IIBES: a proposed framework to improve the identity-based encryption system for securing federated learning

INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS(2022)

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摘要
Purpose In the field of cryptography, authentication, secrecy and identification can be accomplished by use of secret keys for any computer-based system. The need to acquire certificates endorsed through CA to substantiate users for the barter of encoded communications is one of the most significant constraints for the extensive recognition of PKC, as the technique takes too much time and susceptible to error. PKC's certificate and key management operating costs are reduced with IBC. IBE is a crucial primeval in IBC. The thought behind presenting the IBE scheme was to diminish the complexity of certificate and key management, but it also gives rise to key escrow and key revocation problem, which provides access to unauthorised users for the encrypted information. Design/methodology/approach This paper aims to compare the result of IIBES with the existing system and to provide security analysis for the same and the proposed system can be used for the security in federated learning. Findings Furthermore, it can be implemented using other encryption/decryption algorithms like elliptic curve cryptography (ECC) to compare the execution efficiency. The proposed system can be used for the security in federated learning. Originality/value As a result, a novel enhanced IBE scheme: IIBES is suggested and implemented in JAVA programming language using RSA algorithm, which eradicates the key escrow problem through eliminating the need for a KGC and key revocation problem by sing sub-KGC (SKGC) and a shared secret with nonce. IIBES also provides authentication through IBS as well as it can be used for securing the data in federated learning.
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关键词
Public key cryptography (PKC), Identity-based cryptography (IBC), Identity-based encryption (IBE), Identity-based signature (IBS), Key generation centre (KGC), Key escrow problem, Key revocation problem, Federated learning
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