Private Aggregation of Teacher Ensembles (PATE)

Private Aggregation of Teacher Ensembles (PATE) is a privacy-preserving machine learning approach that uses multiple models (teachers) trained on sensitive data to guide the creation of a new, private student model. Instead of exposing individual data points, it combines the teachers’ predictions on a query and adds noise to the aggregated result, ensuring that no single data point can be identified. This method enables the development of accurate models while protecting the privacy of the original data, making it suitable for sensitive applications like healthcare or finance.