# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 4 Smart City De-Anonymization and Re-Identification Use Cases
### WiFi Probes  
WiFi is the most used communication protocol in the IoT. The article written by [115] aims to study behavioral aspects of cell phones with regard to the transmission of probe requests and the exposure of users' privacy. Researchers in a previous work [116] have identified privacy risks associated with WiFi probe requests, such as leaking service set identifiers (SSIDs) already connected by users. Despite several efforts to develop privacy-preserving alternatives, modern mobile devices continue to expose the SSIDs already used by users during WiFi probe requests. In the work in question, the threats of WiFi probe requests to privacy are quantified, carrying out an experimental study of the most popular smartphones in different configurations. The authors' objective is to identify how different factors influence the frequency of probes and the average number of probes transmitted. The findings are worrying: on average, some mobile devices send probe requests at a frequency of 55 times per hour, thus revealing their unique MAC address at a high frequency. When a mobile device is not charging the battery and is also not in sleep mode, it can transmit up to 2000 probes per hour. The work of [117] presents the idea of capturing and reading WiFi probe request frames to de-anonymize the origin of participants in large events. The authors collected around 11 million records of probe request boards captured in events of different levels of relevance held in Italy. When transmitting probe requests, in many cases, cell phones end up citing in their request the SSID of a network already connected to by the user of the cell device. These networks are maintained by the cell phone in a structure known as the Preferred Network List, or NLP. Some cellular devices even send several requests in a row exposing several networks already connected to by the cellular device user. Capturing such information, it is possible to know which networks users present at a given event usually connect to. By itself, this information would be enough to classify the crowd of people into groups of individuals who frequent the same places. However, the authors also proposed to cross-reference this data with a public database, which maps the geographic location of known wireless networks. An example of a public database with the function of storing information about wireless networks on a global scale is Wigle.net. The mapping of these networks is done cooperatively by voluntary users, who install the application on their cell phones and activate the scanning of wireless networks with the respective upload of the data to the Wigle.net database. Therefore, knowing the MAC addresses of the cellular devices that generated the probe requests; knowing the wireless networks that such users frequently connect to; and knowing the geographic location of such networks; it is then possible to know the origin of the people who are present at a particular public event. The experiments conducted by the authors showed that it is possible to explore the semantic information brought by probe requests, to discover with high precision the provenance of the crowds in each event. An example is the de-anonymization result of two political meetings held a few days before the elections in Italy, which surprisingly coincide with the reported official voting results. In [118], the authors propose the use of passive monitors to monitor WiFi evidence in a museum to extract information about the behavior of its visitors. More than 1.7 million probes were collected during the six months of capture. The authors obtain promising results regarding the visitors' trajectory, knowing some behavior patterns of their visitors. In [119], a mechanism is presented to detect the social interaction of people, through evidence left in the wireless environment by WiFi networks. For this, they use probe requests to detect devices not associated with a network, and null data frames to detect those that are associated.