Recently, many researchers are engaged in developing novel mechanisms to maximize the spread of influence through social networks. The problem is defined as finding a small subset of individuals that can maximize the spread of influence. There exist lots of mechanisms, such as threshold and cascade models. However, the problem of incentivizing users is still under investigation. Moreover, the privacy of users has not been considered in this approaches.In this thesis, we propose a new mechanism based on auction and selecting group of users, named Group Auction Model (GAM). GAM leverages on selecting group of users in order to preserve privacy of individuals when they want to start advertising. Moreover, users can submit their costs through sealed bids in an auction. This would help designers to select groups that not only can maximize the influence, but also take into the consideration the users’ privacy. A new version of mechanism named, T-GAM is also proposed to guarantee truthfulness in the auction. Using DBLP dataset we made a few simulations and show how the proposed algorithm can choose groups considering the submitted bids. This will also preserve the privacy of users as the group makes advertisement instead of users. Keywords: Viral marketing, Diffusion models, Preserving privacy, Group auction, Truthfulness mechanism design