measuring ocal opological anonymity in social …anonimized graph, g tar (anonimized export, e.g.,...
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MEASURING LOCAL TOPOLOGICAL
Brussels
December 10, 2012
PinSoDa: Privacy in Social Data Workshopin conjunction with the 11th IEEE International Conference on Data Mining (ICDM 2012)
ANONYMITY IN SOCIAL NETWORKS
Gábor György Gulyás and Sándor Imre
Dept. of Telecommunications (BME)
{gulyasg, imre}@hit.bme.hu
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Anonimized graph, Gtar
(anonimized export, e.g., Twitter)
Anonymous exports and private information?
Auxiliary information, Gsrc
(a public crawl, e.g., Flickr)
Measuring Local Topological Anonymity in Social Networks 2© Gábor György Gulyás, Sándor Imre
RepublicanDemocratic
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Primary attack types
Active attacks1 Passive attacks2
Measuring Local Topological Anonymity in Social Networks 3© Gábor György Gulyás, Sándor Imre
1 Backstrom et al.: Wherefore Art Thou R3579X? Anonymized Social Networks,
Hidden Patterns, and Structural Steganography (2007) 2 Narayanan & Shmatikov: De-anonymizing social networks (2009)
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Primary attack types
Active attacks1 Passive attacks2
Not always possible, e.g,
• data is unavailable prior to anonimization,
• creating links requires mutual confirmation.
Or it may not be feasible, e.g.,
• it is expensive to create new nodes, links (phone calls),
Measuring Local Topological Anonymity in Social Networks 4© Gábor György Gulyás, Sándor Imre
• it is expensive to create new nodes, links (phone calls),
• it is too slow to be done,
etc.
Plus it is more limited than identifying existing strutures.
1 Backstrom et al.: Wherefore Art Thou R3579X? Anonymized Social Networks,
Hidden Patterns, and Structural Steganography (2007) 2 Narayanan & Shmatikov: De-anonymizing social networks (2009)
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Re-identification example
Gsrc: Gtar:
� The attacker uses nodedegrees.Anonymity sets:{D} {A, G} {C, H} {B, E, F}
� Dave is globally unique: Dave ↔ 3
� But what about Harry?• He is in {H, C}
• Relatively to Dave, i.e., (D,*)∈E, anon. sets:{A, G}, {B, E}, {H}
• Harry is locally unique:
Harry ↔ 1
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Global re-identification phase(a.k.a. seed identification phase)
Gsrc: Gtar:
A B C D E F G H
d(vi) 1 3 2 5 3 3 1 2
A(vi) 1/2 2/3 1/2 0 2/3 2/3 1/2 1/2
� Problems:• Not feasible for large
networks
• Limited de-anonymizationrate
• Most of the nodes are notglobally outstanding
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Anonymity sets: {D} {A, G} {C, H} {B, E, F}
Measuring anonymity:
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Local re-identification phase(a.k.a. propagation identification phase)
Gsrc: Gtar:
� Local TopologicalAnonymity (LTA)• User: privacy status
estimation
• Data providers (and attackers): estimation of thesuccess of an attack
Measuring Local Topological Anonymity in Social Networks 7© Gábor György Gulyás, Sándor Imre
� Problem:anonymity sets depend onseed locations
� How to measure a priori anonymity?
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How do propagation phases work?
� State-of-the-art algorithm:
Narayanan, A., Shmatikov, V.: De-anonymizing social networks.
In: 30th IEEE Symposium on Security and Privacy, pp. 173-187,
IEEE Computer Society, Washington (2009)
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How do propagation phases work? (2)
� Need seeds as an initial mapping (Gsrc � Gtar)
� Round based: tries to extend mapping in each round
• Unmapped source nodes are structurally compared to
unmapped targed nodes
• Comparison involves their mapped neighbors and their
degree values
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Gsrc Gtar
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How do propagation phases work? (3)
� Need seeds as an initial mapping (Gsrc � Gtar)
� Round based: tries to extend mapping in each round
• Unmapped source nodes are structurally compared to
unmapped targed nodes
• Comparison involves their mapped neighbors and their
degree values
Measuring Local Topological Anonymity in Social Networks 10© Gábor György Gulyás, Sándor Imre
Gsrc Gtar
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How do propagation phases work? (4)
� Need seeds as an initial mapping (Gsrc � Gtar)
� Round based: tries to extend mapping in each round
• Unmapped source nodes are structurally compared to
unmapped targed nodes
• Comparison involves their mapped neighbors and their
degree values
Measuring Local Topological Anonymity in Social Networks 11© Gábor György Gulyás, Sándor Imre
Gsrc Gtar
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How do propagation phases work? (5)
� Future algorithms are likely to share these principles
� Node comparison yields success if
• a source node has an instance in the target graph,
• the source node and its target instance are similar enough,
• and the target instance is outstanding to its „competitors”.
⇒ this property can be captured by an a priori anonymity measure!
Measuring Local Topological Anonymity in Social Networks 12© Gábor György Gulyás, Sándor Imre
⇒ this property can be captured by an a priori anonymity measure!
Gsrc Gtar
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Local Topological Anonymity
� Principle: how vi is
structurally hidden in its
2-neighborhood
• i.e., how similar vi is to its
neighbors of neighbors
� Proposed metrics:
vi
d(vi)=1
� Proposed metrics:
Measuring Local Topological Anonymity in Social Networks 13© Gábor György Gulyás, Sándor Imre
d(vi)=2
( )( )∑
∈∀
=2ik Vv i
kiiB
2,Vmax
v,vsim)v(LTA
( )∑∈∀
=2ik Vv
2i
kiiA
V
v,vsim)v(LTA
( )( )( )∑
∈∀ ∆σ⋅=
2ik Vv
2ideg
2i
kiiC
1,VmaxV
v,vsim)v(LTA
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Local Topological Anonymity (2)
� Similarity metric?• State-of-the-art attack is
based on cosine similarity
• CosSim produced bestresults in the comparisonof similarity metrics
• (our comparison & Spertus et al, 2005)
vi
d(vi)=1
� Simulations: CosSim()
• For the state-of-the-artattack
• Other attack � differentmetric
Measuring Local Topological Anonymity in Social Networks 14© Gábor György Gulyás, Sándor Imre
d(vi)=2
Spertus et al.: Evaluating similarity measures:
a large-scale study in the orkut social network. (2005)
ki
kiki
VV
VV)v,v(CosSim
⋅=
I
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Visual comparison for small nets
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Evaluation methodology
� Simulational evaluation:attack results vs. LTA prediction• State-of-the-art attack (**)
• 10 rounds to avoid seeddependencies (e.g., location)
• Strong attacker
� Dataset sources:Slashdot, Wikivote, Epinions (*)LiveJournal (our crawl)
� Realistic test data (**)• Given overlap factors:
αV = Jacc(Vsrc, Vtar)αE = Jacc(Esrc, Etar)αE = Jacc(Esrc, Etar)
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* Source: http://snap.stanford.edu/data/index.html
** Narayanan & Shmatikov: De-anonymizing social networks (2009)
V1 V2 V3
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Dataset generation
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Dataset generation (2)
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Simulational results of LJ-10KvA
Scoring:
+1 for a TP
-1 for a FP
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Simulational results of LJ-10KvA (2)
83.9% of the
overlapping
nodes!
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Pearson correlation of LTA and results
avg(LTA )=-0.27945
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avg(LTAA)=-0.27945
avg(LTAB)=-0.23164
avg(LTAC)=-0.17742
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Corrected LTA evaluation
avg(LTA )=-0.42133
Measuring Local Topological Anonymity in Social Networks 22© Gábor György Gulyás, Sándor Imre
avg(LTAA)=-0.42133
avg(LTAB)=-0.34466
avg(LTAC)=-0.26988
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Future work
� Attacker perspective:
network level LTA
predictions?
� Directed networks?
� Improving measures?
� Further LTA analysis
• Structural dependency
� LTA testing for other
algorithms
• E.g., seed-and-grow
� Combined global +
local metrics?
Measuring Local Topological Anonymity in Social Networks 23© Gábor György Gulyás, Sándor Imre
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Questions?
THANK YOU FOR YOUR ATTENTION!
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Gábor György Gulyás
assistant research fellow
Dept. of Telecommunications (BME)