---
title: "SecureCROWN: First Framework for Privacy-Preserving Neural Network Robustness Verification"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.05251v1"
permalink: "https://feed.astralor.com/items/itm_98183cad7c73680104f70d53"
published: "2026-07-06T15:59:17.000Z"
score: 79
channel: "tech"
tags: ["SecureCROWN","Secure Two-Party Computation","Linear Bound Propagation","Privacy Protection","Robustness Verification"]
---

# SecureCROWN: First Framework for Privacy-Preserving Neural Network Robustness Verification

## AI commentary

> What excites me most about this paper is how it uses a cross-disciplinary approach combining cryptography and formal verification to solve the practical dilemma of 'wanting to verify but afraid to expose'. For medical AI or financial risk control teams, SecureCROWN means they can complete compliance audits without handing over model weights or sensitive data — a key enabler for regulatory deployment. Also, the trick of converting conditional branches into continuous arithmetic operations is quite elegant and worth watching.

## At a glance

Neural network verification and data privacy are inherently in tension. SecureCROWN, built upon secure two-party computation (2PC), enables a model owner and a data owner to jointly compute certified robustness bounds, revealing only the final result while protecting both parties' private data. It eliminates branching in Linear Bound Propagation by formulating conditional logic as continuous arithmetic operations, and introduces a Newton-Raphson refinement method for numerical stability. Experiments show SecureCROWN strictly matches plaintext verification results, completing in 0.1–200s across varied model sizes, demonstrating the feasibility of privacy-preserving neural network verification.

## Key points

- First privacy-preserving neural network robustness verification framework SecureCROWN, addressing the tension between verification needs and data privacy.
- Built on secure two-party computation (2PC), revealing only the final verification result while protecting both model parameters and input data.
- Eliminates branching by formulating conditional logic as continuous arithmetic operations, avoiding incompatibility with standard secure computation protocols.
- Introduces Newton-Raphson refinement to improve numerical stability; experimental results strictly match plaintext verification.

## Full text

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. This tension has left robustness verification impractical in privacy-sensitive domains. In this work, we address this gap with SecureCROWN, the first framework for privacy-preserving neural network robustness verification. Built upon secure two-party computation (2PC), our framework enables a model owner and a data owner to jointly compute certified robustness bounds -- revealing only the final result while provably protecting both parties' private data under the semi-honest security model. A key challenge is securely computing the conditional operations in Linear Bound Propagation, where the data-dependent branching is incompatible with standard secure computation protocols. We eliminate branching by formulating conditional logic as continuous arithmetic operations. Additionally, we introduce a Newton--Raphson refinement method to improve numerical stability. Extensive analysis and experiments show that SecureCROWN strictly matches plaintext verification results, while completing in 0.1--200s across varied model sizes and communication settings (LAN/WAN), demonstrating the feasibility of privacy-preserving neural network verification.

---

Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_98183cad7c73680104f70d53