Fully Encrypted Machine Learning Training Using Function-Hiding Functional Encryption.

This work presents a practical protocol for fully encrypted machine learning using one-time (OT) function-hiding (FH) multi-input functional encryption (MIFE). Prior works employing functional encryption for privacy-preserving machine learning typically protect intermediate results using differential privacy (DP) techniques, which requires a large privacy budget and a predefined number of model training iterations. Our protocol overcomes these limitations by ensuring that all intermediate results remain encrypted throughout the training process, revealing only the final model, which can be further protected using DP if desired. This allows for more flexibility in the number of training iterations and improves the utility and robustness of the trained model, especially in the high privacy regime. We demonstrate the practicality of our protocol by training logistic regression models on real-world datasets. To do this, we present the first OT FH-MIFE scheme that ensures correctness over ℤ_p and supports a flexible number of decryption keys. This is also the first FH-(MI)FE schemes for affine functions based on lattices. Furthermore, we adapt this construction to efficiently handle affine average functions for horizontally partitioned data.