Homomorphic Encryption enables computations on encrypted data [[ZKP/Research/Masking/Homomorphic Masking|without decrypting it]], preserving privacy throughout the process [10]. The encrypted result, when decrypted, matches the outcome of the same computation on plaintext data. This is invaluable for [[ZKP/Research/Advanced Federated Learning/Example Application|applications requiring confidentiality]], such as secure AI training, where data exposure must be minimized. However, its computational complexity—often orders of magnitude slower than plaintext operations—poses practical challenges. In the [[ZKP/Introduction/ZKP Ecosystem/Architecture|ZKP ecosystem,]] Homomorphic Encryption complements ZKPs by offering an alternative for privacy-preserving computation, particularly in scenarios like federated learning. Nodes could process encrypted datasets for AI models, ensuring data remains confidential even during computation. While ZKPs handle most privacy needs currently, Homomorphic Encryption is a [[ZKP/Introduction/About|research focus]] for future enhancements, balancing its privacy benefits against performance trade-offs. ![[Diagram9 1.png]] See also: [[ZKP/ZKP Base Layer/Core Concepts/zk-STARKs/zk-STARKs|zk-STARKs]]