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Next we execute the query SELECT COUNT(*) FROM Patient GROUP BY Race, Sex. The patients that are Black and Male or Female are no longer in groups of size k ≥ 2, so we execute UPDATE Patient SET Male=* AND Female=* WHERE Race=Black and the number of values that have to be suppressed = 3. Now we execute SELECT COUNT(*) FROM Patient GROUP BY Race, Sex, Native Country and continue in the same way until we group by all the columns in permutation order. Given that the total possible number of values that could have been suppressed is n×m (the number of rows multiplied by the number of columns), the fitness for permutation 5 6 7 3 4 0 1 2 = 10 × 8 − (0 + 3 + 3 + 7 + 7 + 10 + 5 + 8) = 80 − 43 = 37, which is the number of entries in the resulting k-anonymized version of D that are not suppressed, as shown in Figure 3.

We also modify the EnforceAnonymityHelper pseudocode (see Figure 11) and the crossover and mutation operators (see Figure 12). The modified helper algorithm works in the following way. For each possible value of each domain, lines 1–6 of Figure 11 check if suppression is necessary. Then, in line 7, nodes with matching values are merged and their counts are updated. Table 2. Ordered Greed Framework Implementation Details Population size Number of runs Termination criteria Selection Crossover Replacement Mutation Mutation rate 1.

If this entry corresponds to a leaf node 13. c = COUNT(*) 14. else 15. c = 0 16. currentNode = new node (ID=this entry, count=c, children=null, parent=root) 17. addChild(currentNode) 18. root = currentNode 19. propagateCounts(root) 20. EnforceAnonymity(root, k) 21. setNumSuppressions(root) 22. fitness = numRecords * numCols - numSuppressions 23. return(fitness) Fig. 9. Pseudocode for Datafly Algorithm (a) 1. 2. 3. 4. 5. 6. 7. getChildren() for each child EnforceAnonymity(child, k) (b) 1. minIndex = 0, totUnderk = 0, minOverk = INFINITY 2.

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