1. 1 Dr. Xiao Qin Auburn University http://www.eng.auburn.edu/~xqin xqin@auburn.edu Spring, 2011 COMP 7370 Advanced Computer and Network Security The MinGen Algorithm

2. 2 Step 1: PT vs. PT[QI] vs.

3. 3 Step 2: history <- [d_1, … d_n] n =2 E_0 -> d_1 = 0 Z_0 -> d_2 = 0 E_1 -> d_1 = ? Z_2 -> d_2 = ? E_1 -> d_1 = 1 Z_2 -> d_2 = 2 Use subscripts to represent generalization strategies.

4. 4 Step 2: history <- [d_1, … d_n] Note: E_i and Z_j must be specific when you implement the MinGen algorithm. You must specify your generalization strategies. For example:

5. 5 Step 2: E_i, Z_j n =2 E_0 -> d_1 = 0 Z_0 -> d_2 = 0 E_1 -> d_1 = ? Z_2 -> d_2 = ? E_1 -> d_1 = 1 Z_2 -> d_2 = 2

6. 6 Step 3: Check single attributes Each single attribute must satisfy k-anonymity E -> MGT[E] v = a -> freq(a, MGT[E]) = ? If 4 < k then what does this mean? What should we do? 4

7. 7 Step 3.1: Check single attributes Each single attribute must satisfy k-anonymity If 4 < k then we need data generalization! V_E = [d_E, d_Z] = [1, 0] not [0, 1] Note: move one step at a time. 4

8. 8 Step 3.2: the generalize() function Each single attribute must satisfy k-anonymity E -> MGT[E] Value v = a -> freq(a, MGT[E]) = ? If 4 < k then what does this mean? V_E = [d_E, d_Z] = [1, 0] MGT <- generalize(MGT, V_E, [0,0]) 4

9. 9 Step 3.2: the generalize() function Each single attribute must satisfy k-anonymity MGT <- generalize(MGT, v, h) Generalize() transform MGT based on a generalization strategy specified by v, h.

10. 10 Step 3.3: update the history vector Each single attribute must satisfy k-anonymity Can you give me an example to illustrate how step 3.3 works? History [d_E, d_Z] = [0, 0] V_E = [1, 0] New History [0, 0] + [1, 0] = [1, 0]