Industrial Technology Research Institute

International Business Machines Corporation

Ya-Ling Chen

Ding-Jun Yin

Kuo-Yang Hung

Ci-Wei Lan

Jen-Hao Hsiao

Tyrone Wilberforce Grandison

Li-Feng Tseng

Yi-Hui Chen

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.

Method and device for risk evaluation

An anonymous dataset generation method comprises following steps. A critical attribute set and a quasi-identifier (QID) set are acquired, and one of the critical attribute and the quasi-identifier is set as an anchor attribute. An attribute sequence and an equivalence table are generated according to the quasi-identifier set and the critical attribute set. A data cluster and a cluster table are generated according to the equivalence table. The content of the cluster table is generalized to generate and output an anonymous dataset corresponding to an original dataset. A risk evaluation method for an anonymous dataset calculates data weight to extract distinctive data and to attacking defects of the anonymous dataset according to the distinctive data, thereby enhancing a risk evaluation efficiency of the anonymous dataset.

Generation method and device for generating anonymous dataset, and method and device for risk evaluation

A method and a system for preserving privacy of a dataset are provided. In the method, a k-anonymity value with respect to a sensitive data field is determined according to at least one first quasi-identifier. Data entries in each group have the same value in the one or more fields of the first quasi-identifier and data entries in different groups have different values in the one or more fields of the first quasi-identifier. A first group and a second group among the plurality of groups are determined according to the reference number Kr, where the first group and the second group are merged into a merging group. The number of data entries in the merging group is not less than a reference number Kr. One or more fields of at least one first quasi-identifier is masked for the merging group.

Method and system for preserving privacy of a dataset

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Hsinchu, Taiwan

Ya-Ling Chen