Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools

Merzdovnik, Georg; Huber, Markus; Buhov, Damjan; Nikiforakis, Nick; Neuner, Sebastian; Schmiedecker, Martin; Weippl, Edgar

doi:10.1109/eurosp.2017.26

Cited by 107 publications

(108 citation statements)

References 17 publications

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“…This replication cannot be seen as representative as our measurement misses essential features, especially to identify IDs, to assess cookie syncing since we only used one profile in each region. To test whether our results of increased cookie usage on subsites also applies to user tracking, we use the numbers presented by Merzdovnik et al [34] on the presence of trackers on websites as a baseline. To test if a tracker is active on a website, we use the EasyPrivacy List [8], which is a list combining URLs of known trackers.…”

Section: Replication and Comparisonmentioning

confidence: 99%

Beyond the Front Page:Measuring Third Party Dynamics in the Field

Urban¹,

Degeling

Holz

et al. 2020

Proceedings of the Web Conference 2020

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In the modern Web, service providers often rely heavily on third parties to run their services. For example, they make use of ad networks to finance their services, externally hosted libraries to develop features quickly, and analytics providers to gain insights into visitor behavior.For security and privacy, website owners need to be aware of the content they provide their users. However, in reality, they often do not know which third parties are embedded, for example, when these third parties request additional content as it is common in real-time ad auctions.In this paper, we present a large-scale measurement study to analyze the magnitude of these new challenges. To better reflect the connectedness of third parties, we measured their relations in a model we call third party trees, which reflects an approximation of the loading dependencies of all third parties embedded into a given website. Using this concept, we show that including a single third party can lead to subsequent requests from up to eight additional services. Furthermore, our findings indicate that the third parties embedded on a page load are not always deterministic, as 50 % of the branches in the third party trees change between repeated visits. In addition, we found that 93 % of the analyzed websites embedded third parties that are located in regions that might not be in line with the current legal framework. Our study also replicates previous work that mostly focused on landing pages of websites. We show that this method is only able to measure a lower bound as subsites show a significant increase of privacy-invasive techniques. For example, our results show an increase of used cookies by about 36 % when crawling websites more deeply.

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Section: Replication and Comparisonmentioning

confidence: 99%

Beyond the Front Page:Measuring Third Party Dynamics in the Field

Urban¹,

Degeling

Holz

et al. 2020

Proceedings of the Web Conference 2020

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“…7 Browser support for tracking protection has been extensively studied elsewhere [29], so we do not consider it here.…”

Section: Survey Of Email Clientsmentioning

confidence: 99%

I never signed up for this! Privacy implications of email tracking

Englehardt

Han

Narayanan

2018

Proceedings on Privacy Enhancing Technologies

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Abstract:We show that the simple act of viewing emails contains privacy pitfalls for the unwary. We assembled a corpus of commercial mailing-list emails, and find a network of hundreds of third parties that track email recipients via methods such as embedded pixels. About 30% of emails leak the recipient's email address to one or more of these third parties when they are viewed. In the majority of cases, these leaks are intentional on the part of email senders, and further leaks occur if the recipient clicks links in emails. Mail servers and clients may employ a variety of defenses, but we analyze 16 servers and clients and find that they are far from comprehensive. We propose, prototype, and evaluate a new defense, namely stripping tracking tags from emails based on enhanced versions of existing web tracking protection lists.

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“…The list of blocked scripts is somehow unique to each instance of Privacy Badger as it is being built alongside the websites that the user visits. However, the main downside of Privacy Badger is that the heuristic creation of blocking rules can be too aggressive and can lead to a high number of unresponsive websites as reported by [90].…”

Section: Decreasing the Surface Of Browser Apismentioning

confidence: 99%

Browser Fingerprinting

Bielova²,

et al. 2020

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With this paper, we survey the research performed in the domain of browser fingerprinting, while providing an accessible entry point to newcomers in the field. We explain how this technique works and where it stems from. We analyze the related work in detail to understand the composition of modern fingerprints and see how this technique is currently used online. We systematize existing defense solutions into different categories and detail the current challenges yet to overcome.

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Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools

Cited by 107 publications

References 17 publications

Beyond the Front Page:Measuring Third Party Dynamics in the Field

Beyond the Front Page:Measuring Third Party Dynamics in the Field

I never signed up for this! Privacy implications of email tracking

Browser Fingerprinting

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