Citizens rely on government websites for a wide array of essential services. However, these websites may embed third-party trackers, raising questions on privacy, security, and data sovereignty. We conduct a large-scale longitudinal study of tracker adoption and evolution on government websites worldwide. Our study spans 61 countries and nearly three decades (1996-2025), using historical snapshots from the Internet Archive. We find that tracking has shifted from rare to routine: by 2025, third-party trackers appear on 50% of studied government websites, with growth overwhelmingly driven by external (third-party) services. This tracking is dominated by a few large US-based organizations, with a long tail of lesser-known players. We also find great heterogeneity in tracker adoption and presence across regions, indicating that users around the world have differing exposure to privacy and security issues when accessing essential services. Our findings highlight the need for approaches to improve privacy and data sovereignty on public-service platforms.

Web trackers are pervasive on the Internet, collecting user data as it flows from the end user's devices to servers. Understanding the physical location of these servers and trackers data flow is essential for assessing privacy risks and ensuring control over user data. While much of the existing research focuses on the Global North, especially the EU where regulations such as the General Data Protection Regulation (GDPR) are in place, the Global South, where most Internet users reside, remains under-studied. We address this gap by collecting measurements from the web browser and the data-plane (IP of responding server & latency) on the same device. A challenge to apply this approach in the Global South is the lower density of observation points in measurement infrastructure. To address this, we build a software suite that automatically collects measurements in various countries from a volunteer's computer. Our suite is interoperable with all major OSes and requires limited user intervention. We apply our method across 23 geographically diverse countries, offering in-depth insights into tracker data flow.

EU data localization regulations limit data transfers to non-EU countries with the GDPR. However, BGP, DNS and other Internet protocols were not designed to enforce jurisdictional constraints, so implementing data localization is challenging. Despite initial research on the topic, little is known about if or how companies currently operate their server infrastructure to comply with the regulations. We close this knowledge gap by empirically measuring the extent to which servers and routers that process EU requests are located outside of the EU (and a handful of 'adequate' non-EU countries). The key challenge is that both browser measurements (to infer relevant endpoints) and data-plane measurements (to infer relevant IP addresses) are needed, but no large-scale public infrastructure allows both. We build a novel methodology that combines BrightData (browser) and RIPE Atlas (data-plane) probes, with joint measurements from over 1,000 networks in 20 EU countries. We find that, on average, 2.2% of servers serving users in each EU country are located in non-adequate destination countries (1.4% of known trackers). Our findings suggest that data localization policies are largely being followed by content providers, though there are exceptions.

Modern vehicles contain a multitude of electronic control units that implement software features controlling most of the operational, entertainment, connectivity, and safety aspects of the vehicle. However, with security requirements often being an afterthought in automotive software development, incorporation of such software features with intra- and inter-vehicular connectivity requirements often opens up new attack surfaces. Demonstrations of such security vulnerabilities in past reports and literature bring in the necessity to formally analyze how secure automotive control systems really are against adversarial attacks. Modern vehicles often incorporate onboard monitoring systems that test the sanctity of data samples communicated among controllers and detect possible attack/noise insertion scenarios. The performance of such monitors against security threats also needs to be verified. In this work, we outline a rigorous methodology for estimating the vulnerability of automotive CPSs. We provide a computer-aided design framework that considers the model-based representation of safety-critical automotive controllers and monitoring systems working in a closed loop with vehicle dynamics and verifies their safety and robustness w.r.t. false data injection attacks. Symbolically exploring all possible combinations of attack points of the input automotive CPS, the proposed framework tries to find out which sensor and/or actuation signal is vulnerable by generating stealthy and successful attacks using a formal method-based counter-example guided abstract refinement process. We also validate the efficacy of the proposed framework using a case study performed in an industry-scale simulator.

We consider the problem of securing a given control loop implementation of a cyber-physical system (CPS) in the presence of Man-in-the-Middle attacks on data exchange between plant and controller over a compromised network. To this end, there exists various detection schemes which provide mathemat¬ical guarantees against such attacks for the theoretical control model. However, such guarantees may not hold for the actual control software implementation. In this article, we propose a formal approach towards synthesizing attack detectors with varying thresholds which can prevent performance degrading stealthy attacks while minimizing false alarms.