Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 345-350.doi: 10.11896/jsjkx.210900047

• Information Security • Previous Articles     Next Articles

Secure Multi-party Computing Protocol Based on Efficient Fully Homomorphic Encryption

ZHU Zong-wu, HUANG Ru-wei   

  1. School of Computer and Electronic Information,Guangxi University,Nanning 530004,China
  • Received:2021-09-06 Revised:2022-03-11 Online:2022-11-15 Published:2022-11-03
  • About author:ZHU Zong-wu,born in 1997,postgra-duate,is a member of China Computer Federation.His main research interests include homomorphic encryption and secure multi-party computing.
    HUANG Ru-wei,born in 1978,Ph.D,professor,is a member of China Computer Federation.Her main research interests include cloud computing and homomorphic encryption.
  • Supported by:
    National Natural Science Foundation of China(62062009)and Guangxi Innovation-Driven Development Project(AA17204058-17,AA18118047-7).

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Abstract: In view of the problem of large ciphertext size and low efficiency of the current secure multi-party computation protocol based on fully homomorphic encryption,this paper proves that the fully homomorphic encryption scheme that supports multi-bit encryption proposed by Chen et al. satisfies the key homomorphism.Based on this scheme and threshold decryption,an efficient and secure multi-party computation protocol with three rounds of interaction under the common random string(CRS) model is designed.The protocol can be concluded from the non-interactive zero knowledge proof that the protocol is safe under the malicious model,and its security can be boiled down to the variants of the learning with errors problem(LWE).Compared with the existing protocol of the CRS model,the protocol supports multi-bit encryption,which can effectively reduce the complexity of the NAND gate.At the same time,the size of the ciphertext is smaller,the amount of calculation is reduced,and the time and space efficiency are improved.

Key words: Fully homomorphic encryption, Secure multi-party computation, Multi-bit encryption, Threshold decryption, Learning with errors

CLC Number: 

  • TP309
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