Efficient number theoretic transform implementation on GPU for homomorphic encryption
Özerk, Özgün and Elgezen, Can and Mert, Ahmet Can and Öztürk, Erdinç and Savaş, Erkay (2021) Efficient number theoretic transform implementation on GPU for homomorphic encryption. The Journal of Supercomputing . ISSN 0920-8542 (Print) 1573-0484 (Online) Published Online First http://dx.doi.org/10.1007/s11227-021-03980-5
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Official URL: http://dx.doi.org/10.1007/s11227-021-03980-5
Lattice-based cryptography forms the mathematical basis for current homomorphic encryption schemes, which allows computation directly on encrypted data. Homomorphic encryption enables privacy-preserving applications such as secure cloud computing; yet, its practical applications suffer from the high computational complexity of homomorphic operations. Fast implementations of the homomorphic encryption schemes heavily depend on efficient polynomial arithmetic, multiplication of very large degree polynomials over polynomial rings, in particular. Number theoretic transform (NTT) accelerates large polynomial multiplication significantly, and therefore, it is the core arithmetic operation in the majority of homomorphic encryption scheme implementations. Therefore, practical homomorphic applications require efficient and fast implementations of NTT in different computing platforms. In this work, we present an efficient and fast implementation of NTT, inverse NTT and NTT-based polynomial multiplication operations for GPU platforms. To demonstrate that our GPU implementation can be utilized as an actual accelerator, we experimented with the key generation, the encryption and the decryption operations of the Brakerski/Fan–Vercauteren (BFV) homomorphic encryption scheme implemented in Microsoft’s SEAL homomorphic encryption library on GPU, all of which heavily depend on the NTT-based polynomial multiplication. Our GPU implementations improve the performance of these three BFV operations by up to 141.95 ×, 105.17 × and 90.13 × , respectively, on Tesla v100 GPU compared to the highly optimized SEAL library running on an Intel i9-7900X CPU.
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