k.u.leuven george danezis 1, markulf kohlweiss 1, ben livshits 1, and alfredo rial 2 private...
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K.U.Leuven
George Danezis1, Markulf Kohlweiss1, Ben Livshits1, and Alfredo Rial2
Private Client-Side Profiling with Random Forests and Hidden Markov Models
1Microsoft Research2KU Leuven ESAT/COSIC – IBBT, Belgium
PETS 2012
Private Client-Side Profiling PETS 2012
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• Introduction• System Overview• Applications• Random Forests• Our Protocol• Conclusion
Index
2Private Client-Side Profiling
http://www.dmrdirect.com/direct-mail/customer-profiling/gain-valuable-marketing-intelligence/
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1 – Introduction
3Private Client-Side Profiling
http://blog.maia-intelligence.com/2009/10/05/customer-analytics-in-retail/
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• Client Profiling -> Deliver Customized Services• Current techniques:
o Cookieso Third party apps in social networkso Web bugs
• Disadvantageso Privacyo Correctness
• Ad-hoc• Block
Current Client Profiling Tools
4Private Client-Side Profiling
http://www.pc-xp.com/2010/12/04/web-bug-reveals-internet-browsing-history/
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• User’s perform the classification task:o Input certified features and certified algorithmo Run algorithm:
• Classification: Random Forest• Pattern Recognition: Hidden Markov Model
o Output result and proof of correctnesso Service provider verifies result
• Advantageso Privacy: Only classification result is disclosedo Correctness guaranteed by proof
Private Client-Side Profiling
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2- System Overview
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• Behavioral advertising• P2P dating & matchmaking• Financial logs• Pay-as-you-drive Insurance• Bio-medical & genetic
3- Applications
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Behavioural Advertising
8Private Client-Side Profiling
http://kickstand.typepad.com/metamuse/2008/05/behavioral-adve.html
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P2P Dating & Matchmaking
9Private Client-Side Profiling
http://www.robhelsby.com/P2P%20Dating.html
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Financial logs
10Private Client-Side Profiling
http://www.ikeepsafe.org/privacy/arm-yourself-against-online-fraud/
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Pay-as-you-drive Insurance
11Private Client-Side Profiling
http://www.fenderbender.com/FenderBender/April-2011/Pay-As-You-Drive-Insurance/
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Bio-medical & Genetic
12Private Client-Side Profiling
http://www.pattern-expert.com/Bioinformatics/eng/bioinformatics/SNPAnalysis.html
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4- Random Forests
13Private Client-Side Profiling
http://www.iis.ee.ic.ac.uk/~tkkim/iccv09_tutorial.html
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• Classification algorithm: a data item with a set of features is classified into two classes or
• It consists of a collection of trees. Each tree:oNon-leaf nodes: oLeaf-nodes:
• Classification result:
Definition of Random Forest
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Tree Example
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• Zero-Knowledge Proofs of Knowledge
• P-Signatures: signature schemes with an efficient ZKPK of signature possession
5- Our Protocol
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• LOOKUP
• ZKTABLE
Notation
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• A sends Prover his certified features:
Phase 1
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A’ sends Prover a certified random forest:• Branches:
o Left Branches:o Right Branches:
• Leaf nodes:
Phase 2
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• Prover computes the following ZKPK:
Phase 3 – Tree Resolution
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• Prover repeats tree resolution for all the trees
Phase 3 – Forest Resolution
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• P-signature scheme by Au et al. [SCN 2006]• Hidden range proof based on Camenisch et al.
[Asiacrypt 2008]• Random forest parameters:oNumber of trees: t = 50oDepth: D = 10oNumber of features: M = 100oAverage number of feature values: K = 100
Instantiation
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• Fu = Table of certified user features
• Bt = Table of branches of all trees
• Lt = Table of leaf nodes of all trees
• Vt = Table of signatures for the hidden range proof
• Pt = Proof of random forest resolution
Efficiency
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• Private Client-Side Profiling:o Classification: Random Forestso Pattern Recognition: Hidden Markov Models
• The mere act of profiling may violate privacy.
Conclusion
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“We do not see the power which is in speech because we forget that all speech is a classification, and thatAll classifications are oppressive”
Roland Barthes
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Comparison Shopping
25Private Client-Side Profiling
http://article.wn.com/view/2012/04/19/Life_insurance_cos_new_biz_premiums_down_92/