We tracked the largest volunteer security information sharing community known to date: the COVID-19 Cyber Threat Coalition, with over 4,000 members. This enabled us to address long-standing questions on threat information sharing. First, does collaboration at scale lead to better coverage? And second, does making threat data freely available improve the ability of defenders to act? We found that the CTC mostly aggregated existing industry sources of threat information. User-submitted domains often did not make it to the CTC's blocklist as a result of the high threshold posed by its automated quality assurance using VirusTotal. Although this ensured a low false positive rate, it also caused the focus of the blocklist to drift away from domains related to COVID-19 (1.4%-3.6%) to more generic abuse, such as phishing, for which established mitigation mechanisms already exist. However, in the slice of data that was related to COVID-19, we found promising evidence of the added value of a community like the CTC: just 25.1% of these domains were known to existing abuse detection infrastructures at time of listing, as compared to 58.4% of domains on the overall blocklist. From the unique experiment that the CTC represented, we draw three lessons for future threat data sharing initiatives.@en

In order to evaluate the prevalence of security and privacy practices on a representative sample of the Web, researchers rely on website popularity rankings such as the Alexa list. While the validity and representativeness of these rankings are rarely questioned, our findings show the contrary: we show for four main rankings how their inherent properties (similarity, stability, representativeness, responsiveness and benignness) affect their composition and therefore potentially skew the conclusions made in studies. Moreover, we find that it is trivial for an adversary to manipulate the composition of these lists. We are the first to empirically validate that the ranks of domains in each of the lists are easily altered, in the case of Alexa through as little as a single HTTP request. This allows adversaries to manipulate rankings on a large scale and insert malicious domains into whitelists or bend the outcome of research studies to their will. To overcome the limitations of such rankings, we propose improvements to reduce the fluctuations in list composition and guarantee better defenses against manipulation. To allow the research community to work with reliable and reproducible rankings, we provide TRANCO, an improved ranking that we offer through an online service available at https://tranco-list.eu. @en

Fitness tracking social networks such as Strava allow users to record sports activities and share them publicly. Sharing encourages peer interaction but also constitutes a risk, because an activity's start or finish may inadvertently reveal privacy-sensitive locations such as a home or workplace. To mitigate this risk, networks introduced endpoint privacy zones (EPZs), which hide track portions around protected locations. In this paper, we show that EPZ implementations of major services remain vulnerable to inference attacks that significantly reduce the effective anonymity provided by the EPZ, and even reveal the protected location. Our attack leverages distance information leaked in activity metadata, street grid data, and the locations of the entry points into the EPZ. This yields a constrained search space where we use regression analysis to predict protected locations. Our evaluation on 1.4 million Strava activities shows that our attack discovers the protected location for up to 85% of EPZs. Larger EPZs reduce the performance of our attack, while geographically dispersed activities in sparser street grids yield better performance. We propose six countermeasures, that, however, come with a usability trade-off, and responsibly disclosed our findings and countermeasures to the major networks.@en

Fitness tracking social networks such as Strava allow users to record sports activities and share them publicly. Sharing encourages peer interaction but also constitutes a risk, because an activity's start or finish may inadvertently reveal privacy-sensitive locations such as a home or workplace. To mitigate this risk, networks introduced endpoint privacy zones (EPZs), which hide track portions around protected locations. In this paper, we show that EPZ implementations of major services remain vulnerable to inference attacks that significantly reduce the effective anonymity provided by the EPZ, and even reveal the protected location. Our attack leverages distance information leaked in activity metadata, street grid data, and the locations of the entry points into the EPZ. This yields a constrained search space where we use regression analysis to predict protected locations. Our evaluation on 1.4 million Strava activities shows that our attack discovers the protected location for up to 85% of EPZs. Larger EPZs reduce the performance of our attack, while geographically dispersed activities in sparser street grids yield better performance. We propose six countermeasures, that, however, come with a usability trade-off, and responsibly disclosed our findings and countermeasures to the major networks.@en