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.<p>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.<p>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):<p><a href="https://media.ccc.de/v/33c3-8034-build_your_own_nsa" rel="nofollow">https://media.ccc.de/v/33c3-8034-build_your_own_nsa</a>