Abstract: As one of the important tools for complex network analysis, community detection can be used to help gain insight into the community structure of the network and the potential relationship between nodes. However, it also brings privacy leakage problems. As a concomitant problem of community detection, community hiding aims to destroy the community structure of the network with minimal edge disturbance cost, and it has received more and more attention from scholars in recent years. However, the existing community hiding methods ignore the network generation mechanism and lack a unified framework for hiding at different scales. Therefore, we propose a community hiding algorithm based on a stochastic block model (HC-SBM), which constructs a unified community hidden framework from the perspective of network generation mechanism, and launches three-scale attacks against community detection algorithm, namely, micro (individual), mesoscopic (community), and macro (network). The principle of this method is to illustrate the generation mechanism of the network based on the stochastic block model, especially the rules and patterns of the formation and division of the network community, mining critical links and link collections in the process of network generation.Finally, the network community structure is destroyed at the minimum cost of perturbation. Through extensive experiments on real networks and comparisons with several mainstream baseline algorithms, the proposed HC-SBM algorithm is shown to be superior in terms of community hiding effect.