TrackSign-labeled web tracking dataset

Recent studies show that more than 95% of the websites available on the Internet contain at least one of the so-called web tracking systems. These systems are specialized in identifying their users by means of a plethora of different methods. Some of them (e.g., cookies) are very well known by most Internet users. However, the percentage of websites including more "obscure" and privacy-threatening systems, such as fingerprinting methods identifying a user's computer, is constantly increasing. Detecting those methods on today's Internet is very difficult, as almost any website modifies its content dynamically and minimizes its code in order to speed up loading times. This minimization and dynamicity render the website code unreadable by humans. Thus, the research community is constantly looking for new ways to discover unknown web tracking systems running under the hood. In this paper, we present a new dataset containing tracking information for more than 76 million URLs and 45 million online resources, extracted from 1.5 million popular websites. The tracking labeling process was done using a state-of-the-art discovery web tracking algorithm called TrackSign. The dataset also contains information about online security and the relation between the domains, the loaded URLs, and the online resource behind each URL. This information can be useful for different kinds of experiments, such as locating privacy-threatening resources, identifying security threats, or determining characteristics of the URL network graph.This publication is part of the Spanish I+D+i project TRAINER-A (ref.∼PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also supported by the Catalan Institution for Research and Advanced Studies (ICREA Academia).Peer ReviewedPostprint (published version

Deterministic and Contingent Factors in the Genesis of Agribusiness Clusters: The Pigmeat Industry in Nineteenth-Century Catalonia

This paper deals with the location, origins, and early development of clusters and industrial districts in the agribusiness sector. It considers the case of the meat processing industry in the second half of the 19th century in Catalonia, a prosperous southern-European region in the period when this industry emerged. By constructing an index of potential locations at the municipal level, the paper discusses whether, and to what extent, deterministic factors explain the genesis of pigmeat processing clusters. The paper concludes that although these latter factors need primary consideration, contingency, particularly human agency, may also play a non-negligible role. As an illustration, this study pays particular attention to the cluster of Vic, which developed around this middle-sized town in central Catalonia. It became a leading meat cluster in early 20th-century Spain, even though it was not better suited to this industry than other Catalan localities where meat companies had also emerged

ASTrack: Automatic detection and removal of web tracking code with minimal functionality loss

Recent advances in web technologies make it more difficult than ever to detect and block web tracking systems. In this work, we propose ASTrack, a novel approach to web tracking detection and removal. ASTrack uses an abstraction of the code structure based on Abstract Syntax Trees to selectively identify web tracking functionality shared across multiple web services. This new methodology allows us to: (i) effectively detect web tracking code even when using evasion techniques (e.g., obfuscation, minification, or webpackaging); and (ii) safely remove those portions of code related to tracking purposes without affecting the legitimate functionality of the website. Our evaluation with the top 10k most popular Internet domains shows that ASTrack can detect web tracking with high precision (98%), while discovering about 50k tracking code pieces and more than 3,400 new tracking URLs not previously recognized by most popular privacy-preserving tools (e.g., uBlock Origin). Moreover, ASTrack achieved a 36% reduction in functionality loss in comparison with the filter lists, one of the safest options available. Using a novel methodology that combines computer vision and manual inspection, we estimate that full functionality is preserved in more than 97% of the websites.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also par-tially supported by the NII internship program.Peer ReviewedPostprint (author's final draft

Predicting topology propagation messages in mobile ad hoc networks: The value of history

This research was funded by the Spanish Government under contracts TIN2016-77836-C2-1-R,TIN2016-77836-C2-2-R, and DPI2016-77415-R, and by the Generalitat de Catalunya as Consolidated ResearchGroups 2017-SGR-688 and 2017-SGR-990.The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.Peer ReviewedPostprint (published version

Amazon Alexa traffic traces

The number of devices that make up the Internet of Things (IoT) has been increasing every year, including smart speakers such as Amazon Echo devices. These devices have become very popular around the world where users with a smart speaker are estimated to be about 83 million in 2020. However, there has also been great concern about how they can affect the privacy and security of their users [1]. Responding to voice commands requires devices to continuously listen for the corresponding wake word, with the privacy implications that this entails. Additionally, the interactions that users may have with the virtual assistant can reveal private information about the user. In this document we publicly share two datasets that can help conduct privacy and security studies from the Amazon Echo Dot smart speaker. The included data contains 300.000 raw PCAP traces containing all the communications between the device and Amazon servers from 100 different voice commands on two different languages. The data can be used to train machine learning algorithms in order to find patterns that can characterize both, the voice commands and people using the device as well as Alexa as the device generating the traffic.Peer ReviewedPostprint (published version

Demystifying content-blockers: Measuring their impact on performance and quality of experience

With the evolution of the online advertisement and tracking ecosystem, content-blockers have become the reference tool for improving the security, privacy and browsing experience when surfing the Internet. It is also commonly believed that using content-blockers to stop unsolicited content decreases the time needed for loading websites. In this work, we perform a large-scale study on the actual improvements of using content-blockers in terms of performance and quality of experience. For measuring it, we analyze the page size and loading times of the 100K most popular websites, as well as the most relevant QoE metrics, such as the Speed Index, Time to Interactive or the Cumulative Layout Shift, for the subset of the top 10K of them. Our experiments show that using content-blockers results in small improvements in terms of performance. However, contrary to popular belief, this has a negligible impact in terms of loading time and quality of experience. Moreover, in the case of small and lightweight websites, the overhead introduced by content-blockers can even result in decreased performance. Finally, we evaluate the improvement in terms of QoE based on the Mean Opinion Score (MOS) and find that two of the three studied content-blockers present an overall decrease between 3% and 5% instead of the expected improvement.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

ePrivo.eu: An online service for automatic web tracking discovery

Given the pervasiveness of web tracking practices on the Internet, many countries are developing and enforcing new privacy regulations to ensure the rights of their citizens. However, discovering websites that do not comply with those regulations is becoming very challenging, given the dynamic nature of the web or the use of obfuscation techniques. This work presents ePrivo, a new online service that can help Internet users, website owners, and regulators inspect how privacy-friendly a given website is. The system explores all the content of the website, including traffic from third parties and dynamically modified content. The ePrivo service combines different state-of-the-art tracking detection and classification methods, including TrackSign, to discover both previously known and zero-day tracking methods. After 6 months of service, ePrivo detected the largest browsing history trackers and more than 40k domains including cookies with a lifespan longer than one year, which is forbidden in some countries.This work was supported in part by the Spanish I+D+i Project TRAINER-A, funded by MCIN/AEI/10.13039/501100011033, under Grant PID2020-118011GB-C21; and in part by the Catalan Institution for Research and Advanced Studies (ICREA Academia).Peer ReviewedPostprint (published version

Effect of Gamma Radiation on the Processability of New and Recycled PA-6 Polymers

The growing quantities of plastic waste have raised environmental concerns, with almost a quarter of disposed plastics being sent to landfill. This has motivated research efforts into various recycling technologies to ease dependence on fossil resources, increasing circularity. Irradiation of various kinds, such as electron beam, beta and gamma rays, has been studied in the past as a way of revamping end-of-life polymer properties. The present work focuses on the effects of gamma radiation on the processability of new and recycled polymers, which is intimately linked with their rheological properties. In this study, both virgin and recycled polymers were irradiated under different radiation doses and the effects of the radiation on their viscosity assessed and compared. Results were analyzed making use of different theoretical relationships, and the causes of the changes in rheology were investigated by means of various characterization techniques, such as GPC, FTIR, EPR and DSC. Finally, the rheological curves of all samples were fitted to the Ostwald–de Waele relationship and the dependence of its parameters on the absorbed dose fitted to a function