A privacy-aware framework for online advertisement targeting

With the prosperity of the Internet, many advertisers choose to deliver their advertisements by online targeting, where the ad broker is responsible for matching advertisements with users who are likely to be interested in the underlying products or services. However, this online advertisement targeting system requires user profile information and may fail due to privacy issues. In light of growing privacy concerns, we propose a privacy-aware framework for online advertisement targeting, where users are compensated for their privacy leakage and motivated to click more advertisements. In the framework, an ad broker pays a varying amount of money to users for clicking different advertisements due to distinct privacy leakage. Meanwhile advertisers send advertisements to the ad broker and determine the price per user click they need to pay. We model the interactions among advertisers, the ad broker and users as a three-stage game, where every player aims at maximizing its own utility, and Nash Equilibrium is achieved by backward induction. We further analyze the optimal strategies for advertisers, the ad broker and users. Numerical results have shown that the proposed privacy-aware framework is effective as it enables all advertisers, the ad broker and users to maximize their utilities in case of different levels of user privacy sensitivities. In addition, the proposed framework produces higher profits for advertisers and the ad broker than the traditional “paid to click” system.