With the expansion of mobile crowdsensing applications, privacy leakage has become an increasingly serious issue, making privacy preservation crucial for enhancing user participation. However, existing research often overlooks the heterogeneity of users’ privacy needs, leading to insufficient or excessive allocation of privacy budgets. To solve this problem, this paper proposes a personalized privacy preserving model based on the Stackelberg game, referred to as SPPM (Stackelberg Game-based Personalized Privacy Preserving Model). This model employs a two-stage Stackelberg game framework to determine the optimal personalized privacy budget for each user, ensuring the user’s preferences are met while maximizing the utility of the requester. Additionally, a dynamic perturbation algorithm is designed within the model, which adjusts the noise level based on the personalized privacy budget to meet varying levels of privacy preserving. The paper not only provides theoretical proof that SPPM satisfies differential privacy, but also validates the feasibility and effectiveness of the model by plenty of experiments on real-world datasets.