北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (1): 125-133.doi: 10.13190/j.jbupt.2017-163

• 研究报告 • 上一篇    下一篇

改进的格上基于多身份全同态加密方案

汤永利, 胡明星, 叶青, 秦攀科, 于金霞   

  1. 河南理工大学 计算机科学与技术学院, 河南 焦作 454000
  • 收稿日期:2017-08-14 出版日期:2018-02-28 发布日期:2018-01-04
  • 作者简介:胡明星(1994-),男,硕士生,E-mail:18236885186@163.com;叶青(1981-),女,硕士生导师.
  • 基金资助:
    "十三五"国家密码发展基金项目(MMJJ20170122);河南省科技厅项目(142300410147);河南省教育厅项目(12A520021,16A520013);河南理工大学博士基金项目(B2014-044)

Improved Multi-Identity Based Fully Homomorphic Encryption Scheme over Lattices

TANG Yong-li, HU Ming-xing, YE Qing, QIN Pan-ke, YU Jin-xia   

  1. School of Computer Science and Technology, Henan Polytechnic University, Henan Jiaozuo 454000, China
  • Received:2017-08-14 Online:2018-02-28 Published:2018-01-04

摘要: 针对格上基于多身份的全同态加密方案(mIBFHE)中陷门函数低效的问题,提出一种改进的格上mIBFHE方案.首先利用MP12陷门函数结合对偶Regev算法构造出一种可转化的基于身份的加密(IBE)方案,并构造出一种支持标准模型下IBE方案转化的Mask系统;然后基于该系统利用特征向量思想将构造出的IBE方案转化为mIBFHE方案.对比分析结果表明,新方案较同类方案在陷门生成和原像采样阶段均有效率提升,且格的维数、密文和运算密文尺寸等明显缩短.在标准模型下,方案的安全性归约至格上容错学习问题的难解性,并包含严格的安全性证明.

关键词: 格, 基于多身份的加密, 全同态加密, 标准模型, 容错学习问题

Abstract: Aiming at low efficiency of trapdoor function in multi-identity based fully homomorphic encryption (mIBFHE) schemes, a new mIBFHE scheme was proposed. Firstly, the MP12 trapdoor function with Dual-Regev algorithm was combined to construct a transformable identity-based encryption (IBE) scheme, and a Mask system which supports to transform IBE scheme presented to mIBFHE scheme under standard model. Then, based on presented Mask system and eigenvector idea, the IBE schemes was transformed to mIBFHE scheme. Comparing with the similar schemes, the efficiency of the scheme is improved in trapdoor generation and preimage sampling stage, and the lattice dimension, the size of ciphertext and evaluated ciphertext, etc. are obviously reduced. The security of the presented scheme strictly is reduced to the hardness of learning with errors problem in the standard model.

Key words: lattices, multi-identity based encryption, fully homomorphic encryption, standard model, learning with errors

中图分类号: