Understanding the Effects of Real-World Behavior in Statistical - - PowerPoint PPT Presentation
Understanding the Effects of Real-World Behavior in Statistical - - PowerPoint PPT Presentation
Understanding the Effects of Real-World Behavior in Statistical Disclosure Attacks Simon Oya , Carmela Troncoso and Fernando Prez-Gonzlez In Introduction. Mix ixes. MIX The adversary is able to learn Alices sending profile!!! 2 In
In
- Introduction. Mix
ixes.
2
The adversary is able to learn Alice’s sending profile!!! MIX
In
- Introduction. Mix
ixes.
3
Anonymity? How?
- Changing appearance
(re-encryption) MIX
In
- Introduction. Mix
ixes.
4
Anonymity? How?
- Changing appearance
(re-encryption) MIX The adversary is able to learn Alice’s sending profile with the timing information!!!
In
- Introduction. Mix
ixes.
5
ANONYMOUS Anonymity? How?
- Changing appearance
(re-encryption)
- Removing timing
information (delays) MIX
In
- Introduction. LSDA (I)
(I)
In
- Introduction. LSDA (I)
(I)
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In
- Introduction. LSDA (I)
(I)
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Least-Squares Disclosure Attack
In
- Introduction. LSDA (II
(II)
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Real data:
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220.000 emails sent between employees of Enron Corporation. http://www.cs.cmu.edu/~./enron/ 400.000 location check-ins from Gowalla social networking website. http://snap.stanford.edu/data/loc-gowalla.html 180.000 posts to the public mailing lists of Indimedia http://lists.indimedia.org/
Motivation: LSDA’s analysis falls short
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Real-World Behavior. In Input process.
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Emails dataset
Real-World Behavior. Output process.
#messages Email Location Mailing List =2 1.85 1.03 1.29 =3 2.71 1.05 1.46 =4 3.53 1.06 1.53 =5 4.40 1.08 1.53 ≥6 13.56 1.11 1.57
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Average number of receivers chosen
New Theoretical Analysis (I) (I)
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Multinomial output Maximum variance output “uniformity” Number of rounds observed Variance of the number
- f messages sent
Global contribution Individual term
New Theoretical Analysis (II (II)
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Results
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