Local Differential Privacy: a tutorial
This is a tutorial paper, offering a comprehensive guide for researchers and practitioners interested in privacy-preserving data analysis, but it is incremental as it summarizes existing work rather than introducing new methods.
The paper provides an overview of Local Differential Privacy (LDP), a method for enabling statistical computations while protecting individual user privacy without relying on a central authority, covering algorithms for tasks like heavy hitter identification and spatial data collection.
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.