AI RESEARCH
InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy
arXiv CS.LG
•
ArXi:2507.02974v3 Announce Type: replace As major progress in LLM-based long-form text generation enables paradigms such as retrieval-augmented generation (RAG) and inference-time scaling, safely incorporating private information into the generation remains a critical open question. We present InvisibleInk, a highly scalable long-form text generation framework satisfying rigorous differential privacy guarantees with respect to the sensitive reference texts. It interprets sampling from the LLM's next-token-distribution as the exponential mechanism over the LLM logits with two innovations.