  In the domain of Privacy-Preserving Machine Learning (PPML), Fully
Homomorphic Encryption (FHE) is often used for encrypted computation to allow
secure and privacy-preserving outsourcing of machine learning modeling. While
FHE enables encrypted arithmetic operations, execution of programmatic logic
such as control structures or conditional programming have remained a
challenge. As a result, progress in encrypted training of PPML with FHE has
been relatively stagnant compared to encrypted inference owing to the
considerably higher logical complexity required in training. In addition, prior
works that have demonstrated encrypted training use Interactive Rounds of
Decryption and Evaluation (IRDE), where certain operations are decrypted and
evaluated in plaintext using interactive rounds between the untrusted computing
party (server) and the trusted private-key owner (client). In decision tree
training for example, the current state-of-the-art requires d-rounds of IRDE
for tree-depth of d. To address this issue in PPML and FHE, we introduce the
Blind Evaluation Framework (BEF), a cryptographically secure programming
framework that enables blind, but correct, execution of programming logic
without IRDE. This is achieved by deconstructing programming logic into binary
circuits and binary arithmetic to find alternative representations of logical
statements, and adopting them to FHE for secure logical programming. To the
best of our knowledge, this is the first framework to enable both training and
inference of PPML models with FHE without decryption rounds. By advancing the
state-of-the-art in IRDE efficiency by eliminating IRDE entirely, BEF enables
adoption of FHE in use-cases where large amounts of computing services are
available without the ability to have trusted clients available to perform
decryption rounds.
