There are several reasons for organizations to publish or share their data. Therefore, ensuring the privacy of individual information is a serious issue. A typical medical organization must publish data about thousands of patients that contain detailed information about each patient. There may be several vulnerable relationships in the data that may lead to identities being exposed. For example, certain diseases are usually associated with groups of a particular age, gender, location, or ethnicity. Data exposure is not limited to specific types of attacks; attackers often try to find vulnerable relationships between data that may lead to exposure of identities. Therefore, the clustering method must be used to find more relationships between large amounts of data. The model provided in this paper aims to improve the concept of data anonymity by proposing an anonymization method that focuses on critical relationships between data. The main idea behind MR-Anonymization is to apply the clustering technique in order to find leakages in such a large dataset.