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Table 1 - Most relevant related literature
Category

Detection/Classification

Regionality/Location

Targeted Advertisement/
Price Discrimination

Privacy/Security

Reference and title of published research

Active

[5] Using Passive Measurements to Demystify Online Trackers

Passive

[6] Detecting and Defending Against Third-Party Tracking on the Web

Active

[7] Tracking the Trackers

Client-side

[8] Online Tracking: A 1-million-site Measurement and Analysis

Active

[9] Towards Accurate Detection of Obfuscated Web Tracking

Client-side

[10] Tracing Cross Border Web Tracking

Client-side

[11] Annoyed Users: Ads and Ad-Block Usage in the Wild

Passive

[12] The Ad Wars: Retrospective Measurement and Analysis of AntiAdblock Filter Lists

Active

[13] Crowd-assisted Search for Price Discrimination in E-Commerce: First
results

Client-side

[14] Measuring Privacy Loss and the Impact of Privacy Protection in Web
Browsing

Active

[15] The Chain of Implicit Trust: An Analysis of the Web Third-party
Resources Loading

Active

many relevant publications related to web tracking in different
ways. In Table 1, we present a summary, divided by categories, of the most important related work in the field of network
measurements. We mainly focus on research that used network measurements as a methodology to detect or analyze
web tracking on the Internet.

Detection/Classification
T. Li et al. present a third-party tracker detection method using
machine learning techniques to inspect cookies that achieves
high accuracy [4]. The work also includes a measurementbased study of the third-party ecosystem within the top 10,000
most popular websites as per the Amazon Alexa's list [16]. They
found third-party tracking to be very frequent, with almost
50% of websites having some third-party tracker embedded
and 25% including a tracker from Google.
In [5], Metwalley et al. use passive network measurements
in a network with about 10,000 users to study the penetration and intrusiveness of web tracking systems in the Internet.
They found embedded trackers in more than 70% of the websites, and some of them collected data from 98% of the users.
Roesner et al. present a new Firefox addon called ShareMeNot, the first tool to block third-party tracking social
buttons by maintaining their functionality [6]. It also makes an
extensive study of the state of third-party tracking in a population of 1,000 websites, finding Google as the top tracker with
one third of occurrences.
Yu et al. present a new collaborative way of detecting trackers by computing the number of users that reach the same URL
resources on a website [7]. The idea behind it is that if a specific
URL is only used by one or a few users within all of the population loading the same website, that resource probably includes
a tracking identifier. They also observed a high percentage of
52	

Type

[4] TrackAdvisor: Taking Back Browsing Privacy from Third-Party Trackers

tracking penetration, being more than 95% for the studied German population.
In [8], Englehardt et al. design a new web tracking measurement system called OpenWPM that uses a combination
of a browser plugin, an HTTP proxy and the raw files created
by the browser (e.g., cookies, temp files) to detect web tracking from active web measurements. The research also includes
the study of the state of web tracking from one million websites in 2016.
Lastly, in [9], Le et al. study the presence of obfuscation
within web tracking and specifically in the canvas fingerprinting method. They found obfuscation to be present although
still not very widely used.

Regionality/Location
In [10], Iordanou et al. confront the problem of defining the
geo-location borders within web tracking traffic. They measure
the relation between the location of the user and the location of
the company performing web tracking by means of a browser
plugin to find if the current regulations (e.g., GDPR) allow to
investigate how the collected data was used. They found that
most tracking flows are well confined within the GDPR jurisdiction, but also that the most sensitive data (e.g., health,
sexual orientation, political opinions) is being tracked at some
extent inside as well as outside of the GDPR effective area.

Targeted Advertisement/Price Discrimination
Pujol et al. characterize advertisement traffic using passive
measurements and Adblock Plus, the most famous advertisement content-blocker plugin (also called adblocker) [11].
Starting from there, they explore the usage of adblockers
in the wild, taking measurements from a national ISP. They
found that about 22% of users browse the Internet with a

IEEE Instrumentation & Measurement Magazine	

December 2020



Instrumentation & Measurement Magazine 23-9

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