  This study investigates the optimal selection of parameters for collaborative
clustering while ensuring data privacy. We focus on key clustering algorithms
within a collaborative framework, where multiple data owners combine their
data. A semi-trusted server assists in recommending the most suitable
clustering algorithm and its parameters. Our findings indicate that the privacy
parameter ($\epsilon$) minimally impacts the server's recommendations, but an
increase in $\epsilon$ raises the risk of membership inference attacks, where
sensitive information might be inferred. To mitigate these risks, we implement
differential privacy techniques, particularly the Randomized Response
mechanism, to add noise and protect data privacy. Our approach demonstrates
that high-quality clustering can be achieved while maintaining data
confidentiality, as evidenced by metrics such as the Adjusted Rand Index and
Silhouette Score. This study contributes to privacy-aware data sharing, optimal
algorithm and parameter selection, and effective communication between data
owners and the server.
