Privacy-Preserving Robustness Verification for Neural Networks
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...
| Authors | Nianyun Song, Xiaokun Luan, Yu Guo et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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