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IEEE/ICACT20260202 Slide.16        [Big Slide]	Oral Presentation
	To evaluate the privacy protection performance of our system, we conducted a membership inference attack. The AUC reflects the classification performance of the trained model. In this context, a higher AUC means that the attacker is better at distinguishing certain data, which indicates that the data is more vulnerable to attacks. The attacker advantage measures how effectively an attacker can perform an attack on the model. Similarly, a higher attacker advantage means that the attacker can more accurately identify samples belonging to the dataset.

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