In this paper, we introduce a workflow that utilizes human-in-the-loop for post-editing anonymized texts, with the aim of reconciling the competing needs of data privacy and data quality. By combining the strengths of machine translation and human post-editing, our methodology facilitates the efficient and effective translation of anonymized texts, while ensuring the confidentiality of sensitive information. Our experimental results validate that this approach is capable of providing all necessary information to the translators for producing high-quality translations effectively. Overall, our workflow offers a promising solution for organizations seeking to achieve both data privacy and data quality in their translation processes.