OUP accepted manuscript | Zendy

Weiyi Xia | Zendy; Yongtai Liu | Zendy; Zhiyu Wan | Zendy; Yevgeniy Vorobeychik | Zendy; Murat Kantacioglu | Zendy; Steve Nyemba | Zendy; Ellen Wright Clayton | Zendy; Bradley Malin | Zendy

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OUP accepted manuscript

Author(s) -

Weiyi Xia,

Yongtai Liu,

Zhiyu Wan,

Yevgeniy Vorobeychik,

Murat Kantacioglu,

Steve Nyemba,

Ellen Wright Clayton,

Bradley Malin

Publication year - 2020

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.

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