Information about consumer's web navigation trails are increasingly being collected, analyzed, and used to target them with advertisements. This information can be quite personal, indicating individuals' likes and dislikes, as well as current and long term needs. While the ability to effectively target advertisements helps keep many sites and services on the web available freely, this practice has also raised privacy concerns. These concerns concern multiple factors: lack of transparency by the data aggregators and lack of control by the consumers. One viable approach that individuals can take to regain control is obfuscation, whereby real user requests are masked via the injection of noisy requests. In this paper, we describe a theoretical model and design for a web browser extension that relies on a trusted third party to generate fake HTTP requests (dummies). The dummy requests are generated as k different users' profiles surfing in parallel with the actual user. The value of k can be adjusted by the user to achieve the level of obfuscation they are comfortable with.