On the Relationship between Model Quantization and Model Inversion Attacks
Model quantization reduces the numerical precision of neural network weights and activations to lower storage and computational costs. Model inversion attacks recover or reconstruct sensitive training data or inference inputs from model outputs or intermediate features, so quantization may also alter their effectiveness. However, two questions remain unresolved: How does model quantization affect model inversion? How do data characteristics influence this relationship? To address the first, we bound quantization-induced changes in mutual information between inputs and a categorical variable defined by prediction probabilities, distinguishing informational effects from attack optimization ...
| Authors | Rongke Liu, Youwen Zhu |
|---|---|
| Primary category | cs.CR |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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