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Model explanations improve the transparency of black-box machine learning (ML) models and their decisions; however, they can also be exploited to carry out privacy threats such as membership inference attacks (MIA).
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Lemonia Dritsoula, Patrick Loiseau, and John Musacchio · 2017
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Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2017
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Yunhui Long, Vincent Bindschaedler, and Carl A Gunter · 2017
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A unified approach to interpreting model predictions
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Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou · 2019
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Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
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Scott M Lundberg and Su-In Lee · 2017
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Continuous-time stochastic games
Abraham Neyman · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Stolen memories: Leveraging model memorization for calibrated { \{ White-Box } \} membership inference
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
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Signaling games
Joel Sobel · 2020
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Defending model inversion and membership inference attacks via prediction purification
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Label-only membership inference attacks
Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2021
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Practical blind membership inference attack via differential comparisons
Bo Hui, Yuchen Yang, Haolin Yuan, Philippe Burlina, Neil Zhenqiang Gong, and Yinzhi Cao · 2021
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Membership inference attacks and defenses in classification models
Jiacheng Li, Ninghui Li, and Bruno Ribeiro · 2021
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Membership privacy for machine learning models through knowledge transfer
Virat Shejwalkar and Amir Houmansadr · 2021
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On the privacy risks of model explanations
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Resisting membership inference attacks through knowledge distillation
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Vasisht Duddu and Antoine Boutet · 2022
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Mitigating membership inference attacks by { \{ Self-Distillation } \} through a novel ensemble architecture
Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, and Prateek Mittal · 2022
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