预告：基于TEE的DNN分区在设备安全推断中的毒学习
SPOILER: TEE-Shielded DNN Partitioning of On-Device Secure Inference with Poison Learning
AI总结：
SPOILER通过硬件感知NAS解耦TEE子网络与主干网络，实现设备安全推断中的隐私保护与效率平衡。
摘要：
在边缘设备上部署深度神经网络（DNN）会暴露有价值的知识产权给模型窃取攻击。虽然TEE保护的DNN分区（TSDP）通过隔离敏感计算来缓解这一问题，但现有方法无法同时满足隐私和效率。训练前分区范式存在内在隐私泄露，而分区前训练范式由于结构依赖性导致严重延迟。为克服这些限制，我们提出了SPOILER，一种新的搜索前训练框架，通过硬件感知的神经网络架构搜索（NAS）从根本上将TEE子网络与主干网络解耦。SPOILER识别出一个轻量级的TEE架构，严格优化于硬件约束，最大化并行效率。此外，我们引入自毒学习以强制逻辑隔离，使暴露的主干功能在没有TEE组件时变得不一致。在CNN和Transformer上的大量实验表明，SPOILER在安全、延迟和准确性之间实现了最先进的权衡。
英文摘要：
Deploying deep neural networks (DNNs) on edge devices exposes valuable intellectual property to model-stealing attacks. While TEE-shielded DNN partitioning (TSDP) mitigates this by isolating sensitive computations, existing paradigms fail to simultaneously satisfy privacy and efficiency. The training-before-partition paradigm suffers from intrinsic privacy leakage, whereas the partition-before-training paradigm incurs severe latency due to structural dependencies that hinder parallel execution. To overcome these limitations, we propose SPOILER, a novel search-before-training framework that fundamentally decouples the TEE sub-network from the backbone via hardware-aware neural architecture search (NAS). SPOILER identifies a lightweight TEE architecture strictly optimized for hardware constraints, maximizing parallel efficiency. Furthermore, we introduce self-poisoning learning to enforce logical isolation, rendering the exposed backbone functionally incoherent without the TEE component. Extensive experiments on CNNs and Transformers demonstrate that SPOILER achieves state-of-the-art trade-offs between security, latency, and accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
