Batch normalization amplifies memorization of outlier samples in deep neural networks, directly increasing susceptibility to membership inference attacks.
C., Sedghi, H., Lipton, Z
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
LM-DP-SGD estimates layer-specific MIA risks from shadow models and reweights gradients to give stronger protection to vulnerable layers, improving the privacy-utility trade-off over uniform DP-SGD.
citing papers explorer
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Batch Normalization Amplifies Memorization and Privacy Risks
Batch normalization amplifies memorization of outlier samples in deep neural networks, directly increasing susceptibility to membership inference attacks.
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
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Mitigating Membership Inference in Intermediate Representations with Differentially Private Training
LM-DP-SGD estimates layer-specific MIA risks from shadow models and reweights gradients to give stronger protection to vulnerable layers, improving the privacy-utility trade-off over uniform DP-SGD.