Privacy-Aware Online Social Networking With Targeted Advertisement

Abstract

In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users’ information for revenue generation through targeted advertisement, in which the SNP presents advertisements to proper users effectively. Therefore, an advertiser is more willing to pay for targeted advertisement to promote his product. However, the over-exploitation of users’ information would invade users’ privacy, which would negatively impact users’ social activeness. Motivated by this, we study the privacy policy (policies) of the SNP(s) with targeted advertisement, in both monopoly and duopoly markets. We characterize the privacy policy in terms of the fraction of users’ information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP(s) as a three-stage Stackelberg game. By leveraging the model’s supermodularity property, we prove the threshold structure of users’ equilibrium information levels. We discover the overall information that can be exploited by an SNP is non-monotonic in the exploitation fraction. Monopoly (one SNP) study shows our proposed optimal privacy policy helps the SNP earn even more advertisement revenue than full exploitation policy does. The situation of the duopoly market is much more complicated. In that case, if the service quality gap between the two SNPs is large, the stronger SNP will choose a conservative privacy protection policy that drives the other SNP out of the market. However, if the service quality gap is small and the advertisement revenue is promising, the stronger SNP would choose an aggressive policy to exploit the advertisement revenue and both SNPs will have positive market shares.