SOURCE-LINKED INTELLIGENCE
On the Impact of Anonymization on the Performance of Large Language Models
As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has become a critical practice. However, the impact of this anonymization on model utility is not well understood. This paper presents a systematic empirical study of the trade-off between privacy and performance. We evaluate five prominent language models across eleven diverse benchmarks, comparing their performance on original versus pseudonymized inputs. Our results reveal that while anonymization generally degrades performance, the effect is highly
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T10:12:36.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.