Paper 2020/146

Hierarchical Identity-Based Encryption with Tight Multi-Challenge Security

Roman Langrehr and Jiaxin Pan

Abstract

We construct the first hierarchical identity-based encryption (HIBE) scheme with tight adaptive security in the multi-challenge setting, where adversaries are allowed to ask for ciphertexts for multiple adaptively chosen identities. Technically, we develop a novel technique that can tightly introduce randomness into user secret keys for hierarchical identities in the multi-challenge setting, which cannot be easily achieved by the existing techniques for tightly multi-challenge secure IBE. In contrast to the previous constructions, the security of our scheme is independent of the number of user secret key queries and that of challenge ciphertext queries. We prove the tight security of our scheme based on the Matrix Decisional Diffie-Hellman Assumption, which is an abstraction of standard and simple decisional Diffie-Hellman assumptions, such as the k-Linear and SXDH assumptions. Finally, we also extend our ideas to achieve tight chosen-ciphertext security and anonymity, respectively. These security notions for HIBE have not been tightly achieved in the multi-challenge setting before.

Metadata
Available format(s)
PDF
Category
Public-key cryptography
Publication info
A major revision of an IACR publication in PKC 2020
Keywords
Hierarchical identity-based encryptiontight securitymulti-challenge securitychosen-ciphertext securityanonymity
Contact author(s)
roman langrehr @ inf ethz ch
jiaxin pan @ ntnu no
History
2020-02-10: received
Short URL
https://ia.cr/2020/146
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2020/146,
      author = {Roman Langrehr and Jiaxin Pan},
      title = {Hierarchical Identity-Based Encryption with Tight Multi-Challenge Security},
      howpublished = {Cryptology {ePrint} Archive, Paper 2020/146},
      year = {2020},
      url = {https://eprint.iacr.org/2020/146}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.