A Model for Quan.fying Informa.on Leakage. Steven Whang, Hector Garcia Molina Stanford University
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1 A Model for Quan.fying Informa.on Leakage Steven Whang, Hector Garcia Molina Stanford University
2 Mo.va.on Insurers Test Data Profiles to Iden7fy Risky Clients Steven E. Whang 2
3 Mo.va.on How Apple and Amazon Security Flaws Led to My Epic Hacking Steven E. Whang 3
4 Mo.va.on URL Gmail Addr Gmail Name.Me Addr TwiBer Homepage Amazon CC Last 4 Digits Apple icloud Whois Billing Addr Steven E. Whang 4
5 Outline Informa.on Leakage Model Applica.ons Comparison with Exis.ng Privacy Models Steven E. Whang 5
6 Outline Informa.on Leakage Model Applica.ons Comparison with Exis.ng Privacy Models Steven E. Whang 6
7 Informa.on Leakage Mat s Home Page Steven E. Whang 7
8 Informa.on Leakage Mat s Home Page Steven E. Whang 8
9 Model p D N C C P A n 1 c 1 c 2 p 1 a 1 N C s t u n 1 c 2 n 1 c 1 p 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y N C A n 1 c 1 a 1 Steven E. Whang 9
10 Record Leakage p N C C P A n 1 c 1 c 2 p 1 a 1 r n 1 c 1 x 1 Steven E. Whang 10
11 Record Leakage p r N C C P A n 1 c 1 c 2 p 1 a 1 n 1 c 1 x 1 Pr(p, r) = p r / r = 2/3 Re(p, r) = p r / p = 2/5 L r (p, r) = F 1 (p, r) = 2 Pr Re / Pr+Re = 1/2 Steven E. Whang 11
12 Database Leakage p N C C P A n 1 c 1 c 2 p 1 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D N C n 1 c 2 s t u n 1 c 1 p 1 N C A n 1 c 1 a 1 Steven E. Whang 12
13 Database Leakage p N C C P A n 1 c 1 c 2 p 1 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D N C n 1 c 2 s t u n 1 c 1 p 1 N C A n 1 c 1 a 1 μ(t,u) A n 1 c 1 p 1 a 1 Steven E. Whang 13
14 Database Leakage p N C C P A n 1 c 1 c 2 p 1 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D N C n 1 c 2 s t u n 1 c 1 p 1 N C A n 1 c 1 a 1 L q (p, D) = max{l r (p,s), L r (p,μ(t,u))} = max{0.57, 0.89} = 0.89 μ(t,u) A n 1 c 1 p 1 a 1 Steven E. Whang 14
15 Key Features Privacy: NOT all or nothing No privacy Perfect privacy Incorporates Adversary Opera.on e.g., En.ty Resolu.on ABribute Weights Confidence Values Steven E. Whang 15
16 Outline Informa.on Leakage Model Applica.ons Comparison with Exis.ng Privacy Models Steven E. Whang 16
17 Releasing Cri.cal Informa.on p N C C P P A n 1 c 1 c 2 p 1 p 2 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D s n 1 c 2 p 2 t n 1 c 1 p 1 u v n 1 c 1 p 1 n 1 c 2 p 1 Steven E. Whang 17
18 Releasing Cri.cal Informa.on p N C C P P A n 1 c 1 c 2 p 1 p 2 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D s n 1 c 2 p 2 t n 1 c 1 p 1 u n 1 c 1 p 1 μ(t,u) n 1 c 1 p 1 Steven E. Whang 18
19 Releasing Cri.cal Informa.on p N C C P P A n 1 c 1 c 2 p 1 p 2 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D s n 1 c 2 p 2 t n 1 c 1 p 1 u n 1 c 1 p 1 μ(t,u) n 1 c 1 p 1 L d (p, DU{u}) = max{l r (p,s), L r (p, μ(t,u)} = 0.67 Steven E. Whang 19
20 Releasing Cri.cal Informa.on p N C C P P A n 1 c 1 c 2 p 1 p 2 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D s n 1 c 2 p 2 t n 1 c 1 p 1 v n 1 c 2 p 1 L d (p, DU{u}) = max{l r (p,s), L r (p, μ(t,u)} = 0.67 Steven E. Whang 20
21 Releasing Cri.cal Informa.on p N C C P P A n 1 c 1 c 2 p 1 p 2 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D s n 1 c 2 p 2 t n 1 c 1 p 1 v n 1 c 2 p 1 μ(s,t,v) N C C P P n 1 c 1 c 2 p 1 p 2 L d (p, DU{u}) = max{l r (p,s), L r (p, μ(t,u)} = 0.67 Steven E. Whang 21
22 Releasing Cri.cal Informa.on p N C C P P A n 1 c 1 c 2 p 1 p 2 a 1 M(x,y) = true if same N,C or N,P μ(x,y) = x U y D s n 1 c 2 p 2 t n 1 c 1 p 1 v n 1 c 2 p 1 μ(s,t,v) N C C P P n 1 c 1 c 2 p 1 p 2 L d (p, DU{u}) = max{l r (p,s), L r (p, μ(t,u)} = 0.67 L d (p, DU{v}) = max{l r (p,μ(s,t,v)} = 0.91 Steven E. Whang 22
23 Releasing Disinforma.on Mat s Home Page Steven E. Whang 23
24 Releasing Disinforma.on Mat s Home Page Steven E. Whang 24
25 Releasing Disinforma.on Mat s Home Page XXX Steven E. Whang 25
26 Releasing Disinforma.on Mat s Home Page XXX YYY Steven E. Whang 26
27 Releasing Disinforma.on Minimize L d (p, DUS) s.t. Cost(r) B r S Steven E. Whang 27
28 Outline Informa.on Leakage Model Applica.ons Comparison with Exis.ng Privacy Models Steven E. Whang 28
29 k anonymity Guarantees that any record is indis.nguishable from k 1 others In comparison, informa.on leakage is user specific (i.e., fine grained) can incorporate background knowledge Steven E. Whang 29
30 Example D N Z A S Alice Heart Bob Cancer Carol Flu Dave Flu Steven E. Whang 30
31 Example D N Z A S Alice Heart Bob Cancer Carol Flu Dave Flu D (k=2) Z A S 1** 3* Heart 1** 3* Cancer Steven E. Whang 31
32 Individual Informa.on Leakage D (k=2) N Z A S r 1 r 2 r 3 r 4 Z A S 1** 3* Heart 1** 3* Cancer p 1 Alice Heart Steven E. Whang 32
33 Individual Informa.on Leakage r 1 r 2 r 3 r 4 D (k=2) Z A S 1** 3* Heart 1** 3* Cancer p 1 N Z A S Alice Heart L d (p 1, D) = L r (p 1, r 1 Ur 2 ) 0.75 Steven E. Whang 33
34 Individual Informa.on Leakage r 1 r 2 r 3 r 4 D (k=2) Z A S 1** 3* Heart 1** 3* Cancer p 1 L d (p 1, D) = L r (p 1, r 1 Ur 2 ) 0.75 p 2 N Z A S Alice Heart N Z A S Carol Flu Steven E. Whang 34
35 Individual Informa.on Leakage r 1 r 2 r 3 r 4 D (k=2) Z A S 1** 3* Heart 1** 3* Cancer p 1 L d (p 1, D) = L r (p 1, r 1 Ur 2 ) 0.75 p 2 N Z A S Alice Heart N Z A S Carol Flu L d (p 2, D) = L r (p 2, r 3 Ur 4 ) 0.86 Steven E. Whang 35
36 Background Knowledge D (k=2) Z A S r 1 r 2 r 3 r 4 1** 3* Heart 1** 3* Cancer s 1 D N Z A Alice Steven E. Whang 36
37 Background Knowledge r 1 r 2 r 3 r 4 D (k=2) Z A S 1** 3* Heart 1** 3* Cancer N Z A S p 1 Alice Heart L d (p 1, D) = L r (p 1, r 1 Ur 2 ) 0.75 s 1 D N Z A Alice Steven E. Whang 37
38 Background Knowledge r 1 r 2 r 3 r 4 D (k=2) Z A S 1** 3* Heart 1** 3* Cancer N Z A S p 1 Alice Heart L d (p 1, D) = L r (p 1, r 1 Ur 2 ) 0.75 s 1 L d (p 1, D U D ) D N Z A Alice Steven E. Whang 38
39 Background Knowledge r 1 r 2 r 3 r 4 D (k=2) Z A S 1** 3* Heart 1** 3* Cancer p 1 s 1 N Z A S Alice Heart L d (p 1, D) = L r (p 1, r 1 Ur 2 ) 0.75 D N Z A Alice L d (p 1, D U D ) = L r (p 1, r 1 Ur 2 Us 1 ) 0.86 Steven E. Whang 39
40 l diversity Other Comparison Seman.c Similarity Steven E. Whang 40
41 Conclusion We formalized informa.on leakage Privacy: NOT all or nothing We listed several applica.ons for quan.fying informa.on leakage We compared informa.on leakage with exis.ng privacy models In paper, we propose efficient algorithms for compu.ng leakage Steven E. Whang 41
42 Thanks! Steven E. Whang 42
43 l diversity D N Z A S Alice Heart Bob Cancer Carol Flu Dave Flu D (k=2) Z A S 1** 3* Heart 1** 3* Cancer Steven E. Whang 43
44 l diversity D N Z A S Alice Heart Bob Cancer Carol Flu Dave Influenza D (l=2) Z A S 1** 3* Heart 1** 3* Cancer 2** 50 Influenza Steven E. Whang 44
45 Seman.c Similarity D (l=2) N Z A S r 1 r 2 r 3 r 4 Z A S 1** 3* Heart 1** 3* Cancer 2** 50 Influenza p Carol Flu Steven E. Whang 45
46 Seman.c Similarity r 1 r 2 r 3 r 4 D (l=2) Z A S 1** 3* Heart 1** 3* Cancer 2** 50 Influenza p N Z A S Carol Flu L d (p, D) = L r (p, r 3 Ur 4 ) 0.75 Steven E. Whang 46
47 Seman.c Similarity r 1 r 2 r 3 r 4 D (l=2) Z A S 1** 3* Heart 1** 3* Cancer 2** 50 Influenza p N Z A S Carol Flu L d (p, D) = L r (p, r 3 Ur 4 ) 0.75 If merge func.on μ replaces Influenza with Flu, Steven E. Whang 47
48 Seman.c Similarity r 1 r 2 r 3 r 4 D (l=2) Z A S 1** 3* Heart 1** 3* Cancer 2** 50 Influenza p N Z A S Carol Flu L d (p, D) = L r (p, r 3 Ur 4 ) 0.75 If merge func.on μ replaces Influenza with Flu, L d (p, D) = L r (p, r 3 Ur 4 {<S, Influenza>}) 0.86 Steven E. Whang 48
49 Seman.c Similarity r 1 r 2 r 3 r 4 D (l=2) Z A S 1** 3* Heart 1** 3* Cancer 2** 50 Influenza p N Z A S Carol Flu L d (p, D) = L r (p, r 3 Ur 4 ) 0.75 If merge func.on μ replaces Influenza with Flu, L d (p, D) = L r (p, r 3 Ur 4 {<S, Influenza>}) 0.86 Steven E. Whang 49
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