mlsanityData to 14 Oct 2026

TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture

arXiv 2302.01584 ↗First posted 3 Feb 2023cs.CR cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 65 references that are in the map. First posted on arXiv 3 Feb 2023.

Builds on

23 of 65 references.

References · 23

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11 more
  1. 2020Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Datacited by 52
  2. 2019XONN: XNOR-based Oblivious Deep Neural Network Inferencecited by 51
  3. 2021CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUcited by 46
  4. 2018Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inferencecited by 45
  5. 2021Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Datacited by 45
  6. 2020Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Datacited by 39
  7. 2019SHE: A Fast and Accurate Deep Neural Network for Encrypted Datacited by 36
  8. 2019Generalized Linear Rule Modelscited by 36
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 3.

4 papers.

  1. 2024DCT-CryptoNets: Scaling Private Inference in the Frequency Domaincited by 1
  2. 2026Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networkscited by 0
  3. 2026Encrypted Neural Networks without Overflowscited by 0
  4. 2026EI-DDLGN: Efficient Encrypted Inference with Deep Differentiable Logic Gate Networks under TFHEcited by 0