arXiv Machine Learning
Encrypted Neural Networks without Overflows
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jun 9
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).
arXiv Machine Learning
Aug 19
Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
PANDA is a scalable system that uses zero‑knowledge proofs to certify the robustness and fairness of neural networks without revealing their private parameters. Built on the CROWN robustness framework, PANDA introduces a novel algorithm for proving linear relaxation bounds on non‑linear activation layers, producing lightweight proofs. The system can generate proofs for networks with over 2.9 million parameters in just five minutes and verify them in ten seconds, scaling polynomially with network size and enabling verification of models four orders of magnitude larger than prior ZKP‑based approaches.
arXiv AI
Jun 11
\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party
arXiv:2505. 17623v2 Announce Type: replace-cross Abstract: Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to external participants due to blockchain limitations.
arXiv AI
Jul 22
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.
arXiv AI
Jul 7
Privacy-Preserving Robustness Verification for Neural Networks
arXiv:2607. 05251v1 Announce Type: cross Abstract: Neural network verification and data privacy are inherently in tension: verification demands full access to model parameters and input data, yet both are increasingly restricted by privacy regulations and intellectual property constraints.
arXiv Machine Learning
Jun 9
Cryptographic Backdoor for Neural Networks: Boon and Bane
arXiv:2509. 20714v2 Announce Type: replace-cross Abstract: In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the attacks as well as in presenting the defenses as well.