Combine masking with [[ZKP/Research/Advanced Federated Learning/Differential Privacy in Federated Learning|differential privacy]] to add [[Introducing Noise|noise]] to outputs, ensuring individual data points remain untraceable even if masked data is compromised [101]. This layered approach strengthens privacy against inference attacks while maintaining compatibility with [[Substrate]]'s privacy-preserving mechanisms. By adding noise to the output of masked computations, we ensure that even if an attacker gains access to the masked data, they cannot [[ZKP/Research/Advanced Federated Learning/Example Application|reconstruct individual records]], providing a dual layer of protection that is particularly valuable in scenarios where [[ZKP/Research/Advanced Federated Learning/Example Application|data sensitivity is high,]] such as in medical research involving patient records processed through Substrate's secure runtime. See also: [[ZKP/Research/Masking/Selective Masking|Selective Masking]]