Functional Encryption

Functional encryption (FE) allows computations on encrypted data without revealing the underlying data itself, addressing privacy concerns in various applications. Current research focuses on improving FE's efficiency and scalability for machine learning tasks, particularly in federated learning settings, exploring techniques like weight clustering and specialized encryption for specific model architectures (e.g., generalized linear models). This active research area holds significant promise for enhancing the privacy and security of data-intensive applications, including large language models and collaborative machine learning frameworks.