Example Concept: Differential privacy is a technique that adds noise to data or query results, ensuring that individual user data cannot be easily extracted or identified from aggregated datasets. This method allows AI systems to learn from data patterns without compromising the privacy of individual users, making it a key tool in privacy-preserving data collection.