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.