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Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model.
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X. He and Y. Zhang, “Quantifying and mitigating privacy risks of contrastive learning,” in CCS , 2021
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S. Rezaei and X. Liu, “On the difficulty of membership inference attacks,” in CVPR , 2021
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L. Song and P. Mittal, “Systematic evaluation of privacy risks of machine learning models,” in USENIX Security , 2021
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D. Chen, N. Yu, Y. Zhang, and M. Fritz, “Gan-leaks: A taxonomy of membership inference attacks against generative models,” in CCS , 2020
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M. A. Shah, J. Szurley, M. Mueller, A. Mouchtaris, and J. Droppo, “Evaluating the vulnerability of end-to-end automatic speech recognition models to membership inference attacks,” in Proc. Interspeech , 2021
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V. Shejwalkar and A. Houmansadr, “Membership privacy for machine learning models through knowledge transfer,” in AAAI , 2021
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Z. Zhang, M. Chen, M. Backes, Y. Shen, and Y. Zhang, “Inference attacks against graph neural networks,” in USENIX Security , 2022
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N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer, “Membership inference attacks from first principles,” in SP , 2022
2022
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