InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy

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. First, we reduce the privacy cost by isolating and clipping only the sensitive information in the model logits (relative to the public logits). Second, we improve text quality by sampling without any privacy cost from a small superset of the top-k private tokens. Empirical evaluations demonstrate a consistent 8× (or more) reduction in computation cost over state-of-the-art baselines to generate long-form private text of the same utility across privacy levels. INVISIBLEINK is able to generate, for the first time, high-quality private long-form text at less than 4-8× times the computation cost of non-private generation, paving the way for its practical use. We open-source a pip-installable Python package (invink) for InvisibleInk at https://github.com/cerai-iitm/invisibleink.

1 Generally, the drop in utility from DP can be offset by increasing data and computation, e.g., by averaging over larger batches to reduce sensitivity; DP model training also admits a similar phenomenon [15]. Thus, we compare methods by the computation cost necessary to attain given privacy and utility levels.

2 We allow some baselines an advantage by (incorrectly) reducing their sensitivity by 2 or √ 2; see §5.