1. GenoGuard: Protecting Genomic Data against Brute-Force Attacks
36th IEEE Symposium on Security and Privacy
May 18, 2015
Zhicong Huang1, Erman Ayday2, Jacques Fellay3, Jean-Pierre Hubaux1, Ari Juels4
1School of Computer and Communication Sciences, EPFL
2Bilkent University
3School of Life Sciences, EPFL 4Cornell Tech (Jacobs)

2. The Genomic Avalanche Is Coming…

3. Things are Moving CS giants start proposing genome-related services
Google Genomics (API to store, process, explore, and share DNA data)
IBM Research (computational genomics)
Microsoft Research (genomic research in collaboration with Sanger Center)
Apple (the ResearchKit program)
Global Alliance for Genomics & Health
Definition of a common framework for effective, responsible and secure sharing of genomic and clinical data
Security Working Group: security infrastructure policy and technology

4. Background: Genomics

5. Genomics Background Single Nucleotide Variant (SNV)
4 million SNVs per individual
A subset of 50 million SNVs that have been discovered
Major allele (0), minor allele (1)
Correlations between SNVs
Genotype data (to be protected)
Consider a pair of chromosomes (out of 23 pairs in human genome)
For an SNV position, encoded as the number of minor alleles (0, 1, or 2)

6. Genomic Privacy Can the protection survive longer? High sensitivity
Predisposition to disease
Genetic discrimination: Denial of access to health insurance, education, and employment.
Long-term data value
But attackers’ computing power keeps increasing
GATTACA, 1997 Movie
Can the protection survive longer?

7. Background: Honey Encryption [1]
[1] A. Juels, T. Ristenpart. Honey Encryption: Security Beyond the Brute-Force Bound. EUROCRYPT, 2014.

8. Honey Encryption Messages: Passwords: Conventional Encryption
Gene_Q
eiKangLpkandlf
Passwords:
Correct password
Wrong password
Conventional Encryption
ddUoIOkesLhKnb
Honey Encryption
Passwords:
Correct password
Wrong password
Messages:
Gene_Q
Gene_R
The threat of brute-force attacks is mitigated by using honey encryption.

9. Distribution-Transforming Encoder (DTE)
Encrypt the seed
Encoder 00
“Gene_Q”
p = 1/4 01
“Gene_R”
p = 1/4
Uniform
Non-uniform 10
“Gene_S”
p = 1/2 11
Message
Probability
1/4
1/2
Seed space
Decoder
Message space
pm: original message distribution
pd: DTE message distribution (the probability of getting a message by decoding a randomly picked seed)

10. Distribution-Transforming Encoder (DTE)00
“Gene_Q”
p = 1/4 01
“Gene_R”
p = 1/4
Uniform
Non-uniform 10
“Gene_S”
p = 1/2 11
Seed space
Decoder
Message space
pd = pm

11. GenoGuard

12. DTE on Genome Sequences
n is the number of SNVs
Probability of a sequence M=(m1 , m2 , … , mn), where mi is from the set {0, 1, 2}:
To encode the sequence : divide seed space in a traversal of the sequence
Subsequence: (m1, m2, …, mn-1)

13. Example pd = pm [0, 2L - 1] Number of SNVs: n = 3 P(m1 = 0) = 0.6
Sequence: M = (0, 2, 1)
P(m1 = 0) = 0.6
P(m2 = 2 | m1 = 0) = 0.1
P(m3 = 1 | M1,2 = (0, 2)) = 0.3
[0, 2L - 1]
L-bit representation

14. Finite-Precision DTE pm ≈ pd pd under L-bit representation pm
Probability increases
Probability decreases
pd under L-bit representation
Probability pm
Probability
pm ≈ pd
Message
Message

15. A Simple Brute-Force Attack
One correct password among 1000 passwords
Compute the probability of each decrypted sequence
Conventional encryption
GenoGuard

16. Defense against Attacks with Phenotypic Traits (1)
Ancestry
(The data is taken from the HapMap project1)
Principal component 1
Principal component 2
Asian
European
African
1An international project for finding genetic variation with human disease

17. Defense against Attacks with Phenotypic Traits (2)
Decrypt as European (red symbols “+”)
Ancestry
Different DTEs for different ancestries
Wrong password Wrong sequence Yet, consistent ancestry
Other traits  Privacy loss quantification
Principal component 1
Principal component 2

18. Performance

19. Performance of Encryption (Decryption)
Chromosome
Time (seconds)
Chromosome length (# of SNVs)
100’000
Python
Cluster with 22 nodes
3.40GHz Intel Xeon CPU E31270
64-bit Linux Debian system
Encoding time
Number of SNVs
Linear cost depending on the number of SNVs
0.5 ms / SNV
Decoding time
Password-based encryption (decryption) time

20. Conclusion and Future Work
GenoGuard provides protection of genomic data against brute-force attacks
A privacy-preserving solution by taking into account the special characteristics of genomic data
Future investigation
Extension to other sensitive sequential data
More investigation for privacy erosion under data model evolution
Source code:
To learn more about genomic privacy
A website for the research community: