Differential Privacy ist ein datenschutzwahrendes Rahmenwerk, das begrenzt, was aus dem Ergebnis einer Berechnung oder eines Modells über eine einzelne Person abgeleitet werden kann. Es verwendet häufig Techniken wie Stichprobenziehung und kontrolliertes Hinzufügen von Rauschen, um das Risiko zu verringern, dass sensible Trainings- oder Analysedaten offengelegt werden.
In machine learning, an anonymization approach to protect any sensitive data (for example, an individual's personal information) included in a model's training set from being exposed. This approach ensures that the model doesn't learn or remember much about a specific individual. This is accomplished by sampling and adding noise during model training to obscure individual data points, mitigating the risk of exposing sensitive training data. Differential privacy is also used outside of machine learning. For example, data scientists sometimes use differential privacy to protect individual privacy when computing product usage statistics for different demographics.