In the era of digital economy， data publication plays a crucial role in data sharing. Histogram data publication is a common method for data publication. However， histogram data publication faces privacy leakage issues. To address this concern， research has been conducted on histogram data publication methods based on Differential Privacy （DP）. Firstly， a brief description of DP and histogram properties， as well as the research on histogram publication methods for both static datasets and streaming data in the past five years both at home and abroad， was provided， and the balance among the grouping number and types of histograms， noise and grouping errors in static data， as well as privacy budget allocation problem， were discussed. Secondly， the issues of data sampling， data prediction， and sliding windows for dynamic data grouping were explored. Additionally， for the DP histogram publication methods oriented to interval tree structures were investigated， the original data was transformed into tree structures， and the discussions about tree-structured data noise addition， tree-structure based optimization， and privacy budget allocation for tree structures were conducted. Moreover， the feasibility and privacy aspects of published histogram data， as well as the issues of query range and accuracy of published histogram data， were discussed. Finally， comparative analysis was conducted on relevant algorithms and their advantages and disadvantages were summarized， quantitative analysis and applicable scenarios for some algorithms were provided， and the future research directions of DP-based histograms in various data scenarios were prospected.