Garbled circuits serve as a form of privacy protection in machine learning, closely related to encrypted methods like homomorphic encryption. This technique allows computations on encrypted data, enabling accurate results without exposing sensitive information. However, the computational demands of homomorphic encryption present challenges, prompting exploration into alternatives like secure multi-party computation for more efficient processing.
