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

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

View Full Document Right Arrow Icon
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
Background image of page 1

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

View Full DocumentRight Arrow Icon
2 Step 1: PT vs. PT[QI] vs.
Background image of page 2
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.
Background image of page 3

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

View Full DocumentRight Arrow Icon
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:
Background image of page 4
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
Background image of page 5

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

View Full DocumentRight Arrow Icon
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
Background image of page 6
7 Step 3.1: Check single attributes Each single attribute must satisfy k-anonymity
Background image of page 7

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

View Full DocumentRight Arrow Icon
Background image of page 8
Background image of page 9

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

View Full DocumentRight Arrow Icon
Background image of page 10
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: If 4 &lt; 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 -&gt; MGT[E] Value v = a -&gt; freq(a, MGT[E]) = ? If 4 &lt; k then what does this mean? V_E = [d_E, d_Z] = [1, 0] MGT &lt;- generalize(MGT, V_E, [0,0]) 4 9 Step 3.2: the generalize() function Each single attribute must satisfy k-anonymity MGT &lt;- 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

This note was uploaded on 12/07/2011 for the course COMP 7370 taught by Professor Qin,x during the Summer '08 term at Auburn University.

Page1 / 10

Lec12b-anonymity5 - If 4 &amp;amp;lt; k then we need data...

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online