TY  - JOUR
AU  - El Emam, Khaled
AU  - Mosquera, Lucy
AU  - Bass, Jason
PY  - 2020
DA  - 2020/11/16
TI  - Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation
JO  - J Med Internet Res
SP  - e23139
VL  - 22
IS  - 11
KW  - synthetic data
KW  - privacy
KW  - data sharing
KW  - data access
KW  - de-identification
KW  - open data
AB  - Background: There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them. Objective: The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data. Methods: A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this “meaningful identity disclosure risk.” The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data. Results: The meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively. Conclusions: We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data. 
SN  - 1438-8871
UR  - http://www.jmir.org/2020/11/e23139/
UR  - https://doi.org/10.2196/23139
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33196453
DO  - 10.2196/23139
ID  - info:doi/10.2196/23139
ER  -