Open AccessOUP accepted manuscript
Author(s) -
Weiyi Xia,
Yongtai Liu,
Zhiyu Wan,
Yevgeniy Vorobeychik,
Murat Kantacioglu,
Steve Nyemba,
Ellen Wright Clayton,
Bradley Malin
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocaa327
Subject(s) - adversarial system , identification (biology) , computer science , social media , data science , subject (documents) , scale (ratio) , outcome (game theory) , computer security , artificial intelligence , world wide web , botany , physics , quantum mechanics , biology , mathematics , mathematical economics
Re-identification risk methods for biomedical data often assume a worst case, in which attackers know all identifiable features (eg, age and race) about a subject. Yet, worst-case adversarial modeling can overestimate risk and induce heavy editing of shared data. The objective of this study is to introduce a framework for assessing the risk considering the attacker's resources and capabilities.