In response to the inadequacy of existing crowd sensing incentive methods to meet the privacy requirements of users in distributed environments, a crowd sensing privacy protection and incentive method called tangle network incentive policy (TNIP) based on the tangle network was proposed. Firstly, guided by the concept of privacy protection, the sensing network adopted the tangle network framework, which ensured network security through a distributed recording approach. Secondly, to address issues such as malicious competition and the proof of work threshold introduced by blockchain, a PoW (proof of work) control algorithm was designed to lower the participation threshold and mitigate malicious competition. Furthermore, the privacy of sensing data was strengthened by utilizing the ECDSA (elliptic curve digital signature algorithm) based digital signature method, enhancing the security of the data. Subsequently, a quantitative evaluation model was used to assess the risks of privacy leakage, and a trusted milestone transaction was employed to control network risks and enhance the privacy of the sensing network. Finally, by utilizing real-world datasets and conducting simulation experiments, a comparative analysis of the incentive effect and privacy protection methods proposed by TNIP was performed. The experimental results demonstrate that the TNIP method achieves an average participation rate improvement of 8.79% and 5.93% over the CSII (cross-space multi-interaction-based-dynamic incentive scheme) method and TCS (tangle-net crowd sensing) method, respectively, for the three categories of tasks.