Journal on Communications ›› 2017, Vol. 38 ›› Issue (11): 54-64.doi: 10.11959/j.issn.1000-436x.2017219

• Papers • Previous Articles     Next Articles

Novel hierarchical identity-based encryption scheme from lattice

Qing YE,Ming-xing HU,Yong-li TANG(),Kun LIU,Xi-xi YAN   

  1. College of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,China
  • Revised:2017-06-05 Online:2017-11-01 Published:2017-12-13
  • Supported by:
    The “13th Five-Year” National Crypto Development Foundation(MMJJ20170122);The National Natural Science Foundation of China(61300216);The Project of Science and Technology Department of Henan Province(142300410147);The Project of Education Department of Henan Province(18A413001);The Project of Education Department of Henan Province(16A520013);Doctoral Fund of Henan Polytechnic University(B2014-044);Doctoral Fund of Henan Polytechnic University(B2016-36)

Abstract:

Aiming at the high complexity in user’s private key extraction and large expansion ratio of trapdoor size in previous hierarchical identity-based encryption (HIBE) schemes,a new HIBE scheme was proposed.The implicit extension method to improve preimage sampling algorithm was used,and then combined the improved algorithm with MP12 trapdoor delegation algorithm to construct an efficient HIBE user’s private key extraction algorithm.Finally,the new extraction algorithm and the Dual-LWE algorithm was integrated to complete the scheme.Compared with the similar schemes,the efficiency of the proposed scheme was improved in system establishment and user’s private key extraction stage,the trapdoor size grows only linearly with the system hierarchical depth,and the improved preimage sample algorithm partly solves the Gaussian parameter increasing problem induced by MP12 trapdoor delegation.The security of the proposed scheme strictly reduces to the hardness of decisional learning with errors problem in the standard model.

Key words: lattice, hierarchical identity-based encryption, trapdoor delegation, standard model, learning with error

CLC Number: 

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