Targeted online advertising has become an inextricable part of the way Web content and applications are monetized. At the beginning, online advertising consisted of simple ad-banners broadly shown to website visitors. Over time, it evolved into a complex ecosystem that tracks and collects a wealth of data to learn user habits and show targeted and personalized ads. To protect users against tracking, several countermeasures have been proposed, ranging from browser extensions that leverage filter lists, to features natively integrated into popular browsers like Firefox and Brave to combat more modern techniques like browser fingerprinting. Nevertheless, few browsers offer protections against IP address-based tracking techniques. Notably, the most popular browsers, Chrome, Firefox, Safari and Edge do not offer any.

Large-scale Web crawls have emerged as the state of the art for studying characteristics of the Web. In particular, they are a core tool for online tracking research. Web crawling is an attractive approach to data collection, as crawls can be run at relatively low infrastructure cost and don’t require handling sensitive user data such as browsing histories. However, the biases introduced by using crawls as a proxy for human browsing data have not been well studied. Crawls may fail to capture the diversity of user environments, and the snapshot view of the Web presented by one-time crawls does not reflect its constantly evolving nature, which hinders reproducibility of crawl-based studies. In this paper, we quantify the repeatability and representativeness of Web crawls in terms of common tracking and fingerprinting metrics, considering both variation across crawls and divergence from human browser usage. We quantify baseline variation of simultaneous crawls, then isolate the effects of time, cloud IP address vs. residential, and operating system. This provides a foundation to assess the agreement between crawls visiting a standard list of high-traffic websites and actual browsing behaviour measured from an opt-in sample of over 50,000 users of the Firefox Web browser. Our analysis reveals differences between the treatment of stateless crawling infrastructure and generally stateful human browsing, showing, for example, that crawlers tend to experience higher rates of third-party activity than human browser users on loading pages from the same domains.

Web Extensions (add-ons) allow clients to customize their Web browsing experience through the addition of auxiliary features to their browsers. The add-on ecosystem is a market differentiator for the Firefox browser, offering contributions from both commercial entities and community developers. In this paper, we present the Telemetry-Aware Add-on Recommender (TAAR), a system for recommending add-ons to Firefox users by leveraging separate models trained to three main sources of user data: the set of add-ons a user already has installed; usage and interaction data (browser Telemetry); and the language setting of the user's browser (locale). We build individual recommendation models for each of these data sources, and combine the recommendations they generate using a linear stacking ensemble method. Our method employs a novel penalty function for tuning weight parameters, which is adapted from the log likelihood ratio cost function, allowing us to scale the penalty of both correct and incorrect recommendations using the confidence weights associated with the individual component model recommendations. This modular approach provides a way to offer relevant personalized recommendations while respecting Firefox's granular privacy preferences and adhering to Mozilla's lean data collection policy. To evaluate our recommender system, we ran a large-scale randomized experiment that was deployed to 350,000 Firefox users and localized to 11 languages. We found that, overall, users were 4.4% more likely to install add-ons recommended by our ensemble method compared to a curated list. Furthermore, the magnitude of the increase varies significantly across locales, achieving over 8% improvement among German-language users.