Example Concept: Differential privacy adds noise to datasets or query results to prevent the identification of individual data points, ensuring privacy while maintaining overall data utility. Federated learning allows models to be trained across multiple decentralized devices without sharing raw data, thus preserving privacy. Secure multi-party computation enables collaborative computation on encrypted data, ensuring that no single party can access the underlying data.