Abstract
This thesis is about tracking online user behavior. Tracking is a granular problem, that goes beyond the phenomenon of behavioral advertising. It takes place in the context of rapid technological, ocial and political developments. The main contributions of this thesis are:(1) A working definition for web tracking.(2) An opt-out mechanism for web tracking based on regular expressions repository in combinationwith persistent opt-out cookies.(3) We show that the current do-not-track-me register, an opt-out mechanism for online behavioraladvertising (OBA) based on opt-out cookies, of IAB/EASA falls short on it’s promise. The opt-out cookie expiration dates are inconsistent, often less then five years. This is in contrast with the NAIopt-out tool, where all opt-out cookies have a minimum expiration data of five years. Added tothat, opt-out behavior is collected by third parties.(4) We show that the interconnectedness between nodes expressed in the number of links per node is an indicator (clustering coefficient) for web tracking.(5) We show that the number off iltered nodes with TDS as a percentage of the total number of nodes is an indicator for web tracking.(6) Five rules for constraint based graph mining of HTTP header information show that with use of a confirmed web tracking repository, it is possible to identify new third party tracking activity with connectivity color maps and pattern matching.
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