A challenging task in privacy protection for public data is to realize an algorithm that generalizes a table according to a user's requirement. In this paper, we propose an anonymization scheme for generating a k-anonymous table, and show evaluation results using three different tables. Our scheme is based on full-domain generalization and the requirements are automatically incorporated into the generated table. The scheme calculates the scores of intermediate tables based on user-defined priorities for attributes and selects a table suitable for the user's requirements. Thus, the generated table meets user's requirements and is employed in the services provided by users without any modification or evaluation.