TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation
This work addresses the usability gap in TFHE code generation for privacy-preserving applications, establishing a first benchmark but is incremental in applying existing LLM techniques to a new domain.
The paper tackled the challenge of generating practical code for Fully Homomorphic Encryption (TFHE) by evaluating LLM-based methods, finding that agentic optimizations like RAG and few-shot prompting reduced errors and improved code fidelity compared to off-the-shelf models.
Fully Homomorphic Encryption over the torus (TFHE) enables computation on encrypted data without decryption, making it a cornerstone of secure and confidential computing. Despite its potential in privacy preserving machine learning, secure multi party computation, private blockchain transactions, and secure medical diagnostics, its adoption remains limited due to cryptographic complexity and usability challenges. While various TFHE libraries and compilers exist, practical code generation remains a hurdle. We propose a compiler integrated framework to evaluate LLM inference and agentic optimization for TFHE code generation, focusing on logic gates and ReLU activation. Our methodology assesses error rates, compilability, and structural similarity across open and closedsource LLMs. Results highlight significant limitations in off-the-shelf models, while agentic optimizations such as retrieval augmented generation (RAG) and few-shot prompting reduce errors and enhance code fidelity. This work establishes the first benchmark for TFHE code generation, demonstrating how LLMs, when augmented with domain-specific feedback, can bridge the expertise gap in FHE code generation.