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New pruning method enhances reliability of encrypted neural networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Polynomial-Sensitivity-Aware Pruning (PSAP) to improve the reliability of neural networks when encrypted using homomorphic encryption (HE). PSAP considers weight magnitude, polynomial activation sensitivity, and rotation cost to prune filters, concentrating pruning in fault-tolerant regions. This approach significantly reduces the number of layers vulnerable to catastrophic accuracy drops compared to standard magnitude pruning, even under bit-flip injection, and provides a conservative proxy for reliability testing. AI

IMPACT Enhances the security and efficiency of deploying neural networks in sensitive applications.

RANK_REASON This is a research paper detailing a new method for neural network encryption. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New pruning method enhances reliability of encrypted neural networks

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This is a research paper detailing a new method for neural network encryption. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sahaj Majavdia, Mahdi Taheri ·

    PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption

    arXiv:2607.18342v1 Announce Type: cross Abstract: 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 characteriza…

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