With the prosperity of the Internet, many advertisers choose to deliver their advertisements by online targeting, where an ad broker is responsible for matching advertisements with users who are likely to be interested in the underlying products or services. However, the existing online advertisement targeting system requires user profile information for matching and may fail when users opt out of revealing their private information. Some existing works, that try to tackle the privacy problem, either fail to fully protect user privacy or dissatisfy advertisers. In light of the growing privacy concerns, we propose two privacy-aware mechanisms for online advertisement targeting. The first mechanism is designed for all types of online targeted advertising, where users are compensat...[ Read more ]

With the prosperity of the Internet, many advertisers choose to deliver their advertisements by online targeting, where an ad broker is responsible for matching advertisements with users who are likely to be interested in the underlying products or services. However, the existing online advertisement targeting system requires user profile information for matching and may fail when users opt out of revealing their private information. Some existing works, that try to tackle the privacy problem, either fail to fully protect user privacy or dissatisfy advertisers. In light of the growing privacy concerns, we propose two privacy-aware mechanisms for online advertisement targeting. The first mechanism is designed for all types of online targeted advertising, where users are compensated for their privacy leakage. 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 all the players. Numerical results have shown that the proposed privacy-aware framework is effective as it enables all the players to maximize their utilities in case of different levels of user privacy sensitivities. The second mechanism is designed for location-based advertising, which pushes location-related advertisements to user mobile devices. In our proposed system, privacy-insensitive users are leveraged to broadcast the location-based ads to the privacy-sensitive users around them. We design a number-reward contract scheme to reward the privacy-insensitive users for delivering the ads. In this scheme, a set of ad broadcast reward plans is offered to different insensitive users, who select the most suitable plans based on their utilities. Optimal contract designs are discussed theoretically and we carry out simulations to verify the analysis. The results show that a win-win situation is achieved, where every entity involved has an increased utility.