Batch Normalization Amplifies Memorization and Privacy Risks
Batch Normalization (BN) is widely adopted to enable faster convergence and more stable training of deep neural networks. However, its impact on privacy and memorization has remained largely unexplored. In this work, we investigate the effect of BN layers on the memorization of atypical or outlier samples and its implications for privacy leakage. We conduct an extensive empirical study using three complementary approaches: (i) unintended memorization of out-of-distribution samples, (ii) per-sample influence, and (iii) susceptibility to membership inference attacks (MIA). Across multiple datasets and architectures, we consistently observe that BN substantially increases the memorization of...
| Authors | Ngoc Phu Doan, Chongyan Gu, Ihsen Alouani |
|---|---|
| Primary category | cs.LG |
| Published | 23 May 2026 |
| Last updated at source | 17 September 2026 |
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