Inference attacks on genomic privacy with an improved HMM and an RCNN model for unrelated individuals

Ayday, 2017, Inference attacks against kin genomic privacy, IEEE Secur. Privacy, 15, 29, 10.1109/MSP.2017.3681052 Ayday, 2013, Personal use of the genomic data: privacy vs. storage cost, 2723 Cai, 2015, Deterministic identification of specific individuals from GWAS results, Bioinformatics, 31, 1701, 10.1093/bioinformatics/btv018 Deznabi, 2018, An inference attack on genomic data using kinship, complex correlations, and phenotype information, IEEE/ACM Trans. Comput. Biol.Bioinf., 15, 1333, 10.1109/TCBB.2017.2709740 Durbin, 1998 En.wikipedia.org, 2019, Inference attack, Accessed April 22. (https://en.wikipedia.org/wiki/Inference_attack). Ganju, 2018, Property inference attacks on fully connected neural networks using permutation invariant representations, 619 Gong, 2016, You are who you know and how you behave: Attribute inference attacks via users’ social friends and behaviors, 979 Gymrek, 2013, Identifying personal genomes by surname inference, Science, 339, 321, 10.1126/science.1229566 Harmanci, 2016, Quantification of private information leakage from phenotype-genotype data: linking attacks, Nat. Methods, 13, 251, 10.1038/nmeth.3746 He, 2017, Addressing the threats of inference attacks on traits and genotypes from individual genomic data, 223 P. Hess, Controversial geneticist warns: we can read your face in your dna., 2017, Accessed June 2, 2018. (https://www.inverse.com/article/36145-genetic-privacy-venter-23andme). Homer, 2008, Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays, PLOS Genet., 4, 1, 10.1371/journal.pgen.1000167 B. Howie, J. Marchini, 2019, IMPUTE2, Accessed April 22. (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#reference). Howie, 2009, A flexible and accurate genotype imputation method for the next generation of genome-wide association studies, PLOS Genet., 5, 1, 10.1371/journal.pgen.1000529 Hu, 1996, HMM based online handwriting recognition, IEEE Trans. Pattern Anal. Mach.Intell., 18, 1039, 10.1109/34.541414 Humbert, 2013, Addressing the concerns of the lacks family: quantification of kin genomic privacy, 1141 Libbrecht, 2015, Machine learning applications in genetics and genomics, Nat. Rev. Genet., 16, 321, 10.1038/nrg3920 Long, 2017, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach.Intell., 39, 640, 10.1109/TPAMI.2016.2572683 Mailman, 2007, The NCBI dbGaP database of genotypes and phenotypes, Nat. Genet., 39, 1181, 10.1038/ng1007-1181 Marchini, 2007, A new multipoint method for genome-wide association studies by imputation of genotypes, Nat. Genet., 39, 906, 10.1038/ng2088 Narain, 2016, Inferring user routes and locations using zero-permission mobile sensors, 397 Nyholt, 2009, On Jim Watson’s APOE status: genetic information is hard to hide, Eur. J. Hum. Genet., 17, 147, 10.1038/ejhg.2008.198 Peng, 2016, Information entropy models and privacy metrics methods for privacy protection, J. Softw., 27, 1891 Pouliot, 2016, The shadow nemesis: Inference attacks on efficiently deployable, efficiently searchable encryption, 1341 Rabiner, 1989, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, 77, 257, 10.1109/5.18626 Rohlfs, 2012, Familial identification: population structure and relationship distinguishability, PLOS Genet., 8, e1002469, 10.1371/journal.pgen.1002469 Samani, 2015, Quantifying genomic privacy via inference attack with high-order SNV correlations, 32 Schadt, 2012, Bayesian method to predict individual SNP genotypes from gene expression data, Nat. Genet., 44, 603, 10.1038/ng.2248 S. Scutti, What the golden state killer case means for your genetic privacy, 2018, Accessed May 28, 2018. (https://www.cnn.com/2018/04/27/health/golden-state-killer-genetic-privacy/index.html). Shi, 2017, An overview of human genetic privacy, Ann. New York Acad. Sci., 1387, 61, 10.1111/nyas.13211 Shokri, 2017, Membership inference attacks against machine learning models, 3 Shringarpure, 2015, Privacy risks from genomic data-sharing beacons, Am. J. Hum. Genet., 97, 631, 10.1016/j.ajhg.2015.09.010 Stamp, 2004, A revealing introduction to hidden Markov models, 26 L. Sweeney, A. Abu, J. Winn, Identifying participants in the personal genome project by name, 2013. The Genomes Project Consortium, 2015, A global reference for human genetic variation, Nature, 526, 68, 10.1038/nature15393 IGSR: the international genome sample resource, 2019, Accessed April 22. (http://www.internationalgenome.org/), The International Genome Sample Resource (IGSR), 2019Which populations are part of your study?, Accessed April 22. (http://www.internationalgenome.org/category/population/). The National Human Genome Research Institute, 2019, Privacy in genomics, Accessed April 22. (https://www.genome.gov/27561246/privacy-in-genomics). Thorisson, 2005, The international HapMap project web site, Genome Res., 15, 1592, 10.1101/gr.4413105 U.S. Equal Employment Opportunity Commission, Genetic information nondiscrimination act of 2008, 2008, = from Accessed 1 June 2018. https://www.eeoc.gov/laws/statutes/gina.cfm). Wagner, 2017, Evaluating the strength of genomic privacy metrics, ACM Trans. Priv. Secur., 20, 2:1, 10.1145/3020003 Walsh, 2011, Irisplex: a sensitive dna tool for accurate prediction of blue and brown eye colour in the absence of ancestry information, Forensic Sci. Int., 5, 170, 10.1016/j.fsigen.2010.02.004 Wang, 2009, Learning your identity and disease from research papers: information leaks in genome wide association study, 534 Wang, 2016, Infringement of individual privacy via mining differentially private GWAS statistics, 355