Web trackers can build accurate topical user profiles(e.g., in terms of habits and personal characteristics) by monitoringa user’s browsing activities across websites. This process, known as behavioral targeting, has a number of practical benefitsbut it also raises privacy concerns. Most existing techniques either try to block web tracking altogether or aim to endowit with privacy preserving mechanisms, but they are system-centeredrather than user-centered. Nowadays, the majority of users want to have some degree of control over their privacy,while their perspectives and feelings towards web tracking may be different, ranging from a desire to avoid being profiled at allto a willingness to trade personal information for better services. Regardless of a specific user’s preference, from a technical pointof view there is is no simple way for him/her to monitor, letalone to influence, the behavior of web trackers. We have taken an approach which makes users aware of theirlikely tracking profile and gives them the possibility to bias theprofile towards both ends of the web tracking spectrum, eitherby improving its accuracy beyond the tracker capabilities (thusemphasizing behavioral targeting) or by filling in false interests(thus increasing privacy). This goal is achieved by simulatingthe process of learning a user profile on the part of the trackerand then by retrofitting a web traffic suitable for producing thedesired profile. Our approach has been implemented as a webbrowser extension called ManTra (Management of Tracking).The system has been evaluated in several dimensions, includingits ability to learn an accurate ad-oriented user profile and toinfluence the behaviour of a commercial tool for web trackingpersonalization; i.e., Google’s Ads Settings.