{"citation":{"ieee":"S. Fienberg, A. Slavkovic, and C. Uhler, “Privacy Preserving GWAS Data Sharing,” presented at the Proceedings of the 11th IEEE International Conference on Data Mining, 2011.","mla":"Fienberg, Stephen, et al. <i>Privacy Preserving GWAS Data Sharing</i>. IEEE, 2011, doi:<a href=\"https://doi.org/10.1109/ICDMW.2011.140\">10.1109/ICDMW.2011.140</a>.","ama":"Fienberg S, Slavkovic A, Uhler C. Privacy Preserving GWAS Data Sharing. In: IEEE; 2011. doi:<a href=\"https://doi.org/10.1109/ICDMW.2011.140\">10.1109/ICDMW.2011.140</a>","short":"S. Fienberg, A. Slavkovic, C. Uhler, in:, IEEE, 2011.","apa":"Fienberg, S., Slavkovic, A., &#38; Uhler, C. (2011). Privacy Preserving GWAS Data Sharing. Presented at the Proceedings of the 11th IEEE International Conference on Data Mining, IEEE. <a href=\"https://doi.org/10.1109/ICDMW.2011.140\">https://doi.org/10.1109/ICDMW.2011.140</a>","chicago":"Fienberg, Stephen, Aleksandra Slavkovic, and Caroline Uhler. “Privacy Preserving GWAS Data Sharing.” IEEE, 2011. <a href=\"https://doi.org/10.1109/ICDMW.2011.140\">https://doi.org/10.1109/ICDMW.2011.140</a>.","ista":"Fienberg S, Slavkovic A, Uhler C. 2011. Privacy Preserving GWAS Data Sharing. Proceedings of the 11th IEEE International Conference on Data Mining."},"date_created":"2018-12-11T12:00:34Z","month":"01","publisher":"IEEE","year":"2011","status":"public","day":"01","date_published":"2011-01-01T00:00:00Z","date_updated":"2021-01-12T07:40:05Z","publist_id":"3766","type":"conference","extern":1,"conference":{"name":"Proceedings of the 11th IEEE International Conference on Data Mining"},"doi":"10.1109/ICDMW.2011.140","author":[{"full_name":"Fienberg, Stephen E","first_name":"Stephen","last_name":"Fienberg"},{"last_name":"Slavkovic","first_name":"Aleksandra","full_name":"Slavkovic, Aleksandra"},{"full_name":"Caroline Uhler","first_name":"Caroline","orcid":"0000-0002-7008-0216","last_name":"Uhler","id":"49ADD78E-F248-11E8-B48F-1D18A9856A87"}],"_id":"2960","title":"Privacy Preserving GWAS Data Sharing","quality_controlled":0,"publication_status":"published","abstract":[{"text":"Traditional statistical methods for the confidentiality protection for statistical databases do not scale well to deal with GWAS (genome-wide association studies) databases and external information on them. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach which provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information. Building on such notions, we propose new methods to release aggregate GWAS data without compromising an individual's privacy. We present methods for releasing differentially private minor allele frequencies, chi-square statistics and p-values. We compare these approaches on simulated data and on a GWAS study of canine hair length involving 685 dogs. We also propose a privacy-preserving method for finding genome-wide associations based on a differentially private approach to penalized logistic regression.","lang":"eng"}]}