HiddenEcho: Mitigating Noise Amplification in Differentially Private LLMs with Hidden-State Correction

The rise of large language models (LLMs) has driven the adoption of Model-as-a-Service (MaaS). However, transmitting raw text to servers raises critical privacy concerns. Existing approaches employ deep neural networks (DNNs) or differential privacy (DP) to perturb inputs. Yet, these approaches suffer notable limitations: DNN-based methods often require task-specific pre-training, and conventional DP techniques, though privacy-preserving, suffer from noise amplification as perturbed inputs propagate through the deep transformer layer, leading to significant degradation in downstream task performance. To alleviate this, we propose HIDDENECHO, an end-to-end framework with client noise correction, where hidden states are sent from the server to the client and refined by a lightweight module using both embeddings and intermediate representations. HIDDENECHO suppresses inter-layer noise amplification without pretraining, effectively preserving task-relevant signals under DP constraints. To further reduce communication, HIDDENECHO incorporates gradient-based hidden layer selection and information bottleneck compression, reducing communication cost while preserving essential task information. Experiments across text classification and generation tasks demonstrate that HIDDENECHO achieves up to 46.89% performance improvement over DP baselines, over 85% communication reduction, and up to 72.52% faster training compared to existing denoising approaches, establishing a new privacy-utility trade-off for privatized LLMs. Codes are available at https://github.com/liwh011/hidden-echo.