This paper presents TT-TFHE, a deep neural network Fully Homomorphic
Encryption (FHE) framework that effectively scales Torus FHE (TFHE) usage to
tabular and image datasets using a recent family of convolutional neural
networks called Truth-Table Neural Networks (TTnet). The proposed framework
provides an easy-to-implement, automated TTnet-based design toolbox with an
underlying (python-based) open-source Concrete implementation (CPU-based and
implementing lookup tables) for inference over encrypted data. Experimental
evaluation shows that TT-TFHE greatly outperforms in terms of time and accuracy
all Homomorphic Encryption (HE) set-ups on three tabular datasets, all other
features being equal. On image datasets such as MNIST and CIFAR-10, we show
that TT-TFHE consistently and largely outperforms other TFHE set-ups and is
competitive against other HE variants such as BFV or CKKS (while maintaining
the same level of 128-bit encryption security guarantees). In addition, our
solutions present a very low memory footprint (down to dozens of MBs for
MNIST), which is in sharp contrast with other HE set-ups that typically require
tens to hundreds of GBs of memory per user (in addition to their communication
overheads). This is the first work presenting a fully practical solution of
private inference (i.e. a few seconds for inference time and a few dozens MBs
of memory) on both tabular datasets and MNIST, that can easily scale to
multiple threads and users on server side.

TT-TFHE 是一个基于神经网络的同态加密框架，可以有效将 TFHE 用于表格和图像数据集中，该框架在加密数据推断的时间和准确性方面表现出色，特别是相对于其他 TFHE 设置和 HE 变体。