标题： 加密大模型推理：等变加密范式 显示英文标题

标题： Encrypted Large Model Inference: The Equivariant Encryption Paradigm

摘要： 大规模深度学习模型，如现代语言模型和扩散架构，已彻底改变了从自然语言处理到计算机视觉的应用。 然而，在分布式或去中心化环境中部署它们会引发重大的隐私问题，因为在推理过程中可能会暴露敏感数据。 传统的技术如安全多方计算、同态加密和差分隐私提供了部分解决方案，但通常会带来显著的计算开销、延迟惩罚或与非线性网络操作的有限兼容性。 在本工作中，我们引入了等变加密（EE），这是一种新范式，旨在实现对加密数据的安全“盲”推理，且性能开销接近于零。 与完全同态方法不同，EE选择性地混淆神经网络层中的关键内部表示，同时保留线性和指定的一组非线性操作的精确功能。 这种有针对性的加密确保了原始输入、中间激活和输出即使在不受信任的基础设施上处理时也保持机密性。 我们详细阐述了EE的理论基础，将其性能和集成复杂度与传统隐私保护技术进行了比较，并展示了其在各种架构中的适用性，从卷积网络到大型语言模型。 此外，我们的工作提供了一个全面的威胁分析，概述了潜在的攻击向量和基准策略，并在去中心化环境中将EE与标准推理流程进行了基准测试。 结果证实，EE保持了高保真度和吞吐量，有效地弥合了强大的数据保密性与现代大规模模型推理严格的效率要求之间的差距。 显示英文摘要

摘要： Large scale deep learning model, such as modern language models and diffusion architectures, have revolutionized applications ranging from natural language processing to computer vision. However, their deployment in distributed or decentralized environments raises significant privacy concerns, as sensitive data may be exposed during inference. Traditional techniques like secure multi-party computation, homomorphic encryption, and differential privacy offer partial remedies but often incur substantial computational overhead, latency penalties, or limited compatibility with non-linear network operations. In this work, we introduce Equivariant Encryption (EE), a novel paradigm designed to enable secure, "blind" inference on encrypted data with near zero performance overhead. Unlike fully homomorphic approaches that encrypt the entire computational graph, EE selectively obfuscates critical internal representations within neural network layers while preserving the exact functionality of both linear and a prescribed set of non-linear operations. This targeted encryption ensures that raw inputs, intermediate activations, and outputs remain confidential, even when processed on untrusted infrastructure. We detail the theoretical foundations of EE, compare its performance and integration complexity against conventional privacy preserving techniques, and demonstrate its applicability across a range of architectures, from convolutional networks to large language models. Furthermore, our work provides a comprehensive threat analysis, outlining potential attack vectors and baseline strategies, and benchmarks EE against standard inference pipelines in decentralized settings. The results confirm that EE maintains high fidelity and throughput, effectively bridging the gap between robust data confidentiality and the stringent efficiency requirements of modern, large scale model inference.