As a result of the GDPR and the ePrivacy Directive, European users encounter cookie banners on almost every website. Many of such banners are implemented by Consent Management Providers (CMPs), who respect IAB Europe's Transparency and Consent Framework (TCF). Via cookie banners, CMPs collect and disseminate user consent to third parties. In this work, we systematically study IAB Europe's TCF and analyze consent stored behind the user interface of TCF cookie banners. We analyze the GDPR and the ePrivacy Directive to identify potential legal violations in implementations of cookie banners based on the storage of consent and detect such suspected violations by crawling 1 426 websites that contains TCF banners, found among 28 257 crawled European websites. With two automatic and semi-automatic crawl campaigns, we detect suspected violations, and we find that: 141 websites register positive consent even if the user has not made their choice; 236 websites nudge the users towards accepting consent by pre-selecting options; and 27 websites store a positive consent even if the user has explicitly opted out. Performing extensive tests on 560 websites, we find at least one suspected violation in 54% of them. Finally, we provide a browser extension to facilitate manual detection of suspected violations for regular users and Data Protection Authorities.

We present several novel techniques to track (unassociated) mobile devices by abusing features of the Wi-Fi standard. This shows that using random MAC addresses, on its own, does not guarantee privacy. First, we show that information elements in probe requests can be used to fingerprint devices. We then combine these fingerprints with incremental sequence numbers, to create a tracking algorithm that does not rely on unique identifiers such as MAC addresses. Based on real-world datasets, we demonstrate that our algorithm can correctly track as much as 50% of devices for at least 20 minutes. We also show that commodity Wi-Fi devices use predictable scrambler seeds. These can be used to improve the performance of our tracking algorithm. Finally, we present two attacks that reveal the real MAC address of a device, even if MAC address randomization is used. In the first one, we create fake hotspots to induce clients to connect using their real MAC address. The second technique relies on the new 802.11u standard, commonly referred to as Hotspot 2.0, where we show that Linux and Windows send Access Network Query Protocol (ANQP) requests using their real MAC address.

MAC address randomization in Wi-Fi-enabled devices has recently been adopted to prevent passive tracking of mobile devices. However, Wi-Fi frames still contain fields that can be used to fingerprint devices and potentially allow tracking. Panoptiphone is a tool inspired by the web browser fingerprinting tool Panopticlick [2], which aims to show the identifying information that can be found in the frames broadcast by a Wi-Fi-enabled device. Information is passively collected from devices that have their Wi-Fi interface enabled, even if they are not connected to an access point. Panoptiphone uses this information to create a fingerprint of the device and empirically evaluate its uniqueness among a database of fingerprints. The user is then shown how much identifying information its device is leaking through Wi-Fi and how unique it is.

MAC address randomization is a common privacy protection measure deployed in major operating systems today. It is used to prevent user-tracking with probe requests that are transmitted during IEEE 802.11 network scans. We present an attack to defeat MAC address randomization through observation of the timings of the network scans with an off-the-shelf Wi-Fi interface. This attack relies on a signature based on inter-frame arrival times of probe requests, which is used to group together frames coming from the same device although they use distinct MAC addresses. We propose several distance metrics based on timing and use them together with an incremental learning algorithm in order to group frames. We show that these signatures are consistent over time and can be used as a pseudo-identifier to track devices. Our framework is able to correctly group frames using different MAC addresses but belonging to the same device in up to 75% of the cases. These results show that the timing of 802.11 probe frames can be abused to track individual devices and that address randomization alone is not always enough to protect users against tracking.

Today, almost all mobile devices come equipped with Wi-Fi technology. Therefore, it is essential to thoroughly study the privacy risks associated with this technology. Recent works have shown that some Personally Identifiable Information (PII) can be obtained from the radio signals emitted by Wi-Fi equipped devices. However, most of the times, the identity of the subject of those pieces of information remains unknown and the Wi-Fi MAC address of the device is the only available identifier. In this paper, we show that it is possible for an attacker to get the identity of the subject.