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Paper Details

Enabling realistic health data re-identification risk assessment through adversarial modeling.
J Am Med Inform Assoc
9
2021
patients
Computer Security, Confidentiality, Data Anonymization, Humans, Probability, Risk, Risk Assessment
Author NameAffiliation
Weiyi XiaVanderbilt University Medical Center
Weiyi XiaCenter for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center
Yongtai LiuCenter for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center
Yongtai LiuVanderbilt University
Zhiyu WanCenter for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center
Zhiyu WanVanderbilt University
Yevgeniy VorobeychikCenter for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center
Yevgeniy VorobeychikWashington University in St. Louis
Murat KantaciogluUniversity of Texas at Dallas
Steve NyembaVanderbilt University Medical Center
Steve NyembaCenter for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center
E ClaytonCenter for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center
E ClaytonCenter for Biomedical Ethics and Society, Vanderbilt University Medical Center
E ClaytonVanderbilt University
E ClaytonVanderbilt University Medical Center
Bradley A MalinVanderbilt University Medical Center
Bradley A MalinCenter for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center
Bradley A MalinVanderbilt University
Bradley A MalinVanderbilt University Medical Center
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Release 1.2