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A White-Box Generator Membership Inference Attack Against Generative Models | IEEE Conference Publication | IEEE Xplore

A White-Box Generator Membership Inference Attack Against Generative Models


Abstract:

Using generative models to generate unlimited number of synthetic samples is a popular replacement of database sharing. When these models are built using sensitive data, ...Show More

Abstract:

Using generative models to generate unlimited number of synthetic samples is a popular replacement of database sharing. When these models are built using sensitive data, the developers should ensure that the training dataset is appropriately protected. Hence, quantifying the privacy risk of these models is important. In this paper, we focus on evaluating privacy risk of publishing generator in generative adversarial network (GAN) models. Specially, we conduct a white box membership inference attack against GAN models. The proposed attack is applicable to various kinds of GANs. We evaluate our attack accuracy with respect to various model types and training configurations. The results demonstrate superior performance of the proposed attack compared to previous attacks in white box generator access.
Date of Conference: 01-02 September 2021
Date Added to IEEE Xplore: 01 March 2022
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Conference Location: Isfahan, Iran, Islamic Republic of

References

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