Infringement of Individual Privacy via Mining Differentially Private GWAS Statistics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9784)

Abstract

Individual privacy in genomic era is becoming a growing concern as more individuals get their genomes sequenced or genotyped. Infringement of genetic privacy can be conducted even without raw genotypes or sequencing data. Studies have reported that summary statistics from Genome Wide Association Studies (GWAS) can be exploited to threat individual privacy. In this study, we show that even with differentially private GWAS statistics, there is still a risk for leaking individual privacy. Specifically, we constructed a Bayesian network through mining public GWAS statistics, and evaluated two attacks, namely trait inference attack and identity inference attack, for infringement of individual privacy not only for GWAS participants but also regular individuals. We used both simulation and real human genetic data from 1000 Genome Project to evaluate our methods. Our results demonstrated that unexpected privacy breaches could occur and attackers can derive identity information and private information by utilizing these algorithms. Hence, more methodological studies should be invested to understand the infringement and protection of genetic privacy.

References

  1. 1.
    Erlich, Y., Narayanan, A.: Routes for breaching and protecting genetic privacy. Nat. Rev. Genet. 15(6), 409–421 (2014)CrossRefGoogle Scholar
  2. 2.
    Greenbaum, D., Gerstein, M.: Genomic anonymity: have we already lost it? Am. J. Bioeth. 8(10), 71–74 (2008)CrossRefGoogle Scholar
  3. 3.
    Greenbaum, D., Gerstein, M.: Social networking and personal genomics: suggestions for optimizing the interaction. Am. J. Bioeth. 9(6–7), 15–19 (2009)CrossRefGoogle Scholar
  4. 4.
    Greenbaum, D., Sboner, A., Mu, X.J., Gerstein, M.: Genomics and privacy: implications of the new reality of closed data for the field. PLoS Comput. Biol. 7(12), e1002278 (2011)CrossRefGoogle Scholar
  5. 5.
    The Health Insurance Portability and Accountability Act of 1996 (HIPAA). http://www.hhs.gov/hipaa/
  6. 6.
    Shi, X., Wu, X.: Genetic privacy: risks, ethics, and protection techniques. In: The Workshop on Data Science Learning and Applications to Biomedical and Health Sciences, pp. 57–62, New York, NY (2016)Google Scholar
  7. 7.
    Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J.V., Stephan, D.A., Nelson, S.F., Craig, D.W.: Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 4(8), e1000167 (2008)CrossRefGoogle Scholar
  8. 8.
    Masca, N., Burton, P.R., Sheehan, N.A.: Participant identification in genetic association studies: improved methods and practical implications. Int. J. Epidemiol. 40(6), 1629–1642 (2011)CrossRefGoogle Scholar
  9. 9.
    Wang, R., Li, Y.F., Wang, X., Tang, H., Zhou, X.: Learning your identity and disease from research papers: information leaks in genome wide association study. In: 16th ACM Conference on Computer and Communications Security, pp. 534–544. ACM (2009)Google Scholar
  10. 10.
    Zhou, X., Peng, B., Li, Y.F., Chen, Y., Tang, H., Wang, X.F.: To release or not to release: evaluating information leaks in aggregate human-genome data. In: Atluri, V., Diaz, C. (eds.) ESORICS 2011. LNCS, vol. 6879, pp. 607–627. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Gymrek, M., McGuire, A.L., Golan, D., Halperin, E., Erlich, Y.: Identifying personal genomes by surname inference. Science 339(6117), 321–324 (2013)CrossRefGoogle Scholar
  12. 12.
    Wang, Y., Wu, X., Shi, X.: Using aggregate human genome data for individual identification. In,: IEEE International Conference on Bioinformatics and Biomedicine, pp. 410–415. IEEE, Shenzhen, China (2013)Google Scholar
  13. 13.
    Hindorff, L.A., MacArthur, J., Morales, J., Junkins, H.A., Hall, P.N., Klemm, A.K., Manolio, T.A.: A Catalog of Published Genome-wide Association Studies. http://www.genome.gov/gwastudies
  14. 14.
    Fienberg, S.E., Slavkovic, A., Uhler, C.: Privacy preserving GWAS data sharing. In: 11th International Conference on Data Mining Workshops, pp. 628–635. IEEE (2011)Google Scholar
  15. 15.
    Johnson, A., Shmatikov, V.: Privacy-preserving data exploration in genome-wide association studies. In: 19th ACM International Conference on Knowledge Discovery and Data Mining, pp. 1079–1087. ACM, Chicago, IL (2013)Google Scholar
  16. 16.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)CrossRefGoogle Scholar
  18. 18.
    Bhaskar, R., Laxman, S., Smith, A., Thakurta, A.: Discovering frequent patterns in sensitive data. In: 16th ACM International Conference on Knowledge Discovery and Data Mining, pp. 503–512. ACM, Washington, DC (2010)Google Scholar
  19. 19.
    Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. In: 23rd Annual Conference on Neural Information Processing Systems, pp. 289–296. Citeseer, Vancouver, B.C., Canada (2008)Google Scholar
  20. 20.
    Kifer, D., Machanavajjhala, A.: No free lunch in data privacy. In: 17th ACM International Conference on Knowledge Discovery and Data Mining, pp. 193–204. ACM, San Diego, CA (2011)Google Scholar
  21. 21.
    Lee, J., Clifton, C.: Differential identifiability. In: 18th ACM International Conference on Knowledge Discovery and Data Mining, pp. 1041–1049. ACM, Beijing, China (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of North Carolina at CharlotteCharlotteUSA
  2. 2.University of ArkansasFayettevilleUSA

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