TFMD: General and Fast Secure Neural Network Inference Framework With Threshold FHE

TFMD is a framework that allows multiple parties to run neural networks (machine learning models) on sensitive data while keeping that data private through threshold FHE (fully homomorphic encryption, a cryptographic technique that lets computation happen on encrypted data without decrypting it). Unlike previous systems that only work with a fixed number of participants and fail if too many are compromised, TFMD handles any number of participants, allows up to all but one to be corrupted, and uses special techniques to make the calculations faster, particularly for the ReLU function (a common operation in neural networks).