Differential privacy is the state-of-the-art definition for privacy, that “addresses the paradox of learning nothing about an individual while learning useful information about a population”. In other words, differential privacy guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this thesis, we develop differentially private algorithms to analyze distributed and streaming data.