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Encryption-Friendly LLM Architecture

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arxiv 2410.02486 v2 pith:YVPBW7ZB submitted 2024-10-03 cs.CR cs.LG

Donghwan Rho , Taeseong Kim , Minje Park , Jung Woo Kim , Hyunsik Chae , Ernest K. Ryu , Jung Hee Cheon This is my paper
classification cs.CRcs.LG
keywords fine-tuningarchitecturecomputationalinferencellmsmodelspersonalizedprivacy-preserving
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states and provides a potential solution for privacy-preserving machine learning (PPML). However, the computational intensity of transformers poses challenges for applying HE to LLMs. In this work, we propose a modified HE-friendly transformer architecture with an emphasis on inference following personalized (private) fine-tuning. Utilizing LoRA fine-tuning and Gaussian kernels, we achieve significant computational speedups -- 6.94x for fine-tuning and 2.3x for inference -- while maintaining performance comparable to plaintext models. Our findings provide a viable proof of concept for offering privacy-preserving LLM services in areas where data protection is crucial. Our code is available on GitHub.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ENSI: Efficient Non-Interactive Secure Inference for Large Language Models

    cs.CR 2025-09 conditional novelty 6.0 of 10

    ENSI performs secure, non-interactive LLM inference by co-designing CKKS homomorphic encryption with BitNet's ternary weights, achieving up to 8x faster matrix multiplication and 2.6x faster softmax than prior work.

  2. Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques

    cs.LG 2025-06 unverdicted novelty 4.0 of 10

    A survey of LLM routing and hierarchical inference techniques that proposes an unvalidated unified evaluation metric called the Inference Efficiency Score.

  3. SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation

    cs.CR 2025-06 conditional novelty 3.0 of 10

    A systematization-of-knowledge survey that categorizes LLM privacy risks into training data, prompts, outputs, and agents, and reviews limitations of current mitigations.

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