Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
Open accessAbstract
We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Genomic data can never be truly anonymized, and a steady stream of provocative studies has demonstrated that it is possible to discover the identities of individuals whose genomic data had otherwise been considered deidentified.
“However, this rapidly evolving research arena has been accompanied by equally rapid confirmation that genomic data can never be truly anonymized. A steady stream of provocative studies (51, 62, 68, 122) has demonstrated that it is possible to discover the identities of individuals whose genomic data had otherwise been considered deidentified.”
From Lessons from HeLa Cells: The Ethics and Policy of Biospecimens (2016) · cites this paper as [62]
The Discussion shows that summary-level statistics, used in part to mask individual-level data, can reveal whether a specific person is in a study. That supports a privacy risk, but the method tests for the presence of a person whose genotype is already known; it does not demonstrate discovering an unknown person's identity or establish that genomic data can never be anonymized. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442 · see citing works
The International HapMap Project
†The International HapMap Consortium, Genotyping centres: Baylor College of Medicine and ParAllele BioScience, Gibbs, R. A., Belmont, J. W., Hardenbol, P., Willis, T. D., Yu, F., Chinese HapMap Consortium, Yang, H., Ch'ang, L.-Y., et al. (2003) · Nature · doi:10.1038/nature02168 · Cited by 6,057 · see citing works
Encoded evidence: DNA in forensic analysis
Jobling, M. A., & Gill, P. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1455 · Cited by 624 · see citing works
Large-scale genotyping of complex DNA
Kennedy, G., Matsuzaki, H., Dong, S.-L., Liu, W.-M., Huang, J., Liu, G., Su, X., Cao, M., Chen, W., Zhang, J., et al. (2003) · Nature Biotechnology · doi:10.1038/nbt869 · Cited by 609 · see citing works
Developing a SNP panel for forensic identification of individuals
Kídd, K. K., PAKSTIS, A. J., Speed, W. C., Grigorenko, E. L., Kajuna, S. L. B., Karoma, N. J., Kungulilo, S., Kim, J.-J., Lu, R., Odunsi, A., et al. (2005) · Forensic Science International · doi:10.1016/j.forsciint.2005.11.017 · Cited by 288 · see citing works
Analysis and interpretation of mixed forensic stains using DNA STR profiling
Clayton, T., Whitaker, J., Sparkes, R. L., & Gill, P. (1998) · Forensic Science International · doi:10.1016/s0379-0738(97)00175-8 · Cited by 287 · see citing works
Chakraborty, R., Meagher, T. R., & Smouse, P. E. (1988) · Genetics · doi:10.1093/genetics/118.3.527 · Cited by 243 · see citing works
Coble, M. D., Just, R. S., O’Callaghan, J. E., Letmanyi, I. H., Peterson, C. T., Irwin, J. A., & Parsons, T. J. (2004) · International Journal of Legal Medicine · doi:10.1007/s00414-004-0427-6 · Cited by 238 · see citing works
Weir, B. S., Triggs, C. M., Starling, L., Stowell, L., Walsh, K. A. J., & Buckleton, J. (1997) · Journal of Forensic Sciences · doi:10.1520/jfs14100j · Cited by 175 · see citing works
Pearson, J. V., Huentelman, M. J., Halperin, R. F., Tembe, W., Melquist, S., Homer, N., Marcel Brun, A., Szelinger, S., Coon, K. D., Zismann, V., et al. (2006) · The American Journal of Human Genetics · doi:10.1086/510686 · Cited by 153 · see citing works
Self-citationLikelihood-based inference for genetic correlation coefficients
Balding, D. J. (2003) · Theoretical Population Biology · doi:10.1016/s0040-5809(03)00007-8 · Cited by 146 · see citing works
Vallone, P. M., Just, R. S., Coble, M. D., Butler, J. M., & Parsons, T. J. (2004) · International Journal of Legal Medicine · doi:10.1007/s00414-004-0428-5 · Cited by 144 · see citing works
Informativity assessment for biallelic single nucleotide polymorphisms
Krawczak, M. (1999) · Electrophoresis · doi:10.1002/(sici)1522-2683(19990101)20:8 · Cited by 113 · see citing works
Parsons, T. J., & Coble, M. D. (2001) · PubMed · Cited by 106 · see citing works
Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays
MacGregor, S., Zhao, Z., Henders, A. K., Martin, N. G., Montgomery, G. W., & Visscher, P. M. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkm1060 · Cited by 105 · see citing works
PENDULUM—a guideline-based approach to the interpretation of STR mixtures
Bill, M., Gill, P., Curran, J. M., Clayton, T., Pinchin, R., Healy, M., & Buckleton, J. (2004) · Forensic Science International · doi:10.1016/j.forsciint.2004.06.037 · Cited by 90 · see citing works
Identification and separation of DNA mixtures using peak area information
Cowell, R. G., Lauritzen, S. L., & Mortera, J. (2006) · Forensic Science International · doi:10.1016/j.forsciint.2006.03.021 · Cited by 69 · see citing works
Estimating the number of contributors to a DNA profile
Egeland, T., Dalen, I., & Mostad, P. (2003) · International Journal of Legal Medicine · doi:10.1007/s00414-003-0382-7 · Cited by 53 · see citing works
Interpretation of complex forensic DNA mixtures.
Ladd, C., Lee, H. C., Yang, N., & Bieber, F. R. (2001) · PubMed · Cited by 49 · see citing works
Toward increased utility of mtDNA in forensic identifications
Just, R. S., Irwin, J. A., O’Callaghan, J. E., Saunier, J. L., Coble, M. D., Vallone, P. M., Butler, J. M., Barritt, S. M., & Parsons, T. J. (2004) · Forensic Science International · doi:10.1016/j.forsciint.2004.09.045 · Cited by 47 · see citing works
Interpreting DNA mixtures with the presence of relatives
Hu, Y., & Fung, W. K. (2003) · International Journal of Legal Medicine · doi:10.1007/s00414-002-0321-z · Cited by 28 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [10]Goodwin W Linacre A Vanezis P 1999 The use of mitochondrial DNA and short tandem repeat typing in the identification of air crash victims. Electrophoresis 20 1707 1711 10435435
Cited by
Sorted by citations
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365 · see citing works
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works
Benefits and limitations of genome-wide association studies
Tam, V., Patel, N. K., Turcotte, M., Bossé, Y., Paré, G., & Meyre, D. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0127-1 · Cited by 2,092 · see citing works
Nasr, M., Shokri, R., & Houmansadr, A. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00065 · Cited by 1,760 · see citing works
Exploiting Unintended Feature Leakage in Collaborative Learning
Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734 · see citing works
Mining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680 · see citing works
Identifying Personal Genomes by Surname Inference
Gymrek, M., McGuire, A. L., Golan, D., Halperin, E., & Erlich, Y. (2013) · Science · doi:10.1126/science.1229566 · Cited by 1,228 · see citing works
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194 · see citing works
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2018.00027 · Cited by 1,001 · see citing works
(2011) · PubMed · Cited by 990 · see citing works
Registries for Evaluating Patient Outcomes: A User’s Guide
(2020) · doi:10.23970/ahrqepcregistries4 · Cited by 968 · see citing works
Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., & Backes, M. (2019) · Proceedings 2019 Network and Distributed System Security Symposium · doi:10.14722/ndss.2019.23119 · Cited by 852 · see citing works
Little, J., Higgins, J. P. T., Ioannidis, J. P. A., Moher, D., Gagnon, F., von Elm, E., Khoury, M. J., Cohen, B., Davey-Smith, G., Grimshaw, J., et al. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000022 · Cited by 811 · see citing works
Manzoni, C., Kia, D. A., Vandrovcová, J., Hardy, J., Wood, N. W., Lewis, P. A., & Ferrari, R. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw114 · Cited by 809 · see citing works
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
Fredrikson, M., Lantz, E., Jha, S., Lin, S. M., Page, D., & Ristenpart, T. (2014) · PubMed · Cited by 658 · see citing works
Membership Inference Attacks on Machine Learning: A Survey
Hu, H., Salcic, Z. A., Sun, L., Dobbie, G., Yu, P. S., & Zhang, X. (2022) · ACM Computing Surveys · doi:10.1145/3523273 · Cited by 586 · see citing works
Membership Inference Attacks From First Principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., & Tramèr, F. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833649 · Cited by 562 · see citing works
Practical aspects of imputation-driven meta-analysis of genome-wide association studies
de Bakker, P. I. W., Ferreira, M. A. R., Jia, X., Neale, B. M., Raychaudhuri, S., & Voight, B. F. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn288 · Cited by 531 · see citing works
Machine Learning Models that Remember Too Much
Song, C., Ristenpart, T., & Shmatikov, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134077 · Cited by 526 · see citing works
Routes for breaching and protecting genetic privacy
Erlich, Y., & Narayanan, A. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3723 · Cited by 466 · see citing works
Dissecting the genetics of complex traits using summary association statistics
Paşaniuc, B., & Price, A. L. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.142 · Cited by 444 · see citing works
Machine Learning with Membership Privacy using Adversarial Regularization
Nasr, M., Shokri, R., & Houmansadr, A. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243855 · Cited by 458 · see citing works
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses
Wallace, C. (2020) · PLOS Genetics · doi:10.1371/journal.pgen.1008720 · Cited by 441 · see citing works
LOGAN: Membership Inference Attacks Against Generative Models
Hayes, J., Melis, L., Danezis, G., & De Cristofaro, E. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0008 · Cited by 433 · see citing works
A Systematic Review of Re-Identification Attacks on Health Data
El Emam, K., Jonker, E., Arbuckle, L., & Malin, B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028071 · Cited by 427 · see citing works
Dong, J., Roth, A., & Su, W. (2022) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12454 · Cited by 428 · see citing works
Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing
Beaulieu‐Jones, B. K., Wu, Z. S., Williams, C., Lee, R., Bhavnani, S. P., Byrd, J. B., & Greene, C. S. (2019) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.118.005122 · Cited by 407 · see citing works
Little, J., Higgins, J. P. T., Ioannidis, J. P. A., Moher, D., Gagnon, F., von Elm, E. B., Khoury, M. J., Cohen, B. J., Smith, G. D., Grimshaw, J., et al. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20410 · Cited by 348 · see citing works
Jia, J., Salem, A., Michael Backes, A., Zhang, Y., & Gong, N. Z. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363201 · Cited by 374 · see citing works
Little, J., Higgins, J. P. T., Ioannidis, J. P. A., Moher, D., Gagnon, F., von Elm, E. B., Khoury, M. J., Cohen, B. J., Smith, G. D., Grimshaw, J., et al. (2009) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2009.02125.x · Cited by 324 · see citing works
Big Data in Public Health: Terminology, Machine Learning, and Privacy
Mooney, S. J., & Pejaver, V. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014208 · Cited by 325 · see citing works
An Open Access Database of Genome-wide Association Results
Johnson, A. D., & O’Donnell, C. J. (2009) · BMC Medical Genetics · doi:10.1186/1471-2350-10-6 · Cited by 323 · see citing works
"You Might Also Like:" Privacy Risks of Collaborative Filtering
Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.40 · Cited by 311 · see citing works
Taylor, K. D., Norris, J. M., & Rotter, J. I. (2007) · Diabetes · doi:10.2337/db07-1324 · Cited by 307 · see citing works
Privacy-Preserving Data Mining: Methods, Metrics, and Applications
Mendes, R., & Vilela, J. P. (2017) · IEEE Access · doi:10.1109/access.2017.2706947 · Cited by 297 · see citing works
Data sharing in genomics — re-shaping scientific practice
Kaye, J. S., Heeney, C., Hawkins, N., de Vries, J., & Boddington, P. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2573 · Cited by 294 · see citing works
Replication in Genome-Wide Association Studies
Kraft, P. L., Zeggini, E., & Ioannidis, J. P. A. (2009) · Statistical Science · doi:10.1214/09-sts290 · Cited by 292 · see citing works
Privacy challenges and research opportunities for genomic data sharing
Bonomi, L., Huang, Y., & Lucila Ohno-machado, A. (2020) · Nature Genetics · doi:10.1038/s41588-020-0651-0 · Cited by 291 · see citing works
Public Opinion about the Importance of Privacy in Biobank Research
Kaufman, D. J., Murphy-Bollinger, J., Scott, J., & Hudson, K. L. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.10.002 · Cited by 279 · see citing works
Shyn, S. I., Shi, J., Kraft, J. B., Potash, J. B., Knowles, J. A., Weissman, M. M., Garriock, H. A., Yokoyama, J. S., McGrath, P. J., Peters, E. J., et al. (2009) · Molecular Psychiatry · doi:10.1038/mp.2009.125 · Cited by 270 · see citing works
Kifer, D., & Machanavajjhala, A. (2014) · ACM Transactions on Database Systems · doi:10.1145/2514689 · Cited by 270 · see citing works
Evaluating Differentially Private Machine Learning in Practice
Jayaraman, B., & Evans, D. (2019) · arXiv · doi:10.48550/arxiv.1902.08874 · Cited by 265 · see citing works
Naveed, M., Ayday, E., Clayton, E. W., Fellay, J., Gunter, C. A., Hubaux, J.-P., Malin, B. A., & Wang, X. (2015) · ACM Computing Surveys · doi:10.1145/2767007 · Cited by 261 · see citing works
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
Cheng, L., Varshney, K. R., & Liu, H. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12814 · Cited by 261 · see citing works
The ClinSeq Project: Piloting large-scale genome sequencing for research in genomic medicinePreprint
Biesecker, L. G., Mullikin, J. C., Facio, F. M., Turner, C., Cherukuri, P. F., Blakesley, R. W., Bouffard, G. G., Chines, P. S., Cruz, P., Hansen, N. F., et al. (2009) · Genome Research · doi:10.1101/gr.092841.109 · Cited by 260 · see citing works
Exposed! A Survey of Attacks on Private Data
Dwork, C., Smith, A., Steinke, T., & Ullman, J. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054123 · Cited by 259 · see citing works
Kahn, S. D. (2011) · Science · doi:10.1126/science.1197891 · Cited by 257 · see citing works
Is Deidentification Sufficient to Protect Health Privacy in Research?
Rothstein, M. A. (2010) · The American Journal of Bioethics · doi:10.1080/15265161.2010.494215 · Cited by 255 · see citing works
Information Leakage in Embedding Models
Song, C., & Raghunathan, A. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417270 · Cited by 245 · see citing works
The tension between data sharing and the protection of privacy in genomics research
Kaye, J. (2012) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-082410-101454 · Cited by 240 · see citing works
Differentially private histogram publication
Xu, J. W., Zhang, Z., Xiao, X., Yang, Y., Yu, G., & Winslett, M. (2013) · The VLDB Journal · doi:10.1007/s00778-013-0309-y · Cited by 239 · see citing works
Clinical Trial Participants’ Views of the Risks and Benefits of Data Sharing
Mello, M. M., Lieou, V., & Goodman, S. N. (2018) · New England Journal of Medicine · doi:10.1056/nejmsa1713258 · Cited by 237 · see citing works
Lo, B. (2015) · JAMA · doi:10.1001/jama.2015.292 · Cited by 232 · see citing works
Knock Knock, Who's There? Membership Inference on Aggregate Location Data
Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2018) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23183 · Cited by 232 · see citing works
Dynamic Backdoor Attacks Against Machine Learning Models
Salem, A., Wen, R., Michael Backes, A., Ma, S., & Zhang, Y. (2022) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp53844.2022.00049 · Cited by 227 · see citing works
Responsible Conduct of Research
Shamoo, A. E., & Resnik, D. B. (2022) · doi:10.1093/oso/9780197547090.001.0001 · Cited by 224 · see citing works
Learning your identity and disease from research papers
Wang, R., Li, Y. F., Wang, X., Tang, H., & Zhou, X. (2009) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1653662.1653726 · Cited by 206 · see citing works
Privacy risks from genomic data-sharing beacons
Shringarpure, S. S., & Bustamante, C. D. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.09.010 · Cited by 211 · see citing works
Suwinski, P., Ong, C. K., Ling, M. H., Poh, Y. M., Khan, A. M., & Ong, H. S. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00049 · Cited by 213 · see citing works
The HapMap and Genome-Wide Association Studies in Diagnosis and Therapy
Manolio, T. A., & Collins, F. S. (2009) · Annual Review of Medicine · doi:10.1146/annurev.med.60.061907.093117 · Cited by 208 · see citing works
Genomic research and wide data sharing: Views of prospective participants
Trinidad, S. B., Fullerton, S. M., Bares, J. M., Jarvik, G. P., Larson, E. B., & Burke, W. (2010) · Genetics in Medicine · doi:10.1097/gim.0b013e3181e38f9e · Cited by 202 · see citing works
Genomic privacy and limits of individual detection in a pool
Sankararaman, S., Obozinski, G., Jordan, M. I., & Halperin, E. (2009) · Nature Genetics · doi:10.1038/ng.436 · Cited by 204 · see citing works
Informed Consent in Genomics and Genetic Research
McGuire, A. L., & Beskow, L. M. (2010) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-082509-141711 · Cited by 204 · see citing works
Publishing data from electronic health records while preserving privacy: A survey of algorithms
Gkoulalas-Divanis, A., Loukides, G., & Sun, J. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.06.002 · Cited by 203 · see citing works
Membership Leakage in Label-Only Exposures
Li, Z., & Zhang, Y. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484575 · Cited by 226 · see citing works
Auditing Data Provenance in Text-Generation Models
Song, C., & Shmatikov, V. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330885 · Cited by 192 · see citing works
Characterizing biobank organizations in the U.S.: results from a national survey
Henderson, G. E., Cadigan, R. J., Edwards, T. P., Conlon, I., Nelson, A. G., Evans, J. P., Davis, A. M., Zimmer, C., & Weiner, B. J. (2013) · Genome Medicine · doi:10.1186/gm407 · Cited by 198 · see citing works
Improved ancestry inference using weights from external reference panels
Chen, C., Pollack, S. J., Hunter, D. J., Hirschhorn, J. N., Kraft, P. L., & Price, A. L. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt144 · Cited by 185 · see citing works
Enhanced Membership Inference Attacks against Machine Learning Models
Ye, J., Maddi, A., Murakonda, S. K., Bindschaedler, V., & Shokri, R. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560675 · Cited by 204 · see citing works
Differential Privacy and Machine Learning: a Survey and ReviewPreprint
Ji, Z., Lipton, Z. C., & Elkan, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7584 · Cited by 239 · see citing works
Fast and accurate imputation of summary statistics enhances evidence of functional enrichment
Paşaniuc, B., Zaitlen, N., Shi, H., Bhatia, G., Gusev, A., Pickrell, J. K., Hirschhorn, J. N., Strachan, D. P., Patterson, N. J., & Price, A. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu416 · Cited by 180 · see citing works
Investigative genetic genealogy: Current methods, knowledge and practice
Kling, D., Phillips, C., Kennett, D., & Tillmar, A. O. (2021) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2021.102474 · Cited by 190 · see citing works
Little, J., Higgins, J. P. T., Ioannidis, J. P. A., Moher, D., Gagnon, F., von Elm, E. B., Khoury, M. J., Cohen, B. J., Smith, G. D., Grimshaw, J., et al. (2009) · Human Genetics · doi:10.1007/s00439-008-0592-7 · Cited by 185 · see citing works
Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge
Narayanan, A., Shi, E., & Rubinstein, B. I. P. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033446 · Cited by 180 · see citing works
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Wood, A., Najarian, K., & Kahrobaei, D. (2021) · ACM Computing Surveys · doi:10.1145/3394658 · Cited by 179 · see citing works
Wolfson, M. C., Wallace, S. E., Masca, N. G. D., Rowe, G., Sheehan, N. A., Ferretti, V., Laflamme, P., Tobin, M. D., Macleod, J., Little, J., et al. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq111 · Cited by 177 · see citing works
Turner, J. A. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00137 · Cited by 176 · see citing works
Privacy-preserving data exploration in genome-wide association studies
Johnson, A., & Shmatikov, V. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487687 · Cited by 172 · see citing works
Membership Inference Attack against Differentially Private Deep Learning Model.
Rahman, M. A., Rahman, T., Laganière, R., & Mohammed, N. (2018) · Cited by 167 · see citing works
White, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176 · see citing works
“…individuals from anonymous datasets (Netflix Prize Privacy Concerns—https://en.wikipedia.org/wiki/Netflix_Prize#Privacy_ concerns) and the example of genetic reidentification from datasets anonymized per NIH guidelines (Cassa, Wieland, & Mandl, 2008; El Emam, 2011; Homer et al., 2008).”
MethodGenetic Modifiers and Rare Mendelian Disease
Rahit, K. M. T. H., & Tarailo‐Graovac, M. (2020) · Genes · doi:10.3390/genes11030239 · Cited by 171 · see citing works
Little, J., Higgins, J. P. T., Ioannidis, J. P. A., Moher, D., Gagnon, F., von Elm, E. B., Khoury, M. J., Cohen, B., Smith, G. D., Grimshaw, J., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-3-200902030-00011 · Cited by 171 · see citing works
Visualizing genomes: techniques and challenges
Nielsen, C. B., Cantor, M., Dubchak, I. L., Gordon, D., & Wang, T. (2010) · Nature Methods · doi:10.1038/nmeth.1422 · Cited by 167 · see citing works
Data Integration Challenges for Machine Learning in Precision Medicine
Martínez-García, M., & Hernández-Lemus, E. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.784455 · Cited by 158 · see citing works
Matise, T. C., Ambite, J. L., Buyske, S. G., Carlson, C. S., Cole, S. A., Crawford, D. C., Haiman, C. A., HEISS, G. M., Kooperberg, C. L., Marchand, L. L., et al. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr160 · Cited by 168 · see citing works
Beck, T., Hastings, R. K., Hesketh, S., Free, R. C., & Brookes, A. J. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2013.274 · Cited by 166 · see citing works
Understanding Membership Inferences on Well-Generalized Learning Models
Long, Y., Bindschaedler, V., Wang, L., Bu, D., Wang, X., Tang, H., Gunter, C. A., & Chen, K. (2018) · arXiv · doi:10.48550/arxiv.1802.04889 · Cited by 166 · see citing works
Human genomics projects and precision medicine
Carrasco-Ramiro, F., Peiró‐Pastor, R., & Aguado, B. (2017) · Gene Therapy · doi:10.1038/gt.2017.77 · Cited by 164 · see citing works
Supporting Regularized Logistic Regression Privately and Efficiently
Li, W., Liu, H., Yang, P., & Xie, W. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156479 · Cited by 162 · see citing works
Adversarial machine learning :
Vassilev, A., Oprea, A., Fordyce, A. J., & Anderson, H. S. (2024) · doi:10.6028/nist.ai.100-2e2023 · Cited by 193 · see citing works
An economic analysis of privacy protection and statistical accuracy as social choices
Abowd, J. M., & Schmutte, I. M. (2019) · American Economic Review · doi:10.1257/aer.20170627 · Cited by 162 · see citing works
The changing privacy landscape in the era of big data
Schadt, E. E. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.47 · Cited by 161 · see citing works
From consent to institutions: Designing adaptive governance for genomic biobanks
O’Doherty, K. C., Burgess, M., Edwards, K., Gallagher, R. P., Hawkins, A. K., Kaye, J. S., McCaffrey, V., & Winickoff, D. E. (2011) · Social Science & Medicine · doi:10.1016/j.socscimed.2011.05.046 · Cited by 156 · see citing works
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Salem, A., Bhattacharya, A., Backes, M., Fritz, M., & Zhang, Y. (2020) · Universität des Saarlandes · doi:10.22028/d291-47370 · Cited by 157 · see citing works
Sharing Heterogeneous Data: The National Database for Autism Research
Hall, D., Huerta, M. F., McAuliffe, M., & Farber, G. K. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9151-4 · Cited by 154 · see citing works
A rigorous and customizable framework for privacy
Kifer, D., & Machanavajjhala, A. (2012) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/2213556.2213571 · Cited by 154 · see citing works
Responsible, practical genomic data sharing that accelerates research
Byrd, J. B., Greene, A. C., Prasad, D. V., Jiang, X., & Greene, C. S. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0257-5 · Cited by 154 · see citing works
Lessons from HeLa Cells: The Ethics and Policy of Biospecimens
Beskow, L. M. (2016) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022536 · Cited by 146 · see citing works
“A steady stream of provocative studies (51, 62, 68, 122) has demonstrated that it is possible to discover the identities of individuals whose genomic data had otherwise been considered deidentified.”
Background
- Authors
- Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J. V., Stephan, D. A., Nelson, S. F., & Craig, D. W.
- Affiliations
- Translational Genomics Research Institute (US)
- University of California, Los Angeles (US)
- Corresponding author
- Craig, David W.
- ORCID
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-08-29
- Volume
- 4
- Issue
- 8
- Pages
- e1000167
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC2516199
- OpenAlex
- W2040228409
- Cited by
- 1,221
- References
- 21
- Citations by year
- 2026: 24 · 2025: 42 · 2024: 54 · 2023: 71 · 2022: 81 · 2021: 76 · 2020: 89 · 2019: 103 · 2018: 87 · 2017: 81 · 2016: 65 · 2015: 59 · 2014: 79 · 2013: 62 · 2012: 63
- FWCI
- 34.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome Trust (076113)
- Translational Genomics Research Institute
- National Institutes of Health
- National Heart, Lung, and Blood Institute (U01HL086528)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- DNA mixture analysis, single nucleotide polymorphisms, forensic DNA identification, sample contamination, genome-wide association study
- MeSH terms
- Computer Simulation, Genetics, Medical, Genotype, Humans, Genome, Human, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review