Federated learning enables numerous clients to collaboratively train a model using their local data without exposing the raw data of individual parties, thereby avoiding data leakage and misuse caused by data migration in traditional machine learning paradigms. However, in practical scenarios, clients often exhibit heterogeneous data distributions and diverse system capabilities, which degrade model performance and training efficiency. Selecting a high-quality subset of clients as participants in federated learning can effectively improve global model performance and accelerate convergence. Nevertheless, existing studies show that malicious adversaries can exploit information such as local training losses or gradients to infer sensitive private data, while current participant selection strategies for heterogeneous federated learning fail to adequately address such privacy leakage risks. To tackle this challenge, this study designs a privacy-preserving participant selection protocol for heterogeneous federated learning based on secure multi-party computation. By leveraging three-party computation (3PC) secret-sharing techniques, the proposed protocol ensures data privacy during training while maintaining the accuracy of the jointly trained model. Furthermore, a secure top-k search protocol is introduced to prevent privacy leakage during the participant selection phase. The security of the proposed protocol is formally analyzed, demonstrating that the required security properties are satisfied. Experimental results indicate that, compared with heterogeneous federated learning schemes without privacy preservation, the proposed approach increases the average computational and communication overhead across all parties by 2.09%.