Researchers have shown that most practical quantization settings—reducing the precision of network weights and activations to 4–8 bits—do not meaningfully lower the risk of membership inference attacks (MIAs). The study evaluated eight leading MIA techniques on six different quantization methods, covering both black‑box and white‑box settings and both population‑level and per‑example attacks. Results on a ResNet‑18 model trained on CIFAR‑100 revealed that attack accuracies for 4‑bit models were within a few percentage points of the full‑precision baseline for most attacks. Only extreme compression to 2‑bit precision substantially degraded attack success, lowering some black‑box attacks to near random guessing while preserving about 50 % accuracy for certain quantizers such as HAWQ and BSQ. However, this privacy benefit comes at a steep cost: classification accuracy can drop by as much as 95 % when the model is limited to 2 bits. Existing defenses designed for full‑precision networks, such as dropout, adversarial regularization, and differential privacy, provide only limited additional protection for quantized models and often worsen the already‑severe utility loss. Furthermore, simply tightening quantization does not always improve privacy; in some cases it can even amplify leakage due to over‑fitting or shifts in output distributions. The authors conclude that moderate quantization (4–8 bits) does not add privacy, and that aggressive low‑bit quantization provides only marginal privacy gains at the expense of model usefulness.