Enabling realistic health data re-identification risk assessment through adversarial modeling.

ABSTRACT:

Objective

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.

Materials and methods

We integrate 3 established risk measures (ie, prosecutor, journalist, and marketer risks) and compute re-identification probabilities for data subjects. This probability is dependent on an attacker's capabilities (eg, ability to obtain external identified resources) and the subject's decision on whether to reveal their participation in a dataset. We ill

SUBMITTER: Xia W 

PROVIDER: S-EPMC8711654 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature