Analysis, detection and classification of web tracking techniques

Abstract

Web tracking technologies are extensively used to collect large amounts of personal information (PI), including the things we search, the sites we visit, the people we contact or the products we buy. During this Master’s Thesis, five of the most common tracking techniques have been analysed in detail, to know how they are performed, how dangerous are for the user’s privacy and classifying them based on this parameter. Then, an algorithm to detect each one was developed and added to the e-Privacy Observatory, a new open-source service that makes it possible for everyone to know if a web is following him or not. For the 10k most popular webs by Alexa’s ranking and in more than 800k URLs that these webs load, in 88.7% of them at least one tracking technique has been found in it, between other alarming results, showing that web tracking is almost everywhere.