arXiv AI July 22, 2026 By 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

efficiency

arXiv Machine Learning

Aug 6

Understanding Fault Tolerance of Adversarially Robust Pruned Models

arXiv:2608. 04173v1 Announce Type: new Abstract: Deep neural networks (DNNs) deployed on resource-constrained neuromorphic hardware face three concurrent challenges: the need for model compression through pruning, vulnerability to adversarial input perturbations, and susceptibility to hardware-induced weight faults such as stuck-at-zero errors.

By Manali Dangarikar, Cory Merkel

efficiency safety

arXiv Machine Learning

Aug 26

Encrypted Neural Networks without Overflows

arXiv:2605.23096v2 Announce Type: replace-cross Abstract: The popular Cheon-Kim-Kim-Song (CKKS) scheme enables efficient private inference in neural networks by evaluating them on encrypted data. Sin...

By Philipp Kern, Lorenzo Rovida, Samuel Teuber, Edoardo Manino, Carsten Sinz, Alberto Leporati

arXiv Machine Learning

Jun 16

FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding

arXiv:2606. 16359v1 Announce Type: cross Abstract: Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead.

By Ran Ran, Zhaoting Gong, Nuo Xu, Yuanchao Xu, Fan Yao, Wujie Wen

benchmarks safety

arXiv Machine Learning

Jul 7

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers

arXiv:2607. 04819v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) enables computation on encrypted data, but practical encrypted Transformer inference is bottlenecked by the sequential composition of many nonlinear blocks.

By Ligong Han, Kai Xu, Hao Wang, Ruijiang Gao, Akash Srivastava

llms

arXiv Machine Learning

Sep 15

EI-DDLGN: Efficient Encrypted Inference with Deep Differentiable Logic Gate Networks under TFHE

arXiv:2609.13636v1 Announce Type: cross Abstract: Privacy-preserving inference via Torus Fully Homomorphic Encryption (TFHE) provides strong protection for sensitive data in outsourced deep learning...

By Mahmoud Y. M. Yassin, Mahmoud AbdelHafeez Sayed, Mostafa Taha

efficiency safety

arXiv AI

Jul 7

From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips

arXiv:2603. 22770v2 Announce Type: replace-cross Abstract: The deployment of deep neural networks (DNNs) in safety-critical edge environments necessitates robustness against hardware-induced bit-flip errors.

By Alan T. L. Bacellar, Sathvik Chemudupati, Shashank Nag, Allison Seigler, Priscila M. V. Lima, Felipe M. G. Fran\c{c}a, Lizy K. John

efficiency benchmarks safety

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Related stories

Understanding Fault Tolerance of Adversarially Robust Pruned Models

Encrypted Neural Networks without Overflows

FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers

EI-DDLGN: Efficient Encrypted Inference with Deep Differentiable Logic Gate Networks under TFHE

From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips