ITU‐T's Technical Reports and Specifications 731 Table 7 – Anonymized medical record Birth Gender ID Problem 1970 Male 121 Cold 1970 Male 121 Obesity 1970 Male 121 diabetes 198* Human 12* diabetes 198* Human 12* Obesity 198* Human 12* diabetes 198* Human 12* Cold As displayed in these tables, the masking or generalization processes prevent an attacker from identifying a specific person. There are several algorithms for calculating masking or generalization. The most popular algorithm is the heuristic searching method, utilizing double‐nested loops. (2) Diversity Diversity is a method designed to protect the privacy of data68. This method considers the diversity of sensitive attributes, and it is, therefore, different from ‐anonymity. The definition of ‐diversity is as follows: \"In all q*‐blocks in a data table, there are at least l different sensitive attributes.\" Researchers designed this method to provide protection from the following attacks. (i) Homogeneity attack: Table 8 is an additional example of a medical record data table. In this case, if an attacker has acquired Alice's quasi‐identifier, the attacker can read Alice's problem from this table because no diversity exists for the sensitive attributes in the q*‐block. (ii) Background knowledge attack: Although theq*‐block in the table has a diversity of sensitive attributes, if the probability of poor circulation is very low for males and an attacker is aware of that, the attacker can read Bob's problem from the table. l‐diversity provides more security than ‐anonymity for preserving privacy. However, the calculation cost of l‐diversity is higher than anonymity. ____________________ 68 Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, Muthuramakrishnan Venkitasubramaniam; L‐diversity: Privacy beyond k‐anonymity; ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 1, No. 1, 2007.