De-Anonymizing Web Browsing Data with Social Networks [pdf]
At the 33c3 I showed how you can often uniquely identify a single anonymized user from a dataset containing three million people by using publicly posted links from his/her Twitter timeline. I also showed that this is possible with other types of public information as well, such as YouTube video ratings or reviews on Google Maps.The math behind it is quite simple and very reliable for many datasets, which makes it very easy to build robust fingerprints based on browsing / location / behavior data. In my opinion, this is what most big companies rely on today for identifying users, as this is more robust than cookie-based mechanisms, which become more ineffective as the use of multiple devices and blockers increases.Here's the link to the video (you can choose the language in the menu, by default it's German but the talk is also available in English and French):https://media.ccc.de/v/33c3-8034-build_your_own_nsa