LoREnc用低秩加密保护大模型和适配器，无需重训练
LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapters
- 通过谱截断与补偿，无需重训练即可加密模型权重
- 对未经授权用户生成结构坍缩输出，授权用户性能不变
- 计算开销低于1%，适合部署在设备端
大模型和低秩适配器使本地生成式AI更高效，但也带来知识产权泄露和模型恢复攻击风险。现有防御方法常需重新训练或原始数据集，不实用。我们提出LoREnc，一种无需训练的框架，通过谱截断与补偿来保护大模型和适配器。该方法抑制大模型权重中的主要低秩成分，为授权适配器补偿丢失信息，并进一步采用正交重参数化以模糊受保护适配器的结构指纹。未经授权用户生成的输出结构坍缩，而授权用户可恢复完整性能。实验表明，LoREnc在不到1%的计算开销下，对模型恢复攻击提供强防护。
原文摘要 · Abstract (English)
Foundation models and low-rank adapters enable efficient on-device generative AI but raise risks such as intellectual property leakage and model recovery attacks. Existing defenses are often impractical because they require retraining or access to the original dataset. We propose LoREnc, a training-free framework that secures both FMs and adapters via spectral truncation and compensation. LoREnc suppresses dominant low-rank components of FM weights, compensates for the missing information in authorized adapters, and further applies orthogonal reparameterization to obscure structural fingerprints of the protected adapter. Unauthorized users produce structurally collapsed outputs, while authorized users recover exact performance. Experiments demonstrate that LoREnc provides strong protection against model recovery with under 1% computational overhead.
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