On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
For Transformer models, cryptographically secure inference ensures that the client learns only the final output, while the server learns nothing about the client's input. However, securely computing nonlinear layers remains a major efficiency bottleneck due to the substantial communication rounds and data transmission required. To address this issue, prior works reveal intermediate activations to the client, allowing nonlinear operations to be computed in plaintext. Although this approach significantly improves efficiency, exposing activations enables adversaries to extract model weights. To mitigate this risk, existing works employ a shuffling defense that reveals only randomly permuted ...
| Authors | Zhengyi Li, Yakai Wang, Kang Yang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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