1. An anonymous dataset generation method, comprising: acquiring, in an anonymous dataset generation device having a processor and a memory, a critical attribute set and a quasi-identifier set, wherein the critical attribute set comprises at least one critical attribute, the quasi-identifier set comprises a plurality of quasi-identifiers, and one of the at least one critical attribute or one of the quasi-identifiers is set as an anchor attribute; generating, by the processor, an equivalence table according to the quasi-identifier set, the critical attribute set and an original dataset, wherein the equivalence table comprises a plurality of equivalence classes, each of the equivalence classes comprises at least one equivalence data, and each equivalence data comprises a plurality of original values corresponding to the quasi-identifiers respectively; generating, by the processor, a plurality of data clusters of a cluster table sequentially according to the equivalence table, wherein each of the data clusters comprises at least one of the equivalence classes; and generalizing, by the processor, content of the cluster table to generate and output an anonymous dataset corresponding to the original dataset, wherein the original values corresponding to the anchor attribute are maintained originally in the anonymous dataset, wherein each of the data clusters comprises a cluster code and at least one of the equivalence classes, and the step of generalizing the content of the cluster table to generate and output the anonymous dataset corresponding to the original dataset, comprises: reading the equivalence classes of the data clusters sequentially; when the read equivalence class is the first in the cluster table, setting the read equivalence class as a temporary generalized model, wherein the temporary generalized model comprises a plurality of first attribute values corresponding to the quasi-identifiers respectively, and the original values of the first attribute values are the original values of first one of the data clusters; when the read equivalence class is not the first in the cluster table, and when the read equivalence class is the same as the cluster code corresponding to the temporary generalized model, performing steps of: searching for a smallest generalized model between the read equivalence class and the temporary generalized model; and storing the smallest generalized model as a updated temporary generalized model; and when the read equivalence class is not the first in the cluster table, and when the read equivalence class is different from the cluster code corresponding to the temporary generalized model, performing steps of: storing the temporary generalized model in the anonymous dataset; and setting the read equivalence class as the temporary generalized model.