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Hospital discharge data use agreement. https:\/\/www.dshs.texas.gov\/THCIC\/Hospitals\/Download.shtm.  Of State Health Services T. D. Hospital discharge data use agreement. https:\/\/www.dshs.texas.gov\/THCIC\/Hospitals\/Download.shtm."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44851-9_44"},{"key":"e_1_3_2_2_49_1","volume-title":"Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. arXiv preprint arXiv:1806.01246","author":"Salem A.","year":"2018","unstructured":"Salem , A. , Zhang , Y. , Humbert , M. , Fritz , M. , and Backes , M . Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. arXiv preprint arXiv:1806.01246 ( 2018 ). Salem, A., Zhang, Y., Humbert, M., Fritz, M., and Backes, M. 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