In a healthcare scenario, patient records could be masked using PHE for sensitive fields like diagnoses, enabling AI model training on encrypted data processed through [[Substrate]]'s secure runtime. [[ZKP/Research/Advanced Federated Learning/Differential Privacy in Federated Learning|Differential privacy]] would [[ZKP/Research/Noise/Scalable Noise Addition|protect]] model outputs, ensuring compliance with regulations like GDPR while maintaining auditability through Substrate's immutable storage, fostering trust in decentralized health research. _For instance, a researcher developing a predictive model for disease outbreaks could train on masked patient data, with the [[ZKP/Research/Masking/Homomorphic Masking|masking]] ensuring that individual patient identities remain hidden, and differential privacy protecting the aggregated results shared with other researchers through Substrate's networking layer, thus enabling secure, collaborative health advancements._ See also: [[Introducing Noise]]