  In the past decade analysis of big data has proven to be extremely valuable
in many contexts. Local Differential Privacy (LDP) is a state-of-the-art
approach which allows statistical computations while protecting each individual
user's privacy. Unlike Differential Privacy no trust in a central authority is
necessary as noise is added to user inputs locally. In this paper we give an
overview over different LDP algorithms for problems such as locally private
heavy hitter identification and spatial data collection. Finally, we will give
an outlook on open problems in LDP.
