Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used density-based clustering algorithm, and the study of its privacy-preserving methods is of practical importance. However, prior works either leak important intermediate results or suffer from intolerable overhead, which makes it difficult to deploy in real-world scenarios. To address this problem, we propose Private-DBSCAN, a practical secure two-party framework for DBSCAN. Specifically, (i) we design an efficient secure comparison protocol for the calculation of the adjacency matrix, which reduces the online communication to only one round and (ii) we employ a secret-shared shuffle protocol to anonymize the data records, which can hide the position relation of elements while avoiding redundant computations. These ingredients allow Private-DBSCAN to achieve practical efficiency and rigorous security at the same time. We implement our protocol and conduct extensive experiments on five datasets, which show that it achieves a $90\sim 340\times$ speedup on LAN and $13\sim 73\times$ speedup on WAN compared to the state-of-the-art work.