Fuente: Wipo "digitalization" Deploying neural networks that use unique data sources generated by enterprises as training or input data has significant potential to unlock novel insights and domain-specific Al capabilities. However, data owners are often hesitant to share their proprietary and valuable datasets with third parties for training neural networks, even though doing so could provide significant benefits. Similarly, neural network owners are reluctant to expose their confidential weights and biases, which represent critical intellectual property. The proposed solution is based on both data and neural network parameters being transformed into a secure mathematical space, enabling collaborative training and deployment of neural networks without exposing sensitive information. The solution proposed here resolves data and neural network model privacy issues by allowing data owners and neural network owners to work together securely, ensuring that neither party's assets are compromised, while still enabling effective collaboration.