people＇s jobs）,telling＂who they are＂.Second,this paper proposed a collaborative filtering（CF）based heuristic protection method,which can＂blur＂an user＇s PNL by adding faked SSIDs,such that nearby users＇ PNLs and Wi-Fi fingerprints are similar to each other.Finally,the effectiveness of the design was verified by using real-world Wi-Fi connection traces.The experiments show that the refined PNLs protect users＇ privacy while still provide fast Wi-Fi reconnection.