730 ITU‐T's Technical Reports and Specifications (1) Anonymity Anonymity is one of the methods utilized for generalization67, and it is the base of l‐diversity. Further explanation of this method will incorporate the various definitions listed below. (i) Data table: A data list similar to a database table is termed a \"data table.\" Its column is termed an \"attribute.\" Address, birth, and gender are examples of attributes. One group of data corresponding to the person or group of people is termed a \"data set\" and one data set is termed a \"tuple\". (ii) Attribute: An attribute among a group of related attributes that can identify a corresponding person by itself, such as name or unique ID, is termed an \"identifier,\" and others that cannot identify a group on their own, however, it can provide identification when combined with other attributes, such as illness, birth, gender, is termed a \"quasi‐identifier\". (iii) Sensitive attribute: A significant attribute for secondary use is termed a \"sensitive attribute,\" which can be selected from attributes that are not identifiers. The method will exclude this attribute from masking or generalization by anonymization. Furthermore, tuple groups that have the same quasi‐identifier values are termed \"q*‐block\". The definition of k‐anonymity is as follows: \"In each q*‐block in the data table, at least k tuples are included\". Table 6 represents an example of a medical records data table. In this table, the sensitive attribute is \"Problem\" and the quasi‐identifiers are \"Birth,\"\"Gender,\" and \"ID.\" The data consists of a t1~ t3 q*‐block, a t4, t5 q*‐block, and a t6, t7 q*‐block. It represents k=2. Even if an attacker attempts to ascertain a specific individual's problem and has already obtained the individual's quasi‐identifier, the attacker can narrow the results down to only two tuples. Table 7 indicates that the anonymization results from Table 6 are k=3. The results displayed in this table demonstrate that anonymization methods provide the required privacy protection level, utilizing masking or generalization. Table 6 – Medical record Birth Gender ID Problem 1970 Male 121 Cold 1970 Male 121 Obesity 1970 Male 121 Diabetes 1980 Female 121 Diabetes 1980 Female 121 Obesity 1981 Male 125 diabetes 1981 Male 125 Cold ____________________ 67 L. Seeney; K‐anonymity: A model for protecting privacy; International Journal on Uncertainty, Fuzziness and Knowledge‐based Systems, vol. 10, no. 5, pp. 557–570, 2002.