Mobile crowdsensing (MCS) is a new mode for collecting and mining data and intelligent decision-making with mobile intelligent devices. The key to the high performance of MCS is the efficient method of task allocation. The traditional algorithm (greedy algorithm or ant algorithm) assumes that workers and tasks are static. It’s not fit for the scene where the position and time of workers and tasks change continuously. In addition, the existing methods usually make decisions by the central server based on the collected information, which usually leads to leakage of workers' privacy. Therefore, we propose a task allocation method based on deep reinforcement learning (DRL) with privacy protection. Firstly, aiming to maximize the two-way benefits of workers and platforms and realizes Nash equilibrium, the task allocation is modeled as a dynamic programming problem of multi-objective optimization. Secondly, the model based on proximal policy optimization (PPO) of DRL for training and learning model parameters is proposed. Finally, we use the local differential privacy method to add random noise to the sensitive information of workers to protect privacy. The central server trains the whole model to obtain the optimal allocation strategy. In this paper, the astringency, revenue and task cover rate are experimentally evaluated. The results show that the proposed method has significant improvement in different indexes, and can protect the privacy of workers, compared with the traditional methods and other DRL based methods.