Privacy protection has become the focus of information security research. Differential privacy has been highly recommended by the theoretical community since 2006. In recent years, the local differential privacy in the industry crowdsourcing model has attracted much attention. We analyze the local differential privacy model from the perspective of theoretical research and engineering practice, and summarize the applications of local differential privacy theory in data collection and data analysis. In the aspect of data collection, the main research and application results of local differential privacy are introduced, and the methods are analyzed and compared from the perspective of differential privacy. In the aspect of data analysis, the implementation and analysis of local differential privacy in encoding, decoding and statistics are discussed, and these algorithms are analyzed theoretically. Finally, on the basis of indepth comparison and analysis of existing technologies, the challenges and research directions of local differential privacy techniques are summarized.