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Differential Privacy For Regression Modeling In Health: An Evaluation Of Algorithms, Joseph Ficek
Differential Privacy For Regression Modeling In Health: An Evaluation Of Algorithms, Joseph Ficek
USF Tampa Graduate Theses and Dissertations
Background: There is a need for rigorous and standardized methods of privacy protection for shared data in the health sciences. Differential privacy is one such method that has gained much popularity due to its versatility and robustness. This study evaluates differential privacy for explanatory regression modeling in the context of health research.
Methods: Surveyed and newly proposed algorithms were evaluated with respect to the accuracy (bias and RMSE) of coefficient estimates, the empirical coverage probability of confidence intervals, and the power and type I error rates of hypothesis tests. Evaluations took place in both simulated and real data from a …