Abstract: A trained machine learning model and a training dataset used to train the trained machine learning model can be received. Based on the training dataset, unsupervised adversarial examples can be generated. Robustness of the trained machine learning model can be determined using the generated unsupervised adversarial examples. The training dataset can be augmented with the generated unsupervised adversarial examples. The trained machine learning model can be retrained using the augmented training dataset.

Abstract: A privacy data integration method and a server are provided. The privacy data integration method includes the following steps. A first processing device and a second processing device respectively obtain a first generative model and a second generative model according to a first privacy data and a second privacy data. A server generates a first generative data and a second generative data via the first generative model and the second generative model respectively. The server integrates the first generative data and the second generative data to obtain a synthetic data.

Abstract: A privacy data integration method and a server are provided. The privacy data integration method includes the following steps. A first processing device and a second processing device respectively obtain a first generative model and a second generative model according to a first privacy data and a second privacy data. A server generates a first generative data and a second generative data via the first generative model and the second generative model respectively. The server integrates the first generative data and the second generative data to obtain a synthetic data.

Abstract: A de-identification data generation apparatus, method, and non-transitory computer readable storage medium thereof are provided. The apparatus is stored with a plurality of original records, wherein each of the records has a plurality of original values corresponding to a plurality of attributes one-to-one. The apparatus decides a plurality of attribute relations (including a user-defined attribute relation) according to the original values, wherein each attribute relation is defined by two attributes. The apparatus decides a plurality of relation groups according to the attribute relations. For each relation group, the apparatus calculates a statistical distribution of the original values corresponding to the attributes in the relation group, aggregates the statistical distribution into a plurality of sub-statistical distributions, and adds noise to each sub-statistical distribution individually.

Abstract: A node disposition apparatus, a node disposition method and a computer readable medium thereof are provided. The node disposition apparatus may determine where a base station (BS) and a relay station (RS) should be disposed according to a connected mobile station amount of each node disposition candidate position in a non-transparent wireless network. Thereby, the BS and the RS may be disposed effectively to overcome the defects of the conventional BS and RS disposition way.

Abstract: A node disposition apparatus, a node disposition method and a computer readable medium thereof are provided. The node disposition apparatus may determine where a base station (BS) and a relay station (RS) should be disposed according to a connected mobile station amount of each node disposition candidate position in a non-transparent wireless network. Thereby, the BS and the RS may be disposed effectively to overcome the defects of the conventional BS and RS disposition way.