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Designated server-aided revocable identity-based keyword search on lattice

Authors
  • Guo, Ying1
  • Meng, Fei2
  • Cheng, Leixiao3
  • Dong, Xiaolei4
  • Cao, Zhenfu4, 5, 6
  • 1 Shanghai Jiao Tong University, Shanghai, 200240, China , Shanghai (China)
  • 2 Shandong University, South Shanda Road, No.27, Jinan, 250100, China , Jinan (China)
  • 3 Fudan University, Handan Road, No.220, Shanghai, 200433, China , Shanghai (China)
  • 4 East China Normal University, Shanghai, 200062, China , Shanghai (China)
  • 5 Peng Cheng Laboratory, Shenzhen, 518055, China , Shenzhen (China)
  • 6 Tongji University, Shanghai, 200092, China , Shanghai (China)
Type
Published Article
Journal
EURASIP Journal on Wireless Communications and Networking
Publisher
Springer Science and Business Media LLC
Publication Date
Sep 06, 2021
Volume
2021
Issue
1
Identifiers
DOI: 10.1186/s13638-021-02006-1
Source
Springer Nature
Keywords
Disciplines
  • Security & Privacy Solutions for Smart City
License
Green

Abstract

Public key encryption scheme with keyword search is a promising technique supporting search on encrypted data without leaking any information about the keyword. In real applications, it’s critical to find an effective revocation method to revoke users in multi-user cryptosystems, when user’s secret keys are exposed. In this paper, we propose the first designated server-aided revocable identity-based encryption scheme with keyword search (dSR-IBKS) from lattice. The dSR-IBKS model requires each user to keep just one private key corresponding with his identity and does not need to communicate with the key generation center or the server during key updating. We have proved that our scheme can achieve chosen keyword indistinguishability in the standard model. In particular, our scheme can designate a unique tester to test and return the search results, therefore no other entity can guess the keyword embedded in the ciphertext by generating search queries and doing the test by itself. We provide a formal security proof of our scheme assuming the hardness of the learning with errors problem on the standard model.

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