The increasing demand to extend data mining technology to data sets inherently distributed among a large number of autonomous and heterogeneous sources over a network with limited bandwidth has motivated the development of several approaches to distributed data mining and knowledge discovery, of which only a few make use of agents. We briefly review existing approaches and argue for the potential added value of using agent technology in the domain of knowledge discovery, discussing both issues and benefits. We also propose an approach to distributed data clustering, outline its agent-oriented implementation, and examine potential privacy violating attacks in which agents may incur.