FGDB‐MLPP: A fine‐grained data‐sharing scheme with blockchain based on multi‐level privacy protection

Autor: Junyu Lin, Libo Feng, Jinli Wang, Fei Qiu, Bei Yu, Jing Cheng, Shaowen Yao
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Communications, Vol 18, Iss 4, Pp 309-321 (2024)
Druh dokumentu: article
ISSN: 1751-8636
1751-8628
DOI: 10.1049/cmu2.12737
Popis: Abstract In the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing.
Databáze: Directory of Open Access Journals
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