Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
Published:Dec 17, 2025 11:28
•1 min read
•ArXiv
Analysis
This article, sourced from ArXiv, focuses on evaluating post-training quantization techniques through membership inference, likely assessing the privacy implications of these methods in the context of large language models (LLMs). The title suggests a focus on the trade-off between model compression (quantization) and privacy preservation. The use of membership inference indicates an attempt to determine if a specific data point was used in the model's training, a key privacy concern.
Key Takeaways
- •Focuses on the privacy implications of post-training quantization.
- •Uses membership inference to evaluate privacy.
- •Relevant to the field of LLMs.
Reference
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