High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA
Homomorphic encryption (HE) offers great capabilities that can solve a wide range of privacy-preserving computing problems. This tool allows anyone to process encrypted data producing encrypted results that only the decryption key’s owner can decrypt. Although HE has been realized in several public...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ruhr-Universität Bochum
2018-05-01
|
| Series: | Transactions on Cryptographic Hardware and Embedded Systems |
| Subjects: | |
| Online Access: | https://tches.iacr.org/index.php/TCHES/article/view/875 |
| _version_ | 1818212922183122944 |
|---|---|
| author | Ahmad Al Badawi Bharadwaj Veeravalli Chan Fook Mun Khin Mi Mi Aung |
| author_facet | Ahmad Al Badawi Bharadwaj Veeravalli Chan Fook Mun Khin Mi Mi Aung |
| author_sort | Ahmad Al Badawi |
| collection | DOAJ |
| description | Homomorphic encryption (HE) offers great capabilities that can solve a wide range of privacy-preserving computing problems. This tool allows anyone to process encrypted data producing encrypted results that only the decryption key’s owner can decrypt. Although HE has been realized in several public implementations, its performance is quite demanding. The reason for this is attributed to the huge amount of computation required by secure HE schemes. In this work, we present a CUDAbased implementation of the Fan and Vercauteren (FV) Somewhat HomomorphicEncryption (SHE) scheme. We demonstrate several algebraic tools such as the Chinese Remainder Theorem (CRT), Residual Number System (RNS) and Discrete Galois Transform (DGT) to accelerate and facilitate FV computation on GPUs. We also show how the entire FV computation can be done on GPU without multi-precision arithmetic. We compare our GPU implementation with two mature state-of-the-art implementations: 1) Microsoft SEAL v2.3.0-4 and 2) NFLlib-FV. Our implementation outperforms them and achieves on average 5.37x, 7.37x, 22.22x, 5.11x and 13.18x (resp. 2.03x, 2.94x, 27.86x, 8.53x and 18.69x) for key generation, encryption, decryption, homomorphic addition and homomorphic multiplication against SEAL-FVRNS (resp. NFLlib-FV). |
| first_indexed | 2024-12-12T05:56:05Z |
| format | Article |
| id | doaj.art-329496934753422ca4514544eedff595 |
| institution | Directory Open Access Journal |
| issn | 2569-2925 |
| language | English |
| last_indexed | 2024-12-12T05:56:05Z |
| publishDate | 2018-05-01 |
| publisher | Ruhr-Universität Bochum |
| record_format | Article |
| series | Transactions on Cryptographic Hardware and Embedded Systems |
| spelling | doaj.art-329496934753422ca4514544eedff5952022-12-22T00:35:33ZengRuhr-Universität BochumTransactions on Cryptographic Hardware and Embedded Systems2569-29252018-05-012018210.13154/tches.v2018.i2.70-95High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDAAhmad Al Badawi0Bharadwaj Veeravalli1Chan Fook Mun2Khin Mi Mi Aung3Faculty of Engineering, National University of Singapore; A * STAR, Data Storage Institute, SingaporeFaculty of Engineering, National University of Singapore, SingaporeA * STAR, Data Storage Institute, SingaporeA * STAR, Data Storage Institute, SingaporeHomomorphic encryption (HE) offers great capabilities that can solve a wide range of privacy-preserving computing problems. This tool allows anyone to process encrypted data producing encrypted results that only the decryption key’s owner can decrypt. Although HE has been realized in several public implementations, its performance is quite demanding. The reason for this is attributed to the huge amount of computation required by secure HE schemes. In this work, we present a CUDAbased implementation of the Fan and Vercauteren (FV) Somewhat HomomorphicEncryption (SHE) scheme. We demonstrate several algebraic tools such as the Chinese Remainder Theorem (CRT), Residual Number System (RNS) and Discrete Galois Transform (DGT) to accelerate and facilitate FV computation on GPUs. We also show how the entire FV computation can be done on GPU without multi-precision arithmetic. We compare our GPU implementation with two mature state-of-the-art implementations: 1) Microsoft SEAL v2.3.0-4 and 2) NFLlib-FV. Our implementation outperforms them and achieves on average 5.37x, 7.37x, 22.22x, 5.11x and 13.18x (resp. 2.03x, 2.94x, 27.86x, 8.53x and 18.69x) for key generation, encryption, decryption, homomorphic addition and homomorphic multiplication against SEAL-FVRNS (resp. NFLlib-FV).https://tches.iacr.org/index.php/TCHES/article/view/875Homomorphic EncryptionFVParallel ProcessingGPGPUCUDA |
| spellingShingle | Ahmad Al Badawi Bharadwaj Veeravalli Chan Fook Mun Khin Mi Mi Aung High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA Transactions on Cryptographic Hardware and Embedded Systems Homomorphic Encryption FV Parallel Processing GPGPU CUDA |
| title | High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA |
| title_full | High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA |
| title_fullStr | High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA |
| title_full_unstemmed | High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA |
| title_short | High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA |
| title_sort | high performance fv somewhat homomorphic encryption on gpus an implementation using cuda |
| topic | Homomorphic Encryption FV Parallel Processing GPGPU CUDA |
| url | https://tches.iacr.org/index.php/TCHES/article/view/875 |
| work_keys_str_mv | AT ahmadalbadawi highperformancefvsomewhathomomorphicencryptionongpusanimplementationusingcuda AT bharadwajveeravalli highperformancefvsomewhathomomorphicencryptionongpusanimplementationusingcuda AT chanfookmun highperformancefvsomewhathomomorphicencryptionongpusanimplementationusingcuda AT khinmimiaung highperformancefvsomewhathomomorphicencryptionongpusanimplementationusingcuda |