Section: New Results
Measurement and Detection of Web Tracking
Missed by Filter Lists: Detecting Unknown Third-Party Trackers with Invisible Pixels
The Web has become an essential part of our lives: billions are using Web applications on a daily basis and while doing so, are placing digital traces on millions of websites. Such traces allow advertising companies, as well as data brokers to continuously profit from collecting a vast amount of data associated to the users.
Web tracking has been extensively studied over the last decade. To detect tracking, most of the research studies and user tools rely on consumer protection lists. EasyList (https://easylist.to/) and EasyPrivacy (https://easylist.to/easylist/easyprivacy.txt) (EL&EP) are the most popular publicly maintained blacklist of know advertising and tracking domains, used by the popular browser extensions AdBlock Plus (https://adblockplus.org/) and uBlockOrigin (https://github.com/gorhill/uBlock). Disconnect (https://disconnect.me/trackerprotection/blocked) is another very popular list for detecting domains known for tracking, used in Disconnect browser extension (https://disconnect.me/) and in integrated tracking protection of Firefox browser. Relying on EL&EP or Disconnect became the de facto approach to detect third-party tracking requests in privacy and measurement community. However it is well-known that these lists detect only known tracking and ad-related requests, and a tracker can easily avoid this detection by registering a new domain or changing the parameters of the request.
Our contributions: To evaluate the effectiveness of filter lists, we propose a new, fine-grained behavior-based tracking detection. Our results are based on a stateful dataset of 8K domains with a total of 800K pages generating 4M third-party requests. We make the following contributions:
We analyse all the requests and responses that lead to invisible pixels (by “invisible pixels” we mean pixel images or images without content). Pixels are routinely used by trackers to send information or third-party cookies back to their servers: the simplest way to do it is to create a URL containing useful information, and to dynamically add an image tag into a webpage. This makes invisible pixels the perfect suspects for tracking and propose a new classification of tracking behaviors. Our results show that pixels are still widely deployed: they are present on more than 94% of domains and constitute 35.66% of all third-party images. We found out that pixels are responsible only for 23.34% of tracking requests, and the most popular tracking content are scripts: a mere loading of scripts is responsible for 34.36% of tracking requests.
We uncover hidden collaborations between third parties. We applied our classification on more than 4M third-party requests collected in our crawl. We have detected new categories of tracking and collaborations between domains. We show that domains sync first party cookies through a first to third party cookie syncing. This tracking appears on 67.96% of websites.
We show that filter lists miss a significant number of cookie-based tracking. Our evaluation of the effectiveness of EasyList&EasyPrivacy and Disconnect lists shows that they respectively miss 25.22% and 30.34% of the trackers that we detect. Moreover, we find that if we combine all three lists, 379,245 requests originating from 8,744 domains still track users on 68.70% of websites.
We show that privacy browser extensions miss a significant number of cookie-based tracking. By evaluating the popular privacy protection extensions: Adblock, Ghostery, Disconnect, and Privacy Badger, we show that Ghostery is the most efficient among them and that all extensions fail to block at least 24% of tracking requests.
This paper  has been accepted for publication at the Privacy Enhancing Technologies Symposium (PETs) 2020.
A survey on Browser Fingerprinting
This year, we have conducted a survey on 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.
The goal of this work is twofold: first, to provide an accessible entry point for newcomers by systematizing existing work, and second, to form the foundations for future research in the domain by eliciting the current challenges yet to overcome. We accomplish these goals with the following contributions:
A thorough survey of the research conducted in the domain of browser fingerprinting with a summary of the framework used to evaluate the uniqueness of browser fingerprints and their adoption on the web.
This work has been submitted for publication at an international journal.