Consider the dataset below. Using k-anonymization and the two techniques discussed in class (generalization and suppression), create an anonymized dataset with k-2 and the QI of Postal Code, Age, and Sex. That is, names should be removed and income should not be modified. The remaining three variables should be sufficiently modified so that anonymity is preserved for k=2. Income (In Name Postal Code Age Sex $,000) Lowery N2L 7D4 46 F 17 Acevedo NOB 52 F 76 Freeman NOB 3A3 48 M 38 Mckee NOB 3A4 26 M Gonzales N2L 7D6 39 M Peterson N2L 7D3 47 F Avila NOB 3A1 42 M Shields N2L 7D5 26 M Gonzalez NOB 3A2 45 F Mcdowell N2L 7D7 43 F 28 84 61 89 47 98 37 Consider the dataset below. Using k-anonymization and the two techniques discussed in class (generalization and suppression), create an anonymized dataset with k-2 and the QI of Postal Code, Age, and Sex. That is, names should be removed and income should not be modified. The remaining three variables should be sufficiently modified so that anonymity is preserved for k=2. Income (In Name Postal Code Age Sex $,000) Lowery N2L 7D4 46 F 17 Acevedo NOB 52 F 76 Freeman NOB 3A3 48 M 38 Mckee NOB 3A4 26 M Gonzales N2L 7D6 39 M Peterson N2L 7D3 47 F Avila NOB 3A1 42 M Shields N2L 7D5 26 M Gonzalez NOB 3A2 45 F Mcdowell N2L 7D7 43 F 28 84 61 89 47 98 37