| Title | The Fast Paillier Decryption with Montgomery Modular Multiplication Based on OpenMP |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Lin, Decong, Cao, Hongbo, Tian, Chunzi, Sun, Yongqi |
| Conference Name | 2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) |
| Date Published | nov |
| Keywords | artificial intelligence, Chinese remainder theorem, data privacy, Data security, Distributed databases, exponentiation, homomorphic encryption, human factors, Montgomery modular multiplication, OpenMP, Paillier, parallel architectures, Programming, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | With the increasing awareness of privacy protection and data security, people's concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. The experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier. |
| DOI | 10.1109/PAAP56126.2022.10010630 |
| Citation Key | lin_fast_2022 |
The Fast Paillier Decryption with Montgomery Modular Multiplication Based on OpenMP