Private Ensemble

Private ensembles aim to build accurate machine learning models while rigorously protecting the privacy of the training data. Current research focuses on developing differentially private ensemble methods, such as Private Aggregation of Teacher Ensembles (PATE) and boosted decision trees, and addressing challenges like fairness and information leakage inherent in these approaches. This field is crucial for enabling the use of sensitive data in machine learning applications, particularly in biometrics and other areas where privacy is paramount, while mitigating potential biases and privacy violations.