Zhicong Huang Erman Ayday Jacques Fellay JeanPierre Hubaux Ari Juels Presented by Chuong Ngo The Genomic Future The Genomic Future The Genomic Future The Genomic Future The Genomic Future ID: 460290
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GenoGuard: Protecting Genomic Data against Brute-Force Attacks
Zhicong Huang, Erman Ayday, Jacques Fellay, Jean-Pierre Hubaux, Ari Juels
Presented by Chuong NgoSlide2
The Genomic FutureSlide3
The Genomic FutureSlide4
The Genomic FutureSlide5
The Genomic FutureSlide6
The Genomic FutureSlide7
The Dangers of the Genomic FutureSlide8
The Dangers of the Genomic FutureSlide9
The Current LandscapeSlide10
The Current LandscapeSlide11
The Current LandscapeSlide12
GenoGuard is honey encryption with n-gram derived seeds.Slide13
GenoGuard - System ModelSlide14
GenoGuard - System Model
Protection Mechanisms (e.g. encryption)Slide15
GenoGuard - System Model
Protection Mechanisms (e.g. encryption)
AuthenticationSlide16
GenoGuard - ProtocolSlide17
GenoGuard - Encoding
(0, 2)
Single Nucleotide Variants
Encoder
Seed
PBE
Encrypted SeedSlide18
GenoGuard - Encoding
0
2Slide19
GenoGuard - Encoding
0
[0, 6)Slide20
GenoGuard - Encoding
0
2
[0.54, 6)Slide21
GenoGuard - Modeling
Linkage disequilibrium and allele frequency
K-th order Markov chainsRecombination ratesSlide22
Evaluation - Model FitnessSlide23
Evaluation - Storage OverheadSlide24
Evaluation - Experimental SecuritySlide25
Side Attacks - Ancestry CompatibilitySlide26
Side Attacks - Hair ColorSlide27
Side Attacks - ChromosomesSlide28
ConclusionSlide29
Discussion
What are the potential weakness of GenoGuard?
Does the use of Markov chains make sense? Why or why not?Do the authors claims of GenoGuard’s security make sense? Why or why not?