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COMP 7370 Advanced Computer and Network Security The VectorCover Algorithm (2). Dr. Xiao Qin Auburn University http://www.eng.auburn.edu/~xqin [email protected]. Spring, 2011. Minimal Distance Vectors. The Outlier Set and All Set. Outliers: Tuples which have less than k occurrences - PowerPoint PPT Presentation
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Dr. Xiao Qin
Auburn Universityhttp://www.eng.auburn.edu/~xqin
Spring, 2011
COMP 7370 Advanced Computer and Network Security
The VectorCover Algorithm (2)
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Minimal Distance Vectors
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The Outlier Set and All Set
• Outliers: Tuples which have less than k occurrences
• All: a set of distinct tuples in a table
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Pair – (strategy, tuples)
• New data structure
• Represents a transformation strategy
• Represents a set of tuples after applying such a transformation.
• Strategy = Distrance Vectors
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Distance between Two Tuples
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The VectorCover Algorithm
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Dr. Xiao Qin
Auburn Universityhttp://www.eng.auburn.edu/~xqin
Spring, 2011
COMP 7370 Advanced Computer and Network Security
The MinGen Algorithm
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Step 1: PT vs. PT[QI]
vs.
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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.
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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:
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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
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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?
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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.
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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])
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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.
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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]
Step 6.2
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Step 6.3
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