We aim to develop efficient, application-specific masking techniques that integrate with [[ZKP/Research/Noise/Integration with Homomorphic Encryption|homomorphic encryption]] and [[ZKP/Research/Advanced Federated Learning/Differential Privacy in Federated Learning|differential privacy]] to provide robust privacy guarantees while minimizing computational overhead, enhancing the [[ZKP/Introduction/ZKP Ecosystem/Architecture|ZKP Ecosystem's]] ability to protect sensitive data during AI computations within [[Substrate]]'s runtime environment. Our masking research leverages Substrate's modular pallet architecture and [[Proof Pods in the Data Marketplace|off-chain workers]] to enable privacy-preserving computations that integrate seamlessly with the ZKP Blockchain's BABE+GRANDPA consensus mechanism and native verification infrastructure. In this section we will showcase future research initiatives of Masking. See also: [[ZKP/Research/Masking/Homomorphic Masking|Homomorphic Masking]]