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L- diversity for Privacy-Preserving Traffic Padding in Web- Based Applications

L- diversity for Privacy-Preserving Traffic Padding in Web- Based Applications. DEMO PRESENTATION. L-DIVERSITY FOR PPTD in WEBAPP. Before -diversity. After -diversity. L-DIVERSITY FOR PPTD in WEBAPP (CNTD). Table T . L-DIVERSITY FOR PPTD in WEBAPP (CNTD). Table T .

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L- diversity for Privacy-Preserving Traffic Padding in Web- Based Applications

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  1. L-diversityfor Privacy-PreservingTrafficPadding in Web-Based Applications DEMO PRESENTATION

  2. L-DIVERSITY FOR PPTD in WEBAPP Before -diversity After -diversity

  3. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) Table T

  4. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) Table T

  5. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) Let r = Index.first() and form a group Table T

  6. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) (2) Compute padding_cost_matrix(T|r) 8 7 1 9 5 2 3 6 4 8 Table T

  7. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) (3) Find the padding priority Q of 8 w.r.t the ungrouped records of T 8 7 1 9 5 2 3 6 4 8 Table T 3  1 Q = 9  7 2 5  6  4

  8. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 1st round (4) Repeat until equivalence class satisfies l-diversity { 3  1 Q = 9  7 2 5  6  4 • diversity_first = diversity_basis() diversity_basis () = = = 1 diversity_basis () = = 1 (is not l-diverse) Table T Remaining records RR= {7,1,9,5,2,3,6,4} diversity_basis (RR) = = = 0.19 ( thereis l-diversity)

  9. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 1st round (4) Repeat until equivalence class satisfies l-diversity { v 3  1 Q = 9  7 2 5  6  4 diversity_basis () = • pop the head vector v from Q Table T

  10. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 1st round (4) Repeat until equivalence class satisfies l-diversity { v 3  1 Q = 9  7 2 5  6  4 diversity_first = • temp = + v temp = {8, 9} • diversity_second = diversity_basis(temp) diversity_second = = 0.81 Table T

  11. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 1st round (4) Repeat until equivalence class satisfies l-diversity { v 3  1 Q = 9  7 2 5  6  4 diversity_first = temp = {8, 9} diversity_second = 0.81 • if (diversity_second < diversity_first) • { = + v • Mark v with assigned sign } diversity_basis () = = 0.81 (is not l-diverse) Table T

  12. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 1st round (4) Repeat until equivalence class satisfies l-diversity { v 3  1 Q = 7 2 5  6  4 diversity_first = temp = {8, 9,7} diversity_second = 0.47 diversity_second > diversity_first } diversity_basis () = = 0.47 (is l-diverse Table T

  13. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 1st round done (5) Copy to T’ done (6) Mark r and ’s elemetnswith assigned sign done (7) Mark ’s elements in T’ with r (8) Remove ’s elements from Index } 8 8 8 Table T Table T' 9 8 7 Index = 1 5 2 3 6 4

  14. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) done (1)Let r = Index.first() and form a group done 2nd round (2) Compute padding_cost_matrix(T|r) (3)Find the padding priority Q of 1w.r.t the ungrouped records of T done Q = 3 2 5  6  4 (4) Repeat until equivalence class satisfies l-diversity { . . . . . . . . . . } Table T diversity_basis () = = 0.41 (is l-diverse) Index = 1 5 2 3 6 4

  15. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 2nd round done (5) Copy to T’ done (6) Mark r and ‘s elements with assigned sign done (7) Mark ’s elements in T’ with r (8) Remove ’s elements from Index } done done done 20 100 1 1 3 40 1 120 Table T 36 2 130 1 2 Index = 5 6 4 1 3 Table T'

  16. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) done (1)Let r = Index.first() and form a group done 3rd round (2) Compute padding_cost_matrix(T|r) done (3)Find the padding priority Q of 1w.r.t the ungrouped records of T done Q = 6  4 done (4) Repeat until equivalence class satisfies l-diversity { done . . . . . . . . . . } Table T diversity_basis () = = 0.43 (is l-diverse) Index = 5 6 4

  17. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) 3rdround done done done done done done done done done 20 135 5 1 3 40 5 140 Table T 36 2 NULL 150 5 6 Index = 5 4 Table T'

  18. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) done done done done done done done done done done 5 25 135 5 6 42 140 5 Table T 4 50 150 5 Table T'

  19. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) done done done done done done done done done done 5 25 135 5 6 42 140 5 Table T 4 50 150 5 Table T'

  20. L-DIVERSITY FOR PPTD in WEBAPP (CNTD) done done done 90 done 90 90 done 130 done done done 130 done 130 done 150 150 Table T 150 Table T'

  21. L-DIVERSITY FOR PPTD in WEBAPP (AXIOM [Minimum Padding Cost]) T is l-diverse (diversity_basis (T) = 0.19 ) but,.. Total Padding cost = 95 Total Padding cost = 335 pc = 30 pc = 335 pc = 40 pc = 25 Table T Table T' Adopt the algorithm is more economical even if the dataset is initially diverse pc = padding cost

  22. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) Now suppose that after the 2nd round we had : done done done done done done Table T' diversity_basis () = = 0.52 (is not l-diverse) Table T So steps (5), (6), (7) and (8) can’t be applied

  23. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) done else remove Index.first() from Index done done done done done Table T 6 4 Index = 5

  24. L-DIVERSITY FOR PPTD in WEBAPP (ASSIGN( ) function) done (1)Let r =Index.first() and form a group done 4th round (2) Compute padding_cost_matrix(T|r) done (3)Find the padding priority Q of 6w.r.t the ungrouped records of T done Q = 4 done (4) Repeat until equivalence class satisfies l-diversity { done . . . . . . . . . . } Table T diversity_basis () = = 0.64 (is not l-diverse) 4 Index = 6

  25. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) done 4th round else remove Index.first() from Index done done done done done Table T 4 Index = 6

  26. L-DIVERSITY FOR PPTD in WEBAPP (ASSIGN( ) function) done (1)Let r = Index.first() and form a group done 5th round done (2)Find the padding priority Q of 4 w.r.t the ungrouped records of T done Q = NULL done (3) Repeat until equivalence class satisfies l-diversity { done . . . . . . . . . . } Table T diversity_basis () = = 1 (is not l-diversified) 4 Index =

  27. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) done 5th round else remove Index.first() from Index done done done done done Table T NULL Index = 4

  28. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) • = { remaining unassigned records of T} done done done done done done Table T

  29. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) • for each record v in{ record 5 • for each group in T’{ - group 8 : c_ diversity_basis (5 | 8) = 0.36 - group 1 : c_ diversity_basis (5 | 1) = 0.36 • } • Compute min_pc(Candidate | 5) min_pc(Candidate | 5 ) = {1} • Find the perfect candidate Table T‘ The perfect candidate for 5 is group 1

  30. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) • Add 5 to group 1 done • Mark v in T with assigned 5 15 135 1 Table T‘

  31. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) • for each record v in{ record 6 • for each group in T’{ - group 8 : c_ diversity_basis (6 | 8) = 0.32 - group 1 : c_ diversity_basis (6 | 1) = 0.30 • } • Compute min_pc(Candidate | 6) min_pc(Candidate | 6 ) = {1} • Find the perfect candidate The perfect candidate for 6 is group 1 Table T‘

  32. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) • Add 6 to group 1 done • Mark v in T with assigned 6 22 140 1

  33. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) • for each record v in{ record 4 • for each group in T’{ - group 8 : c_ diversity_basis (4 | 8) = 0.46 - group 1 : c_ diversity_basis (4 | 1) = 0.23 • } • Compute min_pc(Candidate | 4) min_pc(Candidate | 4 ) = {1} • Find the perfect candidate The perfect candidate for 4 is group 1

  34. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) • Add 6 to group 1 done • Mark v in T with assigned 4 40 150 1

  35. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) done 4 40 150 1

  36. L-DIVERSITY FOR PPTD in WEBAPP(ASSIGN( ) function) done Total padding cost = 155 < 355 90 90 90 150 150 150 150 150 4 40 150 1

  37. References : - Privacy-Preserving Traffic Padding in Web-Based Applications Wen Ming Liu, Lingyu Wang, Pengsu Cheng and MouradDebbabi - An Improved V-MDAV Algorithm for l-Diversity Han Jian-min, Cen Ting-ting and Yu Hui-gun - A Rough Set Based Efficient l-diversity Algorithm B. K. Tripathy1, G. K. Panda2* and K. Kumaran3; • Thanks for visiting the demo • Feedbacks will be welcome

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