Method and device for risk evaluation

摘要

A risk evaluation method and a risk evaluation device for evaluating an anonymous dataset generated according to an original dataset are provided. The risk evaluation method comprises the following steps. Acquiring a plurality of appearing times respectively corresponding to a plurality of original values of the original dataset. Generating a partition set and a weight table according to a sample parameter, an anonymous parameter and the appearing times. Dividing the original dataset into a plurality of data partitions according to the partition set, and generating a penetration dataset according to the weight table and the data partitions, wherein the penetration dataset comprises a plurality of sample data. Comparing each sample data with a plurality of anonymous data of the anonymous dataset to obtain a plurality of matching quantities respectively corresponding to the sample data. And calculating and outputting a risk evaluation result according to the matching quantities.

主权项

1. A risk evaluation method, applied to a processor for evaluating an anonymous dataset generated according to an original dataset, and comprising:
acquiring a plurality of numbers of times of appearing respectively corresponding to a plurality of original values of the original dataset; generating, by the processor, a partition set and a weight table according to a sample parameter, an anonymous parameter, the numbers of times of appearing and a weight parameter, wherein a product of the weight parameter and the anonymous parameter is bigger than or equal to the largest one of the numbers of times of appearing; dividing the original dataset into a plurality of data partitions according to the partition set, and generating a penetration dataset according to the weight table and the data partitions, wherein the penetration dataset comprises a plurality of sample data; comparing each sample data with a plurality of anonymous data of the anonymous dataset to obtain a plurality of matching quantities respectively corresponding to the sample data; calculating and outputting a risk evaluation result according to the matching quantities; and providing the risk evaluation result to an administrator through a graphical user interface (GUI), such that the administrator is configured to determine whether the anonymous dataset is capable of being opened for users under a current risk level.