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  • title: Amplifying Membership Exposure via Data Poisoning
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            Amplifying Membership Exposure via Data Poisoning
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            Amplifying Membership Exposure via Data Poisoning

            Nov 28, 2022

            Speakers

            YC

            Yufei Chen

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            CS

            Chao Shen

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            YS

            Yun Shen

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            About

            As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to test-time accuracy degradation or controlled misprediction. In this paper, we investigate the third type of exploitation of data poisoning - increasing the risks of privacy leakage of benign training samples. To this end, we demonstrate a set of data poisoning attacks to amplify the membership exposure of the targeted…

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            NeurIPS 2022

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