Abstract
Privacy and security have always been a concern that prevents the sharing of data and impedes the success of many projects. Distributed knowledge computing, if done correctly, plays a key role in solving such a problem. The main goal is to obtain valid results while ensuring the non-disclosure of data. Density-based clustering is a powerful algorithm in analyzing uncertain data that naturally occur and affect the performance of many applications like location-based services. Nowadays, a huge number of datasets have been introduced for researchers which involve high-dimensional data points with varying densities. Such datasets contain data points with high-density regions surrounded by data points with sparse density. The existing clustering approaches handle these situations inefficiently, especially in the context of distributed data. In this paper, we design a new decomposable density-based clustering algorithm for distributed datasets (DDBC). DDBC utilizes the concept of mutual k-nearest neighbor relationship to cluster distributed datasets with different density. The proposed DDBC algorithm is capable of preserving the privacy and security of data on each site by requiring a minimal number of transmissions to other sites.