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Third-generation Fully Homomorphic Encryption (FHE), particularly the FHEW/TFHE schemes, is recognized for its balanced security requirements, small parameters, and low memory usage, though the current methods in the scenarios of Deep Neural Network (DNN) inference still have high computational costs, limiting the practical applicability. This work demonstrates how to improve practicality of the third-generation technologies for DNN tasks while preserving its key advantages. Our work focuses on two main contributions. First, we developed a computational architecture called FHE-Neuron, which reconfigures the parameters and bootstrapping structure of traditional FHEW/TFHE Boolean operations. This architecture significantly reducing the cost of encrypted DNN inference by dynamically switching the precision of encrypted data during computation—using high precision for cost-effective linear operations and low precision for computationally expensive nonlinear operations. Second, we introduced an FHE-aware Quantization and Fine-tuning framework that optimizes model parameters to align with FHE-Neuron’s constraints, ensuring high accuracy in encrypted inference. We validate our approach on various neural network models across several computing platforms. In our experiments, our method achieves one-image inference time on average 4.5 milliseconds for MNIST and 17 milliseconds for Fashion MNIST, achieving accuracy rates of 96.52% and 88.57% respectively. For the CIFAR-10 dataset, our system completes one image inference in 30 seconds with a 90.5% accuracy rate.
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GPU Acceleration for FHEW/TFHE Bootstrapping
Fully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data without decryption. Despite its great theoretical potential, the computational overhead remains a major obstacle for practical applications. To address this challenge, hardware acceleration has emerged as a promising approach, aiming to achieve real-time computation across a wider range of scenarios. In line with this, our research focuses on designing and implementing a Graphic Processing Unit (GPU)-based accelerator for the third generation FHEW/TFHE bootstrapping scheme, which features smaller parameters and bootstrapping keys particularly suitable for GPU architectures compared to the other generations.In summary, our accelerator offers improved efficiency, scalability, and flexibility for extensions, e.g., functional bootstrapping (Liu et al., Asiacrypt 2022), compared to current state-of-the-art solutions. We evaluate our implementation and demonstrate substantial speedup in the single-GPU setting, our bootstrapping achieves an 18x - 20x speedup compared to a 64-thread server-class CPU; by using 8 GPUs, the throughput can be further improved by 7x compared to the single-GPU implementation, confirming the scalability of our design. Furthermore, compared to the SoTA GPU solution TFHE-rs, we achieve a maximum speedup of 1.69x in AND gate evaluation. Finally, we benchmark several private machine learning applications, showing real-time solutions for (1) encrypted neural network inference for MNIST in 0.04 seconds per image, which is the fastest implementation to our knowledge.(2) private decision trees in 0.38 seconds for Iris dataset, where as prior 16 cores CPU implementation (Lu et al., IEEE S&P 2021) required 1.87 seconds; These results highlight the effectiveness and efficiency of our GPU-acceleration in real-world applications.As a technical highlight, we design a novel parallelization strategy tailored for FHEW/TFHE bootstrapping, allowing an automated optimization that partitions bootstrapping into multiple GPU thread blocks. This is necessary for FHEW/TFHE bootstrapping with scalable parameters, where the whole bootstrapping process may not fit into a single thread block. With this, our accelerator can support a broader range of parameters, making it ideal for upcoming privacy-preserving applications.
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- Award ID(s):
- 2336490
- PAR ID:
- 10712940
- Publisher / Repository:
- IACR
- Date Published:
- Journal Name:
- IACR Transactions on Cryptographic Hardware and Embedded Systems
- Volume:
- 2025
- Issue:
- 1
- ISSN:
- 2569-2925
- Page Range / eLocation ID:
- 314 to 339
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.46586/tches.v2025.i1.314-339
