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Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned DataAuthor: M. Kantarcioglu and C. CliftonSource: IEEE Trans. on Knowledge and Data Engineering, vol. 16, no. 9, pp. 1026-1037, 2004Speaker: Yu-Chiang LiDate: November 26, 2004
OutlineIntroductionBackground and Related WorkSecure Association Rule MiningSecurity against CollusionDifficulties with the Two-Party CaseCommunication and Computation CostsConclusions
IntroductionGoal: Produce association rules while limiting the information shared about each siteProtects individual data privacy and discoveries global rulesNo site should be able to learn contents of a transaction at any other site
Related WorkFDM: Fast Distributed Mining of association rulesEx: 150 transactionsminSup=10%{A, B, C} {B, C, D} {E, F, G, H}Candidates of local large Candidates of global large
Secure Association Rule Mining
{A, B, C, D, E, F, G, H}
E1({AB, AC, BC, ...})
3
E2({BC, BD, CD,...})
E3({EF, EG, EH, FG, FH, GH,...})
{KL, KM, KN, KO, KP,...}
2
1
E2E1({AB, AC, BC, ...})
E3E2E1({AB, AC, BC, ...})
E3E2({BC, BD, CD,...})
E1E3E2({BC, BD, CD,...})
E3E2E1({AB, AC, BC, ...})
E3E2E1({AB, AC, BC, BD, CD,...})
{ABC:8}
{ABC:6}
3
R=17
S1=17+4-50*10% =16
S2=16+6-50*10% =17
2
1
S3=17+8-50*10% =20
|DB1| = |DB2| = |DB3| =50minSup = 10%
If S3>R Then {ABC} is large
{ABC:4}
ConclusionsDistributed association rule mining can be done efficiently under reasonable security assumptionsAllowing error in mining results may enable more efficient algorithm that maintain the desired level of securityTo predict the value of information for a particular organization, allowing trade off between disclosure cost, computation cost, and benefit from the result
L1C2C2L2C3C3L3Apriori
TIDItems100A C D200B C E300A B C E400B E
ItemsetSup.{A}2{B}3{C}3{D}1{E}3
ItemsetSup.{A}2{B}3{C}3{E}3
Itemset{A B}{A C}{A E}{B C}{B E}{C E}
ItemsetSup.{A B}1{A C}2{A E}1{B C}2{B E}3{C E}2
ItemsetSup.{A C}2{B C}2{B E}3{C E}2
Itemset{B C E}
ItemsetSup.{B C E}2
ItemsetSup.{B C E}2