The Transparency and Consent Framework (TCF), developed by the Interactive Advertising Bureau (IAB) Europe, provides a de facto standard for requesting, recording, and managing user consent from European end-users. Its goal is to help organizations comply with the General Data Protection Regulation (GDPR) and the ePrivacy Directive (ePD). This framework has previously been found to infringe European data protection law and has subsequently been regularly updated. Previous research on the TCF focused exclusively on web contexts, with no attention given to its implementation in mobile apps. No work has systematically studied the compliance implications of the TCF on Android apps. To address this gap, we investigate the prevalence of the TCF in popular Android apps from the Play Store, and assess whether these apps respect users’ consent banner choices. The TCF introduced minor changes in its new version (v.2.3) on March 1st, 2026, aimed at reducing ambiguity for vendors; these changes do not impact our results. We scraped and downloaded 4482 of the most popular Google Play Store apps on an emulated Android device. We automatically identified that 576 (12.85%) of the 4482 downloadable apps implemented the TCF, and we detected potential legal violations within this subset. By automatically interacting with consent banners, we observed that in 15 (2.6%) of these apps, users’ choices are stored only when consent is granted. Users who refuse consent are shown the consent banner again each time they launch the app. We analyzed the apps’ traffic in two different stages, passive (post interaction with the banner) and active (during banner interaction and post user choices). Network analysis conducted during the passive stage reveals that 66.2% of the analyzed TCF-based apps share personal data through the Google Advertising ID (AAID) without consent – the lawful basis for such processing. Furthermore, 55.3% of apps analyzed during the active stage share AAID before users interact with the apps’ consent banners, violating the prior consent requirement. We further expose concerns regarding Google as the dominant Consent Management Provider (CMP) in our dataset (89.76%), structurally accommodating potential legal violations. Our results suggest that mobile implementations of the TCF are prone to significant non-compliance practices, raising concerns about its effectiveness in helping organizations adhere to legal requirements.

Cookie pay walls allow visitors to access the content of a website only after making a choice between paying a fee (paying option) or accepting tracking (cookie option). The practice has been studied in previous research in regard to its prevalence and legal standing, but the effects of the clients’ device and geographic location remain unexplored. To address these questions, this study explores the effects of three factors: 1) the clients’ browser, 2) the device type (desktop or mobile), and 3) the geographic location on the presence and behavior of cookie pay walls and the handling of users’ data. Using an automatic crawler on our dataset composed of 804 websites that present a cookie pay wall, we observed that the presence of a cookie pay wall was most affected by the geographic location of the user. We further showed that both the behavior of a cookie pay wall and the processing of user data are impacted by all three factors, but no patterns of significance could be found. Finally, an additional type of pay wall was discovered on ∼ 11% of the studied websites, coined the “double pay wall”, which consists of a cookie pay wall complemented by another pay wall once tracking is accepted.

Although mobile devices benefit users in their daily lives in numerous ways, they also raise several privacy concerns. For instance, they can reveal sensitive information that can be inferred from location data. This location data is shared through service providers as well as mobile applications. Understanding how and with whom users share their location data as well as users’ perception of the underlying privacy risks, are important notions to grasp in order to design usable privacy-enhancing technologies. In this work, we perform a quantitative and qualitative analysis of smartphone users’ awareness, perception and self-reported behavior towards location data-sharing through a survey of n = 99 young adult participants (i.e., digital natives). We compare stated practices with actual behaviors to better understand their mental models, and survey participants’ understanding of privacy risks before and after the inspection of location traces and the information that can be inferred therefrom. Our empirical results show that participants have risky privacy practices: about 54% of participants underestimate the number of mobile applications to which they have granted access to their data, and 33% forget or do not think of revoking access to their data. Furthermore, most of the participants do not have a realistic perception of privacy risks and have generally heard little about privacy-related scandals. Also, by using a demonstrator to perform inferences from location data, we observe that slightly more than half of participants (57%) are surprised by the extent of potentially inferred information, and that 47% intend to reduce access to their data via permissions as a result of using the demonstrator. Last, a majority of participants have little knowledge of the tools to better protect themselves, but are nonetheless willing to follow suggestions to improve privacy (51%). Educating people, including digital natives, about privacy risks through transparency tools seems a promising approach.

The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT). TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions. As personal data flows between different entities, there is a potential for privacy concerns. In this study, we aimed to identify the privacy factors that impact users’ concerns and preferences for using IoT TAPs. To address this research objective, we conducted three focus groups with 15 participants and we extracted nine themes related to privacy factors using thematic analysis. Our participants particularly prefer to have control and transparency over the automation and are concerned about unexpected data inferences, risks and unforeseen consequences for themselves and for bystanders that are caused by the automation. The identified privacy factors can help researchers derive predefined and selectable profiles of privacy permission settings for IoT TAPs that represent the privacy preferences of different types of users as a basis for designing usable privacy controls for IoT TAPs.

When software services use cloud providers to run their workloads, they place implicit trust in the cloud provider, without an explicit trust relationship. One way to achieve such explicit trust in a computer system is to use a hardware Trusted Platform Module (TPM), a coprocessor for trusted computing. However, in the case of managed platform-as-a-service (PaaS) offerings, there is currently no cloud provider that exposes TPM capabilities. In this paper, we improve trust by integrating a virtual TPM device into the Firecracker hypervisor, originally developed by Amazon Web Services. In addition to this, multiple performance tests along with an attack surface analysis are performed to evaluate the impact of the changes introduced. We discuss the results and conclude that the slight performance decrease and attack surface increase are acceptable trade-offs in order to enable trusted computing in PaaS offerings.