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DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning
Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for individuals when releasing aggregated statistics or building statistical/machine learning models from data.
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
While these models can be highly accurate in prediction, results obtained from these models with the use of sensitive data may be susceptible to privacy attacks.
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