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[2602.17223] Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs
Summary
The paper presents new privacy-preserving protocols for verifiable inference of large language models (LLMs), addressing the challenges of third-party hosting and computation integrity.
Why It Matters
As reliance on third-party services for LLMs increases, ensuring the integrity and privacy of computations becomes critical. This research offers cost-effective solutions that enhance trust in AI systems, which is vital for developers and businesses using LLMs.
Key Takeaways
- Proposes two new protocols for verified inference of LLMs.
- Enhances privacy while ensuring computation integrity at low cost.
- Improves verification runtime compared to traditional cryptographic methods.
Computer Science > Cryptography and Security arXiv:2602.17223 (cs) [Submitted on 19 Feb 2026] Title:Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs Authors:Arka Pal, Louai Zahran, William Gvozdjak, Akilesh Potti, Micah Goldblum View a PDF of the paper titled Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs, by Arka Pal and 4 other authors View PDF HTML (experimental) Abstract:As large language models (LLMs) continue to grow in size, fewer users are able to host and run models locally. This has led to increased use of third-party hosting services. However, in this setting, there is a lack of guarantees on the computation performed by the inference provider. For example, a dishonest provider may replace an expensive large model with a cheaper-to-run weaker model and return the results from the weaker model to the user. Existing tools to verify inference typically rely on methods from cryptography such as zero-knowledge proofs (ZKPs), but these add significant computational overhead, and remain infeasible for use for large models. In this work, we develop a new insight -- that given a method for performing private LLM inference, one can obtain forms of verified inference at marginal extra cost. Specifically, we propose two new protocols which leverage privacy-preserving LLM inference in order to provide guarantees over the inference that was carried out. Our approaches are cheap, requiring the addition of a few extra tokens of co...