PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption
Structured pruning is essential for making neural network inference feasible under homomorphic encryption (HE), yet its impact on model reliability has remained unexplored. This paper presents a systematic reliability characterization of pruned CKKS-encrypted neural networks and introduces Polynomial-Sensitivity-Aware Pruning (PSAP), a structured pruning method that is inherently reliability-aware. PSAP scores filters jointly by weight magnitude, polynomial activation sensitivity, and rotation cost, which concentrates pruning in fault-tolerant regions. Across two architectures, two datasets, two numerical representations, and five bit-error rates (40 full-model and 108 per-layer experimen...
| Authors | Sahaj Majavdia, Mahdi Taheri |
|---|---|
| Primary category | cs.CR |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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