In this paper, we are considering the privacy/utility trade-off in privacy-preserving RDF data publishing. Starting from our recent utility-focused work on semantic anatomy, which prevents the disclosure of new information about groups of individuals, we are enriching the framework with the well-established k-anonymity approach. We propose two algorithms which differ on the set of individuals used to perform the k-anonymity. The integration of this privacy method allows us to study the interaction between privacy and utility. Our evaluation emphasizes that the combination of anatomy and k-anonymity preserves the utility qualities of our previous solution and increases the privacy of released data sets.