A Scalable Architecture for Efficient Multi-bit Fully Homomorphic Encryption

• URL: http://arxiv.org/abs/2509.12676v1
• Date: Tue, 16 Sep 2025 05:00:57 GMT
• Title: A Scalable Architecture for Efficient Multi-bit Fully Homomorphic Encryption
• Authors: Jiaao Ma, Ceyu Xu, Lisa Wu Wills,
• Abstract summary: We introduce Taurus, a hardware accelerator designed to enhance the efficiency of multi-bit TFHE computations.<n>Taurus supports ciphertexts up to 10 bits by leveraging novel FFT units and optimizing memory bandwidth through key reuse strategies.<n>Our experiment results demonstrate that Taurus achieves up to 2600x speedup over a CPU, 1200x speedup over a GPU, and up to 7x faster compared to the previous state-of-the-art accelerator.
• Score: 1.4174227043241145
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In the era of cloud computing, privacy-preserving computation offloading is crucial for safeguarding sensitive data. Fully Homomorphic Encryption (FHE) enables secure processing of encrypted data, but the inherent computational complexity of FHE operations introduces significant computational overhead on the server side. FHE schemes often face a tradeoff between efficiency and versatility. While the CKKS scheme is highly efficient for polynomial operations, it lacks the flexibility of the binary TFHE (Torus-FHE) scheme, which offers greater versatility but at the cost of efficiency. The recent multi-bit TFHE extension offers greater flexibility and performance by supporting native non-polynomial operations and efficient integer processing. However, current implementations of multi-bit TFHE are constrained by its narrower numeric representation, which prevents its adoption in applications requiring wider numeric representations. To address this challenge, we introduce Taurus, a hardware accelerator designed to enhance the efficiency of multi-bit TFHE computations. Taurus supports ciphertexts up to 10 bits by leveraging novel FFT units and optimizing memory bandwidth through key reuse strategies. We also propose a compiler with operation deduplication to improve memory utilization. Our experiment results demonstrate that Taurus achieves up to 2600x speedup over a CPU, 1200x speedup over a GPU, and up to 7x faster compared to the previous state-of-the-art TFHE accelerator. Moreover, Taurus is the first accelerator to demonstrate privacy-preserving inference with large language models such as GPT-2. These advancements enable more practical and scalable applications of privacy-preserving computation in cloud environments.

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