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This research identifies that the choice of 4-bit quantization method significantly impacts privacy leakage in fine-tuned small language models, with calibration-based techniques like AWQ and GPTQ outperforming non-calibrated formats. By linking data reconstruction risks to rounding errors in rare-token prediction channels, the study establishes a critical deployment-time trade-off between model compression efficiency and the protection of sensitive private records.
Not All 4-bit Quantizers Are Equal: Deployment-Time Mitigation of PII Leakage in Fine-Tuned Small Language Models
arXiv:2609.25014v1 Announce Type: cross Abstract: Organizations fine-tune small language models on private data and then compress them to 4 bits for resource-efficient deployment. We show that the compression method also affects privacy. What separates the methods is not the bit width but whether they tune their rounding on a small sample of text, the calibration corpus. On our primary model, when each planted record's own opening text is used as the prompt, the two calibration-based methods we test, Activation-aware Weight Quantization (AWQ) and Gradient-based Post-Training Quantization (GPTQ), each reproduce none of the planted records, while the calibration-corpus-free GGUF Q4_K_M format reproduces 5.3% of them. Tracked across five open models with 0.5-7 billion parameters, AWQ leaks least at every size and in both families, with little accuracy loss at 3-7 billion. Controlled experiments associate the difference with calibration-induced rounding error in channels involved in rare-token prediction. Choosing the 4-bit method is therefore a deployment-time privacy decision, not only a question of speed and quality.
Abstract:Organizations fine-tune small language models on private data and then compress them to 4 bits for resource-efficient deployment. We show that the compression method also affects privacy. What separates the methods is not the bit width but whether they tune their rounding on a small sample of text, the calibration corpus. On our primary model, when each planted record's own opening text is used as the prompt, the two calibration-based methods we test, Activation-aware Weight Quantization (AWQ) and Gradient-based Post-Training Quantization (GPTQ), each reproduce none of the planted records, while the calibration-corpus-free GGUF Q4_K_M format reproduces 5.3% of them. Tracked across five open models with 0.5-7 billion parameters, AWQ leaks least at every size and in both families, with little accuracy loss at 3-7 billion. Controlled experiments associate the difference with calibration-induced rounding error in channels involved in rare-token prediction. Choosing the 4-bit method is therefore a deployment-time privacy decision, not only a question of speed and quality.
10 pages and 5 tables. The source code and per-seed experimental results are publicly available at this https URL. Accepted for publication at SBSeg 2026
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
I.2.7; K.4.1; I.2.6
arXiv:2609.25014 [cs.CR]
(or arXiv:2609.25014v1 [cs.CR] for this version)
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