Online Behavioral Advertising (OBA) is pervasive on the Internet. While there is a line of empirical research that studies Internet users' attitudes and privacy preferences of OBA, little is known about their actual understandings of how OBA works. This is an important question to answer because people often draw on their understanding to make decisions. Through a qualitative study conducted in an iterative manner, we identify four "folk models" held by our participants about how OBA works and show how these models are either incomplete or inaccurate in representing common OBA practices. We discuss how privacy tools can be designed to consider these folk models. In addition, most of our participants felt that the information being tracked is more important than who the web trackers are. This suggests the potential for an information-based blocking scheme rather than a tracker-based blocking scheme used by most existing ad-blocking tools.

Web trackers can build accurate topical user profiles (e.g., in terms of habits and personal characteristics) by monitoring a user's browsing activities across websites. This process, known as behavioral targeting, has a number of practical benefits but it also raises privacy concerns. Most existing techniques either try to block web tracking altogether or aim to endow it with privacy preserving mechanisms, but they are system-centered rather 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 maybe different, ranging from a desire to avoid being profiled at all to a willingness to trade personal information for better services. Regardless of a specific user's preference, from a technical point of view there is is no simple way for him/her to monitor, let alone to influence, the behavior of web trackers. In this paper, we describe an approach which makes users aware of their likely tracking profile and gives them the possibility to bias the profile towards both ends of the web tracking spectrum, either by improving its accuracy beyond the tracker capabilities (thus emphasizing behavioral targeting) or by filling in false interests (thus increasing privacy). This goal is achieved by simulating the process of learning a user profile on the part of the tracker and then by retrofitting a web traffic suitable for producing the desired profile. Our approach has been implemented as a web browser extension called ManTra (Management of Tracking). The system has been evaluated in several dimensions, including its ability to learn an accurate ad-oriented user profile and to influence the behavior of a commercial tool for web tracking personalization, i.e., Google's Ads Settings.