A privacy-aware framework for targeted advertising

Much of today’s Internet ecosystem relies on online advertising for financial support. Since the effectiveness of advertising heavily depends on the relevance of the advertisements (ads) to user’s interests, many online advertisers turn to targeted advertising through an ad broker, who is responsible for personalized ad delivery that caters to user’s preference and interest. Most of existing targeted advertising systems need to access the users’ profiles to learn their traits, which, however, has raised severe privacy concerns and make users unwilling to involve in the advertising systems. Spurred by the growing privacy concerns, this paper proposes a privacy-aware framework to promote targeted advertising. In our framework, the ad broker sits between advertisers and users for targeted advertising and provides certain amount of compensation to incentivize users to click ads that are interesting yet sensitive to them. The users determine their clicking behaviors based on their interests and potential privacy leakage, and the advertisers pay the ad broker for ad clicking. Under this framework, the optimal strategies of the advertisers, the ad broker and the users are analyzed by formulating the problem as a three-stage game, in which a unique Nash Equilibrium is achieved. In particular, we analyze the players’ behaviors for the scenarios of independent advertisers and competing advertisers. Extensive simulations have been conducted and the results validate the effectiveness of the proposed framework by showing that the utilities of all entities are significantly improved compared with traditional systems.