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. 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
Increasing regulatory scrutiny on data privacy and intellectual property, coupled with the critical need for reliable AI systems, makes privacy-preserving verification methods highly relevant.
This work addresses a fundamental tension between AI verification and privacy, enabling the deployment of robust AI in sensitive domains where it was previously impractical.
Robustness verification for neural networks can now be conducted without fully exposing model parameters or input data, expanding AI applications into privacy-sensitive sectors.
- · AI developers in privacy-sensitive sectors
- · Healthcare and financial industries
- · Secure computing solution providers
- · Organisations unable to adapt to privacy-preserving verification standards
Increased adoption of AI in highly regulated and privacy-conscious environments.
Development of industry standards and certifications for privacy-preserving AI robustness.
New competitive landscapes emerge for AI solutions that can guarantee both robustness and privacy by design.
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Read at arXiv cs.AI