The invention discloses a 
privacy protection method oriented to large-scale graph data 
dissemination. The 
privacy protection method specifically comprises the steps of 1, uniformly dividing original graph data into multiple subsidiary blocks; 2, reading 
cut connection sides, comparing node degrees of the two ends of each 
cut connection side, newly adding 
noise nodes in a subsidiary block of each node with a large node degree, and reserving the 
cut connection sides through a 
ligature of the corresponding node with the large node degree and the corresponding 
noise node; 3, constructing an isomorphism 
block matrix, wherein subsidiary 
block structure information is compared with the isomorphism 
block matrix, and isomorphism is conducted in the mode of adding the 
noise sides to 
complete graph data anonymous protection. The time needed for the overall graph data anonymous protection is lowered by one order of magnitudes, and the efficiency of an anonymization process is achieved; finally ananonymous graph satisfies a k anonymous mechanism, and the safety of anonymization is achieved. By means of the 
privacy protection method oriented to the large-scale graph data 
dissemination, it is guaranteed that the 
balance performance of the anonymous graph among 
usability, safety and efficiency is improved by a large margin.