arXiv Machine Learning October 3, 2026 By Rongke Liu, Youwen Zhu

On the Relationship between Model Quantization and Model Inversion Attacks

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The paper investigates how reducing numerical precision through model quantization impacts the vulnerability of neural networks to model inversion attacks. It provides theoretical bounds on mutual information changes and identifies data-dependent effects, especially at 4‑bit precision. Based on these findings, the authors propose a privacy‑aware post‑training quantization strategy that allocates bits adaptively, calibrates activation ranges, and jointly optimizes weight and activation scaling to improve inversion resistance while preserving model utility.

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