{"status":"public","acknowledgement":"This research was partially supported by BCS- 0941518 to the Department of Statistics at Carnegie Mellon University.","volume":8744,"scopus_import":1,"main_file_link":[{"url":"http://arxiv.org/abs/1407.8067","open_access":"1"}],"month":"01","publication":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","citation":{"ista":"Yu F, Rybar M, Uhler C, Fienberg S. 2014. Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PSD: Privacy in Statistical Databases, LNCS, vol. 8744, 170–184.","mla":"Yu, Fei, et al. “Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases.” <i>Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>, edited by Josep Domingo Ferrer, vol. 8744, Springer, 2014, pp. 170–84, doi:<a href=\"https://doi.org/10.1007/978-3-319-11257-2_14\">10.1007/978-3-319-11257-2_14</a>.","apa":"Yu, F., Rybar, M., Uhler, C., &#38; Fienberg, S. (2014). Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. In J. Domingo Ferrer (Ed.), <i>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i> (Vol. 8744, pp. 170–184). Ibiza, Spain: Springer. <a href=\"https://doi.org/10.1007/978-3-319-11257-2_14\">https://doi.org/10.1007/978-3-319-11257-2_14</a>","chicago":"Yu, Fei, Michal Rybar, Caroline Uhler, and Stephen Fienberg. “Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases.” In <i>Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>, edited by Josep Domingo Ferrer, 8744:170–84. Springer, 2014. <a href=\"https://doi.org/10.1007/978-3-319-11257-2_14\">https://doi.org/10.1007/978-3-319-11257-2_14</a>.","ama":"Yu F, Rybar M, Uhler C, Fienberg S. Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. In: Domingo Ferrer J, ed. <i>Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>. Vol 8744. Springer; 2014:170-184. doi:<a href=\"https://doi.org/10.1007/978-3-319-11257-2_14\">10.1007/978-3-319-11257-2_14</a>","ieee":"F. Yu, M. Rybar, C. Uhler, and S. Fienberg, “Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases,” in <i>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>, Ibiza, Spain, 2014, vol. 8744, pp. 170–184.","short":"F. Yu, M. Rybar, C. Uhler, S. Fienberg, in:, J. Domingo Ferrer (Ed.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 170–184."},"publication_status":"published","quality_controlled":"1","doi":"10.1007/978-3-319-11257-2_14","alternative_title":["LNCS"],"year":"2014","_id":"2047","editor":[{"full_name":"Domingo Ferrer, Josep","first_name":"Josep","last_name":"Domingo Ferrer"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["1407.8067"]},"author":[{"full_name":"Yu, Fei","first_name":"Fei","last_name":"Yu"},{"first_name":"Michal","last_name":"Rybar","id":"2B3E3DE8-F248-11E8-B48F-1D18A9856A87","full_name":"Rybar, Michal"},{"full_name":"Uhler, Caroline","last_name":"Uhler","id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7008-0216","first_name":"Caroline"},{"first_name":"Stephen","last_name":"Fienberg","full_name":"Fienberg, Stephen"}],"date_updated":"2021-01-12T06:54:57Z","oa":1,"abstract":[{"text":"Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems with convex penalty functions and selecting the penalty parameters by cross-validation. In particular, we focus on penalized logistic regression with elastic-net regularization, a method widely used to in GWAS analyses to identify disease-causing genes. We show how a differentially private procedure for penalized logistic regression with elastic-net regularization can be applied to the analysis of GWAS data and evaluate our method’s performance.","lang":"eng"}],"conference":{"end_date":"2014-09-19","start_date":"2014-09-17","location":"Ibiza, Spain","name":"PSD: Privacy in Statistical Databases"},"date_published":"2014-01-01T00:00:00Z","oa_version":"Submitted Version","department":[{"_id":"KrPi"},{"_id":"CaUh"}],"publisher":"Springer","type":"conference","publist_id":"5004","project":[{"_id":"25636330-B435-11E9-9278-68D0E5697425","grant_number":"11-NSF-1070","name":"ROOTS Genome-wide Analysis of Root Traits"}],"intvolume":"      8744","page":"170 - 184","day":"01","title":"Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases","date_created":"2018-12-11T11:55:24Z","language":[{"iso":"eng"}]}