A tracking ow is a ow between an end user and a Web tracking service. We develop an extensive measurement methodology for quantifying at scale the amount of tracking ows that cross data protection borders, be it national or international, such as the EU28 border within which the General Data Protection Regulation (GDPR) applies. Our methodology uses a browser extension to fully render advertising and tracking code, various lists and heuristics to extract well known trackers, passive DNS replication to get all the IP ranges of trackers, and state-of-the art geolocation. We employ our methodology on a dataset from 350 real users of the browser extension over a period of more than four months, and then generalize our results by analyzing billions of web tracking ows from more than 60 million broadband and mobile users from 4 large European ISPs. We show that the majority of tracking ows cross national borders in Europe but, unlike popular belief, are pretty well conned within the larger GDPR jurisdiction. Simple DNS redirection and PoP mirroring can increase national connement while sealing almost all tracking ows within Europe. Last, we show that cross boarder tracking is prevalent even in sensitive and hence protected data categories and groups including health, sexual orientation, minors, and others.

Networks-on-Chips (NoCs) are experiencing escalating susceptibility to wear-out and reduced reliability, with the risk of becoming the key point of failure in an entire multicore chip. Aiming towards seamless NoC operation in the presence of faulty communication links, in this paper we propose Hermes, a highly-robust, distributed and lightweight fault-tolerant routing algorithm, whose performance degrades gracefully with increasing faulty link counts. Hermes is a deadlockfree hybrid routing algorithm, utilizing load-balancing routing on faultfree paths to sustain high-performance, while providing pre-reconfigured escape path selection in the vicinity of faults. Additionally, Hermes identifies non-communicating network partitions in scenarios where faulty links are topologically densely distributed. An extensive experimental evaluation, including utilizing traffic benchmarks gathered from fullsystem chip multi-processor simulations, shows that Hermes improves network throughput by up to 3× when compared against prior-art.

Several data protection laws include special provisions for protecting personal data relating to religion, health, sexual orientation, and other sensitive categories. Having a well-dened list of sensitive categories is sucient for ling complaints manually, conducting investigations, and prosecuting cases in courts of law. Data protection laws, however, do not dene explicitly what type of content falls under each sensitive category. Therefore, it is unclear how to implement proactive measures such as informing users, blocking trackers, and ling complaints automatically when users visit sensitive domains. To empower such use cases we turn to the Curlie.org crowdsourced taxonomy project for drawing training data to build a text classier for sensitive URLs. We demonstrate that our classier can identify sensitive URLs with accuracy above 88%, and even recognize specic sensitive categories with accuracy above 90%. We then use our classier to search for sensitive URLs in a corpus of 1 Billion URLs collected by the Common Crawl project. We identify more than 155 millions sensitive URLs in more than 4 million domains. Despite their sensitive nature, more than 30% of these URLs belong to domains that fail to use HTTPS. Also, in sensitive web pages with third-party cookies, 87% of the third-parties set at least one persistent cookie. CCS CONCEPTS• Security and privacy → Privacy protections; • Information systems → World Wide Web; • Networks → Network measurement.

In recent years, governments worldwide have moved their services online to better serve their citizens. Benefits aside, this choice increases the danger of tracking via such sites. This is of great concern as governmental websites increasingly become the only interaction point with the government. In this paper, we investigate popular governmental websites across different countries and assess to what extent the visits to these sites are tracked by third-parties. Our results show that, unfortunately, tracking is a serious concern, as in some countries up to 90% of these websites create cookies of third-party trackers without any consent from users. Non-session cookies, that are created by trackers and can last for days or months, are widely present even in countries with strict user privacy laws. We also show that the above is a problem for official websites of international organizations and popular websites that inform the public about the COVID-19 pandemic. CCS CONCEPTS• Information systems → World Wide Web; • Security and privacy → Human and societal aspects of security and privacy.

As recent events have demonstrated, disinformation spread through social networks can have dire political, economic and social consequences. Detecting disinformation must inevitably rely on the structure of the network, on users particularities and on event occurrence patterns. We present a graph data structure, which we denote as a meta-graph, that combines underlying users' relational event information, as well as semantic and topical modeling. We detail the construction of an example meta-graph using Twitter data covering the 2016 US election campaign and then compare the detection of disinformation at cascade level, using well-known graph neural network algorithms, to the same algorithms applied on the meta-graph nodes. The comparison shows a consistent 3-4% improvement in accuracy when using the meta-graph, over all considered algorithms, compared to basic cascade classification, and a further 1% increase when topic modeling and sentiment analysis are considered. We carry out the same experiment on two other datasets, HealthRelease and HealthStory, part of the FakeHealth dataset repository, with consistent results. Finally, we discuss further advantages of our approach, such as the ability to augment the graph structure using external data sources, the ease with which multiple meta-graphs can be combined as well as a comparison of our method to other graph-based disinformation detection frameworks.