Task 1: Privacy Preserving Genomic Data Sharing Presented by Noman Mohammed School of Computer Science McGill University 24 March 2014

2 Reference  N. Mohammed, R. Chen, B. C. M. Fung, and P. S. Yu. Differentially private data release for data mining. In Proceedings of the17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pages ,

3 Outline  Privacy Models  Algorithm for Relational Data  Algorithm for Genomic Data  Conclusion 3

4 Overview 4 Privacy model Anonymization algorithm Data utility

5 k-Anonymity [Samarati & Sweeney, PODS 1998]  Quasi-identifier (QID): The set of re-identification attributes.  k-anonymity: Each record cannot be distinguished from at least k-1 other records in the table wrt QID. 3-anonymous patient table JobSexAgeDisease ProfessionalMale[36-40] Cancer ProfessionalMale[36-40] Cancer ProfessionalMale[36-40] Cancer ArtistFemale[30-35] Flu ArtistFemale[30-35] Hepatitis ArtistFemale[30-35] Fever ArtistFemale[30-35] Hepatitis Raw patient table JobSexAgeDisease EngineerMale36Cancer EngineerMale38Cancer LawyerMale38Cancer MusicianFemale30Flu MusicianFemale30Hepatitis DancerFemale30Fever DancerFemale30Hepatitis 5

6 Differential Privacy [DMNS, TCC 06] 6 A

7 Differential Privacy 7 A non-interactive privacy mechanism A gives ε -differential privacy if for all neighbour D and D’, and for any possible sanitized database D* Pr A [A(D) = D*] ≤ exp(ε) × Pr A [A(D’) = D*] DD’ D and D’ are neighbors if they differ on at most one record

8 Laplace Mechanism 8 For example, for a single counting query Q over a dataset D, returning Q(D) + Laplace(1/ε) maintains ε -differential privacy. ∆f = max D,D’ ||f(D) – f(D’)|| 1 For a counting query f: ∆f =1

9 Outline  Privacy Models  Algorithm for Relational Data  Algorithm for Genomic Data  Conclusion 9

10 Non-interactive Framework 0 + Lap(1/ ε ) 10

11 For high-dimensional data, noise is too big 0 + Lap(1/ ε ) 11 Non-interactive Framework

12 Non-interactive Framework

13 JobAgeClassCount Any_Job[18-65)4Y4N8 Artist[18-65)2Y2N4 Professional[18-65)2Y2N4 Age [18-65) [18-40)[40-65) Artist[18-40)2Y2N4Artist[40-65)0Y0N0 Anonymization Algorithm [18-30)[30-40) 13 Professional[18-40)2Y1N3Professional[40-65)0Y1N1 Job Any_Job ProfessionalArtist EngineerLawyerDancerWriter

14 Candidate Selection  we favor the specialization with maximum Score value  First utility function: ∆u =  Second utility function: ∆u = 1 14

15 Anonymization Algorithm O(A pr x|D|log|D|) O(|candidates|) O(|D|) O(|D|log|D|) O(1) 15

16 Anonymization Algorithm O(A pr x|D|log|D|) O(|candidates|) O(|D|) O(|D|log|D|) O(1) O((A pr +h)x|D|log|D|) 16

17 Outline  Privacy Models  Algorithm for Relational Data  Algorithm for Genomic Data  Conclusion 17

18 case_chr2_ _ rs AG AG AA AG GG AA AG AA GG AG AA AA AA AA AA AA AA GG GG AG AG AA AG GG AA AA GG AG AG AG GG AG AA AA AG AG AG AG AG AG AA AG GG AG AA GG GG GG GG AG AG AG AG AA GG GG GG AG AA AG GG AG AA GG GG AG AG AG AG AG AA AA AG AG AG AA AG AG AG AG GG AG AG AG GG GG AG AG GG AG AG AG AA AA GG AG AA GG AA AA AG GG AG AG AG AG AG AG AG AG GG GG AA AG AG AG AG AA AG GG AG GG AA AG GG AG AG AG AA AG AG AG GG AG GG AG GG AG AG AG GG AG AG GG GG AG AG GG AA GG AA AG AG AG AG GG AG AA AG GG GG AG AG AG AG AG GG AG AG AA AG AA AA AG GG AA AG AG GG AG GG AG AG GG GG AG AG AA AG AG AG GG AG GG GG AG AG GG AG GG rs CC CC CC CT CT CC CT CC CT CT CC CC CC CC CC CC CC CT CT CT CC CC CT CT CC CC CT CC CT CC CT CC CC CC CT …. rs CC CC CC CT CT CC CT CC TT CT CC CC CC CC CC CC CC TT TT CT CC CC CT CT CC CC CT CC CT CC TT CC CC CC CT …. 18

19 Casers rs rs rs …… 1AGCC 2AGCC 3AACC 4AGCT 5GGCT ……… ……… ……… ……… 198GGTT 199AGCT 200GGCT Raw Data 19

20 Blocks/Attributes Casers rs AGCC 2AGCC 3AACC 4AGCT 5GGCT 20 Unique Combinations: AG CC AA CC AG CT GG CT Any AG CCAA CCAG CTGG CT

21 Taxonomy Trees for Attributes  SNP data was split evenly into N/6 blocks(attributes), where N is number of SNPs 21

22 Hierarchy Tree for Chr2 22

23 Hierarchy Tree for Chr10 23

24 Block 1Block 2Block 3Count Any 200 AA CCAny 130 AG CCAny 70 Block 3 Any CC GGCT AG AA CCAnyCC GG60AA CCAnyCT AG70 Genomic Data 24 AG CCAnyCC GG30AG CCAnyCT AG40 Block 1 Any AG CCAA CC

25 Anonymized Data 25 Casers rs rs rs …… 1AGCCAny 2AGCCAny 3…… 4…… 5AACCAny …AACCAny ……… ……… ……… … … …

26 Heterogeneous Healthcare Data IDJobAgers rs …… 1Engineer50AGCC…… 2Doctor45AACT…… 3……………… Relational DataGenomic Data 26

27  Privacy-Preserving Genomic Data Release  Tree-based approach is promising  Future work  Partitioning the SNPs to generate blocks  Utility function for specialization  Two-level tree Vs. multi-level hierarchy trees  Single-dimension Vs. multi-dimensional partitioning Conclusions 27

28  Privacy-Preserving Genomic Data Release  Tree-based approach is promising  Future work  Partitioning the SNPs to generate blocks  Utility function for specialization  Two-level tree Vs. multi-level hierarchy trees  Single-dimension Vs. multi-dimensional partitioning Thank You ! 28