Can anyone give an example for this penalty in classification metric? I have this in my Data Privacy course. (It is one of the challenges in k-anonymity)

2.4.1.5.5 Utility/Data Quality The utility of anonymized data is always measured/assessed with respect to the utility provided by the original data. The utility of anonymized data is a function of the anonymization algorithm and the intended use of anony- mized data. Therefore, utility measure should be determined in the context of the application and the anonymization technique used. With respect to k-anonymization, some of the utility/data quality metrics are Information loss metric (LM) 17] Classification Metric (CM) [ TABLE 2.18 Ex ample of Homogeneity Attack Zip Code Salary 560000 ANY_DEGREE 15,000 560000 ANY_DEGREE 20,000 560000ANY_DEGREE 20,000 560000ANY_DEGREE 20,000 Gender Education ANY_SEX ANY_SEX ANY_SEX ANY_SEX Classification metric (CM) is defined as the sum of the individual penalties for each row in the table normalized by the total number of rows penalty (row r) CM All Rows (2.2) A row r is penalized if it is suppressed or if its class label class(r) is not the majority class label (G) of its group G 1 if r suppressed 1 if class(r) ^ majority (G(r Penalty (row r) class(r) majority (G(r) 0 otherwise 2.4.1.5.5 Utility/Data Quality The utility of anonymized data is always measured/assessed with respect to the utility provided by the original data. The utility of anonymized data is a function of the anonymization algorithm and the intended use of anony- mized data. Therefore, utility measure should be determined in the context of the application and the anonymization technique used. With respect to k-anonymization, some of the utility/data quality metrics are Information loss metric (LM) 17] Classification Metric (CM) [ TABLE 2.18 Ex ample of Homogeneity Attack Zip Code Salary 560000 ANY_DEGREE 15,000 560000 ANY_DEGREE 20,000 560000ANY_DEGREE 20,000 560000ANY_DEGREE 20,000 Gender Education ANY_SEX ANY_SEX ANY_SEX ANY_SEX Classification metric (CM) is defined as the sum of the individual penalties for each row in the table normalized by the total number of rows penalty (row r) CM All Rows (2.2) A row r is penalized if it is suppressed or if its class label class(r) is not the majority class label (G) of its group G 1 if r suppressed 1 if class(r) ^ majority (G(r Penalty (row r) class(r) majority (G(r) 0 otherwise