TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation

• URL: http://arxiv.org/abs/2503.12217v1
• Date: Sat, 15 Mar 2025 17:57:44 GMT
• Title: TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation
• Authors: Mayank Kumar, Jiaqi Xue, Mengxin Zheng, Qian Lou,
• Abstract summary: Homomorphic Encryption over the torus (TFHE) enables encrypted computation on data without decryption.<n>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.<n>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.
• Score: 10.597643264309415
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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