{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Lec12b-anonymity5

# Lec12b-anonymity5 - If 4< k then we need data...

This preview shows pages 1–10. Sign up to view the full content.

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

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 Step 1: PT vs. PT[QI] vs.
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.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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 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

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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 Step 3.1: Check single attributes Each single attribute must satisfy k-anonymity

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 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 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 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 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]...
View Full Document

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern