The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms create anonymous tables using only the microdata table (MT) to be published, independently of the external knowledge available. This omission leads to high information loss. Motivated by this observation we first introduce the concept of k-join-anonymity (KJA), which permits more effective generalization to reduce the information loss. Briefly, KJA anonymizes a superset of MT, which includes selected records from PD. We propose two methodologies for adapting k-anonymity algorithms to their KJA counterparts. The first generalizes the combination of MT and PD, under the constraint that each group should contain at least one tuple of MT (otherwise, the group is useless and discarded). The second anonymizes MT, and then refines the resulting groups using PD. Finally, we evaluate the effectiveness of our contributions with an extensive experimental evaluation using real and synthetic datasets.