We propose a practical privacy-preserving clustering protocol over horizontally partitioned data. We extend the DBSCAN clustering algorithm into a distributed protocol in which data providers mix real data with fake data to provide privacy. Our privacy-preserving clustering protocol is very efficient whereas the previous privacy-preserving protocols in the distributed environments are not practical to be used in real applications. The efficiency of our privacy-preserving clustering protocol over horizontally partitioned data is comparable with those of privacy-preserving clustering protocols in the non-distributed environments.