1. FPGA-Based Hardware Accelerator for Leveled Ring-LWE Fully Homomorphic Encryption
- Author
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Luogeng Tian, Yang Su, Chen Yang, and Bailong Yang
- Subjects
polynomial multiplication ,Speedup ,General Computer Science ,Computer science ,Pipeline (computing) ,Clock rate ,02 engineering and technology ,Parallel computing ,Encryption ,BGV scheme ,Privacy-preserving ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,ring-LWE ,hardware accelerator ,Virtex ,business.industry ,General Engineering ,Homomorphic encryption ,020206 networking & telecommunications ,leveled fully homomorphic encryption ,Hardware acceleration ,020201 artificial intelligence & image processing ,Multiplication ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,lcsh:TK1-9971 ,Learning with errors - Abstract
Fully homomorphic encryption (FHE) allows arbitrary computation on encrypted data and has great potential in privacy-preserving cloud computing and securely outsource computational tasks. However, the excessive computation complexity is the key limitation that restricting the practical application of FHE. In this paper we proposed a FPGA-based high parallelism architecture to accelerate the FHE schemes based on the ring learning with errors (RLWE) problem, specifically, we presented a fast implementation of leveled fully homomorphic encryption scheme BGV. In order to reduce the computation latency and improve the performance, we applied both circuit-level and block-level pipeline strategies to improve clock frequency, and as a result, enhance the processing speed of polynomial multipliers and homomorphic evaluation functions. At the same time, multiple polynomial multipliers and modular reduction units were deployed in parallel to further improve the hardware performance. Finally, we implemented and tested our architecture on a Virtex UltraScale FPGA platform. Runing at 150MHz, our implementation achieved $4.60\times \sim 9.49\times $ speedup with respect to the optimized software implementation on Intel i7 processor running at 3.1GHz for homomorphic encryption and decryption, and the throughput was increased by $1.03\times \sim 4.64\times $ compared to the hardware implementation of BGV. While compared to the hardware implementation of FV, the throughput of our accelerator also achieved $5.05\times $ and $167.3\times $ speedup for homomorphic addition and homomorphic multiplication operation respectively.
- Published
- 2020
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