JOURNAL ARTICLE

Generic Construction of Fully Anonymous Broadcast Authenticated Encryption with Keyword Search with Adaptive Corruptions

Keita Emura

Year: 2023 Journal:   IET Information Security Vol: 2023 (1)   Publisher: Institution of Engineering and Technology

Abstract

As a multireceiver variant of public key authenticated encryption with keyword search (PAEKS), broadcast authenticated encryption with keyword search (BAEKS) was proposed by Liu et al. (ACISP 2021). BAEKS focuses on receiver anonymity, where no information about the receiver is leaked from ciphertexts, which is reminiscent of the anonymous broadcast encryption. Here, there are rooms for improving their security definitions, e.g., two challenge sets of receivers are selected before the setup phase, and an adversary is not allowed to corrupt any receiver. In this paper, we propose a generic construction of BAEKS derived from PAEKS that provides ciphertext anonymity and consistency in a multireceiver setting. The proposed construction is an extension of the generic construction proposed by Libert et al. (PKC 2012) for the fully anonymous broadcast encryption and provides adaptive corruptions. We also demonstrate that the Qin et al. PAEKS scheme (ProvSec 2021) provides ciphertext anonymity and consistency in a multireceiver setting and can be employed as a building block of the proposed generic construction.

Keywords:
Broadcast encryption Computer science Ciphertext Encryption Authenticated encryption Anonymity Block (permutation group theory) Extension (predicate logic) Computer security Theoretical computer science Public-key cryptography Mathematics

Metrics

1
Cited By
0.26
FWCI (Field Weighted Citation Impact)
41
Refs
0.58
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Cryptography and Data Security
Physical Sciences →  Computer Science →  Artificial Intelligence
Internet Traffic Analysis and Secure E-voting
Physical Sciences →  Computer Science →  Artificial Intelligence
Privacy-Preserving Technologies in Data
Physical Sciences →  Computer Science →  Artificial Intelligence

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