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Trustworthy Blockchain Data Sharing Model Based on Multi-authority ABE and zk-SNARK

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Blockchain and Trustworthy Systems (BlockSys 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1490))

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Abstract

With the advent of the era of big data, data sharing has become a widespread demand. However, the process of data sharing is faced with problems such as privacy leakage, access control ambiguity and data reliability uncertainty. The blockchain data sharing model based on attributed based encryption (ABE) is an existing solution to data sharing, this model enables each node of the blockchain to share fine-grained data in a weak trust environment. However, it puts the access policy and attributes directly on the blockchain, so every one in the blockchain can access these access policies and attributes, which will cause privacy leakage. A privacy protection model based on zk-SNARK was proposed in this article to solve this privacy leakage problem. This model uses the blockchain double-chain structure, and realizes the fine-grained access control of data sharing based on Multi-authoirty ABE scheme. At the same time, this model adopts zk-SNARK technologies to protect sensitive access policies and sensitive attributes from disclosure, effectively defending the privacy of users when data sharing in blockchain, avoiding the risk of single authorization.

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Acknowledgments

This work is supported by the Emerging Interdisciplinary Project of CUFE, Ministry of education of Humanities and Social Science project (No. 19YJCZH178), the National Natural Science Foundation of China (No.61906220).

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Li, Y., Zhang, G., Zhu, J., Wang, X., Wang, Y. (2021). Trustworthy Blockchain Data Sharing Model Based on Multi-authority ABE and zk-SNARK. In: Dai, HN., Liu, X., Luo, D.X., Xiao, J., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2021. Communications in Computer and Information Science, vol 1490. Springer, Singapore. https://doi.org/10.1007/978-981-16-7993-3_24

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  • DOI: https://doi.org/10.1007/978-981-16-7993-3_24

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