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.

翻译：本文提出TT-TFHE，一种深度神经网络全同态加密（FHE）框架，该框架利用名为真值表神经网络（TTnet）的新型卷积神经网络家族，有效将环形TFHE（Torus FHE）的应用扩展至表格和图像数据集。所提框架提供基于TTnet的易实现、自动化设计工具包，并依托底层（基于Python的）开源Concrete实现（基于CPU且实现查找表），支持加密数据上的推理。实验评估表明，在三个表格数据集上，TT-TFHE在所有其他特征相同条件下，在时间和准确率方面均大幅优于所有同态加密（HE）方案。对于MNIST和CIFAR-10等图像数据集，TT-TFHE始终显著优于其他TFHE方案，并与BFV或CKKS等其他HE变体具有竞争力（同时保持相同的128位加密安全保证）。此外，我们的解决方案具有极低的内存占用（例如MNIST仅需数十MB），这与其他HE方案通常每用户需要数十至数百GB内存（并附加通信开销）形成鲜明对比。这是首个在表格数据集和MNIST上提出完全实用化私有推理解决方案（即推理时间仅需数秒、内存仅需数十MB）的工作，且该方案可轻松扩展至服务器端的多线程和多用户场景。