Smartphones and mobile networks have created a new paradigm called mobile crowdsensing for data gathering and processing about a large-scale phenomenon. This paradigm's development allows untrustworthy people to gather large amounts of information over time from the reported events. In this regard, mobile crowdsensing increases the possibility of violating users' privacy. Recently, social communication has added potential capabilities to mobile crowdsourcing environments. Based on these social communications, this article presents an analysis of user's interactions for privacy-preserving using a signaling game with a dynamic approach and incomplete information. First, the analysis identifies the pure-strategy profiles and mixed-strategy profiles and the user's best strategy for accepting access requests in a single time slot. This analysis is then extended into a dynamic multistage game. In this analysis, users' beliefs about the violation of their privacy are analyzed and updated at every stage. As a result, the best response from users will be determined. Following this analysis, a mechanism is presented for privacy-preserving in social mobile crowdsensing. The experimental results have also indicated the success of the proposed method in dealing with different types of user interaction.