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Fully Homomorphic Encryption over the Torus (TFHE) allows arbitrary computations to happen directly on ciphertexts using homomorphic logic gates. However, each TFHE gate on state-of-the-art hardware platforms such as GPUs and FPGAs is extremely slow (> 0.2ms). Moreover, even the latest FPGA-based TFHE accelerator cannot achieve high energy efficiency, since it frequently invokes expensive double-precision floating point FFT and IFFT kernels. In this paper, we propose a fast and energy-efficient accelerator, MATCHA, to process TFHE gates. MATCHA supports aggressive bootstrapping key unrolling to accelerate TFHE gates without decryption errors by approximate multiplication-less integer FFTs and IFFTs, and a pipelined datapath. Compared to prior accelerators, MATCHA improves the TFHE gate processing throughput by 2.3x, and the throughput per Watt by 6.3x.
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An FPGA-based Programmable Vector Engine for Fast Fully Homomorphic Encryption over the Torus
This paper describes an FPGA-based vector engine to accelerate the bootstrapping procedure of Fast Fully Homomorphic Encryption over the Torus (TFHE), a popular and high-performance fully homomorphic encryption scheme. Most TFHE bootstraping comprises many matrix-vector operations that are implemented using Torus polynomials, which are not efficiently implemented on today's standard arithmetic hardware. Our implementation achieves linear performance scaling with up to 16 vector lanes. Future work will switch to an FFT-based polynomial multiplication scheme and switch to larger FPGA parts to accommodate more vector lanes.
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- PAR ID:
- 10282639
- Date Published:
- Journal Name:
- SPSL: Secure and Private Systems for Machine Learning (ISCA Workshop)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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