DegreeSpar: Structured Degree Sparsity for Efficient Secure Transformer Inference
Secure Transformer inference protects sensitive inputs but incurs substantial cryptographic overhead, with nonlinear operations such as Softmax and GeLU becoming major bottlenecks. Existing compression methods reduce nonlinear complexity, sequence-dependent computation, or model structure through separately defined compression variables. Under aggressive compression, however, these independently optimized perturbations can accumulate: at a matched compression level, stacking representative approximation, token-pruning, and model-pruning methods reduces ViT-S accuracy from 80.20% to 76.41%. We introduce DegreeSpar, which formulates secure Transformer compression as structured sparsificatio...
| Authors | Yifei Cai, Zhuoran Li, Xiaozuo Shen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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