Advertisement

Privacy-Preserving Disease Risk Test Based on Bloom Filters

  • Jun Zhang
  • Linru Zhang
  • Meiqi He
  • Siu-Ming Yiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10631)

Abstract

Decreasing costs in genome sequencing have been paving the way for personalised medicine. An increasing number of individuals choose to undergo disease risk tests provided by medical units. However, it poses serious privacy threats on both the individuals’ genomic data and the tests’ specifics. Several solutions have been proposed to address the privacy issues, but they all suffer from high storage or communication overhead. In this paper, we put forward a general framework based on bloom filters, reducing the storage cost by 100x. To reduce communication overhead, we create index for encrypted genomic data. We speed up the searching of genomic data by 60x with bloom filter tree, compared to B\(_+\) tree index. Finally, we implement our scheme using the genomic data of a real person. The experimental results show the practicality of our scheme.

Keywords

Genomic privacy Bloom filters Homomorphic encryption 

Notes

Acknowledgement

This project is partially supported by a collaborative research grant (RGC Project No. CityU C1008-16G) of the Hong Kong Government.

References

  1. 1.
    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
  2. 2.
    Altshuler, D., Daly, M.J., Lander, E.S.: Genetic mapping in human disease. Science 322(5903), 881–888 (2008)CrossRefGoogle Scholar
  3. 3.
    Humbert, M., Ayday, E., Hubaux, J.-P., Telenti, A.: Addressing the concerns of the lacks family: quantification of kin genomic privacy. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security, pp. 1141–1152. ACM (2013)Google Scholar
  4. 4.
    Erlich, Y., Narayanan, A.: Routes for breaching and protecting genetic privacy. Nat. Rev. Genet. 15(6), 409–421 (2014)CrossRefGoogle Scholar
  5. 5.
    Malin, B.A.: An evaluation of the current state of genomic data privacy protection technology and a roadmap for the future. J. Am. Med. Inform. Assoc. 12(1), 28–34 (2005)CrossRefGoogle Scholar
  6. 6.
    Shringarpure, S.S., Bustamante, C.D.: Privacy risks from genomic data-sharing beacons. Am. J. Hum. Genet. 97(5), 631–646 (2015)CrossRefGoogle Scholar
  7. 7.
    Zhang, Y., Blanton, M., Almashaqbeh, G.: Secure distributed genome analysis for gwas and sequence comparison computation. BMC Med. Inform. Decis. Mak. 15(5), S4 (2015)CrossRefGoogle Scholar
  8. 8.
    Perl, H., Mohammed, Y., Brenner, M., Smith, M.: Privacy/performance trade-off in private search on bio-medical data. Future Gener. Comput. Syst. 36, 441–452 (2014)CrossRefGoogle Scholar
  9. 9.
    Chen, Y., Peng, B., Wang, X.F., Tang, H.: Large-scale privacy-preserving mapping of human genomic sequences on hybrid clouds. In: NDSS (2012)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).  https://doi.org/10.1007/978-3-642-23822-2_33CrossRefGoogle Scholar
  11. 11.
    Ayday, E., Raisaro, J.L., Hubaux, J.-P., Rougemont, J.: Protecting and evaluating genomic privacy in medical tests and personalized medicine. In: Proceedings of the 12th ACM Workshop on Workshop on Privacy in the Electronic Society, pp. 95–106. ACM (2013)Google Scholar
  12. 12.
    Ayday, E., Raisaro, J.L., Hubaux, J.-P.: Personal use of the genomic data: privacy vs. storage cost. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 2723–2729. IEEE (2013)Google Scholar
  13. 13.
    Falconer, D.S., Mackay, T.F.C., Frankham, R.: Introduction to Quantitative Genetics. Trends in Genetics, vol. 12, no. 7, 4th edn, 280 p. Elsevier Science Publishers (Biomedical Division), Amsterdam (1996)Google Scholar
  14. 14.
    Danezis, G., De Cristofaro, E.: Fast and private genomic testing for disease susceptibility. In: Proceedings of the 13th Workshop on Privacy in the Electronic Society, pp. 31–34. ACM (2014)Google Scholar
  15. 15.
    Ugus, O., Westhoff, D., Laue, R., Shoufan, A., Huss, S.A.: Optimized implementation of elliptic curve based additive homomorphic encryption for wireless sensor networks. arXiv preprint arXiv:0903.3900 (2009)
  16. 16.
    Huang, R.W., Gui, X.L., Yu, S., Zhuang, W.: Research on privacy-preserving cloud storage framework supporting ciphertext retrieval. In: 2011 International Conference on Network Computing and Information Security (NCIS), vol. 1, pp. 93–97. IEEE (2011)Google Scholar
  17. 17.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)CrossRefGoogle Scholar
  18. 18.
    Ziegeldorf, J.H., Pennekamp, J., Hellmanns, D., Schwinger, F., Kunze, I., Henze, M., Hiller, J., Matzutt, R., Wehrle, K.: BLOOM: bloom filter based oblivious outsourced matchings. BMC Med. Genomics 10(2), 44 (2017)CrossRefGoogle Scholar
  19. 19.
    De Cristofaro, E., Faber, S., Gasti, P., Tsudik, G.: Genodroid: are privacy-preserving genomic tests ready for prime time? In: Proceedings of the 2012 ACM Workshop on Privacy in the Electronic Society, pp. 97–108. ACM (2012)Google Scholar
  20. 20.
    Karvelas, N., Peter, A., Katzenbeisser, S., Tews, E., Hamacher, K.: Privacy-preserving whole genome sequence processing through proxy-aided ORAM. In: Proceedings of the 13th Workshop on Privacy in the Electronic Society, pp. 1–10. ACM (2014)Google Scholar
  21. 21.
    Ayday, E., Raisaro, J.L., Laren, M., Jack, P., Fellay, J., Hubaux, J.-P.: Privacy-preserving computation of disease risk by using genomic, clinical, and environmental data. In: Proceedings of USENIX Security Workshop on Health Information Technologies (HealthTech 2013), no. EPFL-CONF-187118 (2013)Google Scholar
  22. 22.
    Broder, A., Mitzenmacher, M.: Network applications of bloom filters: a survey. Internet Math. 1(4), 485–509 (2004)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Bresson, E., Catalano, D., Pointcheval, D.: A Simple public-key cryptosystem with a double trapdoor decryption mechanism and its applications. In: Laih, C.-S. (ed.) ASIACRYPT 2003. LNCS, vol. 2894, pp. 37–54. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-40061-5_3CrossRefGoogle Scholar
  24. 24.
    Seshadri, S., Fitzpatrick, A.L., Arfan Ikram, M., DeStefano, A.L., Gudnason, V., Boada, M., Bis, J.C., Smith, A.V., Carrasquillo, M.M., Lambert, J.C., et al.: Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 303(18), 1832–1840 (2010)CrossRefGoogle Scholar
  25. 25.
    Rotger, M., Glass, T.R., Junier, T., Lundgren, J., Neaton, J.D., Poloni, E.S., Van’t Wout, A.B., Lubomirov, R., Colombo, S., Martinez, R., et al.: Contribution of genetic background, traditional risk factors, and HIV-related factors to coronary artery disease events in HIV-positive persons. Clin. Infect. Dis. 57(1), 112–121 (2013)CrossRefGoogle Scholar
  26. 26.
    Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., Toft, T.: Privacy-preserving face recognition. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol. 5672, pp. 235–253. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03168-7_14CrossRefGoogle Scholar
  27. 27.
    Barman, L., Graini, E., Raisaro, J.L., Ayday, E., Hubaux, J.-P., et al.: Privacy threats and practical solutions for genetic risk tests. In: 2nd International Workshop on Genome Privacy and Security (GenoPri 2015), no. EPFL-CONF-207435 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhang
    • 1
  • Linru Zhang
    • 1
  • Meiqi He
    • 1
  • Siu-Ming Yiu
    • 1
  1. 1.Department of Computer ScienceThe University of Hong KongPok Fu LamHong Kong

Personalised recommendations