An overview of human genetic privacy

Annals of the New York Academy of Sciences

Volumn 1387, Issue 1, 2017, Pages 61-72

  Shi, Xinghua ^a   Wu, Xintao ^b  

^a University of North Carolina at Charlotte   (United States)

^b University of Arkansas   (United States)

Author keywords

Big Data; ethics; genetic privacy; regulations

Indexed keywords

ETHICS; GENETIC PRIVACY; GENOMICS; HUMAN; BAYES THEOREM; BIOLOGY; COMPUTER SECURITY; DATA MINING; GENOME-WIDE ASSOCIATION STUDY; LEGISLATION AND JURISPRUDENCE; MOBILE APPLICATION; PROCEDURES; RISK MANAGEMENT; SOCIAL MEDIA; STANDARDS; TRENDS;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; COMPUTER SECURITY; DATA MINING; GENETIC PRIVACY; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; MOBILE APPLICATIONS; RISK MANAGEMENT; SOCIAL MEDIA;

EID: 84992561367     PISSN: 00778923     EISSN: 17496632     Source Type: Journal    
DOI: 10.1111/nyas.13211     Document Type: Article

References (71)

1. Vayena, E. & U. Gasser. 2016. Between openness and privacy in genomics. PLoS Med. 13: e1001937.
2. The National Institutes of Health. 2014. The Genomic Data Sharing (GDS) Policy. August 27, 2014. https://gds.nih.gov/.
3. Harvard University. 2016. The Personal Genome Project (PGP). http://www.personalgenomes.org/.
4. The University of Michigan. 2016. Genes for Good. http://genesforgood.sph.umich.edu/.
5. DNA. Land. 2015. https://dna.land/.
6. openSNP. 2016. https://opensnp.org/.
7. Global Alliance for Genomics and Health (GA4GH). 2016. http://genomicsandhealth.org/.
8. Erlich, Y. & A. Narayanan. 2014. Routes for breaching and protecting genetic privacy. Nat. Rev. Genet. 15: 409–421.
9. Erlich, Y., J.B. Williams, D. Glazer, et al. 2014. Redefining genomic privacy: trust and empowerment. PLoS Biol. 12: e1001983.
10. Akgün, M., A.O. Bayrak, B. Ozer, et al. 2015. Privacy preserving processing of genomic data: a survey. J. Biomed. Inform. 56: 103–111.
11. Greenbaum, D. & M. Gerstein. 2008. Genomic anonymity: have we already lost it? Am. J. Bioeth. 8: 71–74.
12. Greenbaum, D. & M. Gerstein. 2009. Social networking and personal genomics: suggestions for optimizing the interaction. Am. J. Bioeth. 9: 15–19.
13. Greenbaum, D., A. Sboner, X.J. Mu, & M. Gerstein. 2011. Genomics and privacy: implications of the new reality of closed data for the field. PLoS Comput. Biol. 7: e1002278.
14. The 1000 Genomes Project Consortium, G.R. Abecasis, A. Auton, et al. 2012. An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65.
15. The 1000 Genomes Project Consortium, A. Auton, L.D. Brooks, et al. 2015. A global reference for human genetic variation. Nature 526: 68–74.
16. Sweeney, L. 2000. Uniqueness of simple demographics in the US population. Technical report. Pittsburg: Data Privacy Laboratory, School of Computer Science, Carnegie Mellon University.
17. Sweeney, L., A. Abu & J. Winn. 2013. Identifying participants in the personal genome project by name. Available at SSRN 2257732. Cambridge: Data Privacy Lab, IQSS, Harvard University.
18. Wheeler, D.A., M. Srinivasan, M. Egholm, et al. 2008. The complete genome of an individual by massively parallel DNA sequencing. Nature 452: 872–876.
19. APOC, A. 2009. On Jim Watson's APOE status: genetic information is hard to hide. Eur. J. Hum. Genet. 17: 147–149.
20. Shringarpure, S.S. & C.D. Bustamante. 2015. Privacy risks from genomic data-sharing beacons. Am. J. Hum. Genet. 97: 631–646.
21. Homer, N., S. Szelinger, M. Redman, et al. 2008. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 4: e1000167.
22. National Center for Biotechnology Information. 2016. The Database of Genotypes and Phenotypes (dbGaP). http://www.ncbi.nlm.nih.gov/gap.
23. Wang, R., Y.F. Li, X. Wang, et al. 2009. Learning your identity and disease from research papers: information leaks in genome wide association study. In Proceedings of the 16th ACM Conference on Computer and Communications Security. 534–544. ACM Digital Library.
24. Wang, Y., X. Wu & X. Shi. 2013. Using aggregate human genome data for individual identification. In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 410–415. IEEE.
25. Wang, Y., X. Wu & X. Shi. Infringement of individual privacy via mining GWAS statistics. Technical Report. University of Arkansas. Fayetteville, AR. Report No. DPL-2014-004.
26. Wang, Y., J. Wen, X. Wu, et al. 2016. Infringement of individual privacy via mining differentially private GWAS statistics. In Proceedings of the 2nd International Conference on Big Data Computing and Communication (BIGCOM 2016). Lect. Notes Comput. Sci. 9784: 355–366. Springer.
27. Welter, D., J. MacArthur, J. Morales, et al. 2014. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42: D1001–D1006.
28. National Human Genome Research Institute. 2016. A catalog of published genome-wide association studies. http://www.genome.gov/gwastudies.
29. Schadt, E.E., S. Woo & K. Hao. 2012. Bayesian method to predict individual SNP genotypes from gene expression data. Nat. Genet. 44: 603–608.
30. Harmanci, A. & M. Gerstein. 2016. Quantification of private information leakage from phenotype-genotype data: linking attacks. Nat. Methods 13: 251–256.
31. McGonigle, I. & N. Shomron. 2016. Privacy, anonymity and subjectivity in genomic research. Genet. Res. 98: e2.
32. National Human Genome Research Institute. 2015. All about the human genome project (HGP). http://www.genome.gov/10001772.
33. The International HapMap Consortium. 2004. Integrating ethics and science in the International HapMap Project. Nat. Rev. Genet. 5: 467–475.
34. Rodriguez, L.L., L.D. Brooks, J.H. Greenberg, et al. 2013. The complexities of genomic identifiability. Science 339: 275–276.
35. Personal Genetics Education Project (pgEd). 2016. http://www.pged.org/.
36. Gymrek, M., A.L. McGuire, D. Golan, et al. 2013. Identifying personal genomes by surname inference. Science 339: 321–324.
37. Hayden, E.C. 2012. A broken contract. Nature 486: 312–314.
38. Dwork, C., F. McSherry, K. Nissim, et al. 2006. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography. Lect. Notes Comput. Sci. 3876: 265–284. Springer.
39. Dwork, C. 2011. A firm foundation for private data analysis. Commun. ACM 54: 86–95.
40. Samarati, P. & L. Sweeney. 1998. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical report. SRI International, Menlo Park, CA.
41. Machanavajjhala, A., D. Kifer, J. Gehrke, et al. 2007. l-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Disc. Data (TKDD) 1: 3.
42. Li, N., T. Li & S. Venkatasubramanian. 2007. t-closeness: privacy beyond k-anonymity and l-diversity. In 2007 IEEE 23rd International Conference on Data Engineering. 106–115. IEEE.
43. Aggarwal, C.C. & P.S. Yu. 2008. “A general survey of privacy-preserving data mining models and algorithms.” In Privacy-Preserving Data Mining: Models and Algorithms. C.C. Aggarwal & P.S. Yu, Eds.: 11–52. Boston, MA: Springer.
44. Bhaskar, R., S. Laxman, A. Smith, et al. 2010. Discovering frequent patterns in sensitive data. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 503–512. ACM.
45. Chaudhuri, K. & C. Monteleoni. 2009. “Privacy-preserving logistic regression.” In Advances in Neural Information Processing Systems. 289–296. Neural Information Processing Systems Foundation, Inc.
46. Hay, M., V. Rastogi, G. Miklau, et al. 2010. Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB Endow. 3: 1021–1032.
47. Kifer, D. & A. Machanavajjhala. 2011. No free lunch in data privacy. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. 193–204. ACM.
48. Lee, J. & C. Clifton. 2012. Differential identifiability. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1041–1049. ACM.
49. Ying, X., X. Wu & Y. Wang. 2013. On linear refinement of differential privacy-preserving query answering. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. 353–364. Springer.
50. Nissim, K., S. Raskhodnikova & A. Smith. 2007. Smooth sensitivity and sampling in private data analysis. In Proceedings of the 39th Annual ACM Symposium on Theory of Computing. 75–84. ACM.
51. McSherry, F. & K. Talwar. 2007. Mechanism design via differential privacy. In 48th Annual IEEE Symposium on Foundations of Computer Science, 2007. FOCS’07. 94–103. IEEE.
52. McSherry, F. & I. Mironov. 2009. Differentially private recommender systems: building privacy into the net. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 627–636. ACM.
53. Zhang, J., Z. Zhang, X. Xiao, et al. 2012. Functional mechanism: regression analysis under differential privacy. Proc. VLDB Endow. 5: 1364–1375.
54. Wang, Y., C. Si & X. Wu. 2015. Regression model fitting under differential privacy and model inversion attack. In Proceedings of the 24th International Joint Conference on Artificial Intelligence. 1003–1009. Palo Alto: AAAI Press.
55. Xiao, X., G. Wang & J. Gehrke. 2011. Differential privacy via wavelet transforms. IEEE Trans. Knowl. Data Eng. 23: 1200–1214.
56. Ding, B., M. Winslett, J. Han, et al. 2011. Differentially private data cubes: optimizing noise sources and consistency. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. 217–228. ACM.
57. McSherry, F.D. 2009. Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data. 19–30. ACM.
58. Phan, N., Y. Wang, X. Wu, et al. 2016. Differential privacy preservation for deep auto-encoders: an application of human behavior prediction. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 12–17. Palo Alto: AAAI Press.
59. Shokri, R. & V. Shmatikov. 2015. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 1310–1321. ACM.
60. Hay, M., C. Li, G. Miklau, et al. 2009. Accurate estimation of the degree distribution of private networks. In 2009 9th IEEE International Conference on Data Mining. 169–178. IEEE.
61. Rastogi, V., M. Hay, G. Miklau, et al. 2009. Relationship privacy: output perturbation for queries with joins. In Proceedings of the 28th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. 107–116. ACM.
62. Wang, Y., X. Wu & L. Wu. 2013. Differential privacy preserving spectral graph analysis. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. 329–340. Springer.
63. Fienberg, S.E., A. Slavkovic & C. Uhler. 2011. Privacy preserving GWAS data sharing. In 2011 IEEE 11th International Conference on Data Mining Workshops. 628–635. IEEE.
64. Johnson, A. & V. Shmatikov. 2013. Privacy-preserving data exploration in genome-wide association studies. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1079–1087. ACM.
65. Im, H.K., E.R. Gamazon, D.L. Nicolae, et al. 2012. On sharing quantitative trait GWAS results in an era of multiple-omics data and the limits of genomic privacy. Am. J. Hum. Genet. 90: 591–598.
66. Djatmiko, M., A. Friedman, R. Boreli, et al. 2014. Secure evaluation protocol for personalized medicine. In Proceedings of the 13th Workshop on Privacy in the Electronic Society. 159–162. ACM.
67. He, D., N.A. Furlotte, F. Hormozdiari, et al. 2014. Identifying genetic relatives without compromising privacy. Genome Res. 24: 664–672.
68. The International HapMap Consortium, D.M. Altshuler, R.A. Gibbs, et al. 2010. Integrating common and rare genetic variation in diverse human populations. Nature 467: 52–58.
69. Baldi, P., R. Baronio, E. De Cristofaro, et al. 2011. Countering GATTACA: efficient and secure testing of fully-sequenced human genomes. In Proceedings of the 18th ACM Conference on Computer and Communications Security. 691–702. ACM.
70. De Cristofaro, E., S. Faber, P. Gasti, et al. 2012. Genodroid: are privacy-preserving genomic tests ready for prime time?. In Proceedings of the 2012 ACM Workshop on Privacy in the Electronic Society. 97–108. ACM.
71. De Cristofaro, E., S. Faber & G. Tsudik. 2013. Secure genomic testing with size-and position-hiding private substring matching. In Proceedings of the 12th ACM Workshop on Privacy in the Electronic Society. 107–118. ACM.