Abstract
Internet of Things (IoT) data from different trust domains is usually shared to assist in providing more services, where privacy sensitive information of shared data will be leaked or accessed without authorization. The traditional centralized access control method is difficult to adapt to the current dynamic and distributed large-scale IoT environment, and there is a risk of the single point of failure. To address these challenges, we propose a fine-grained access control framework for shared data based on cross-blockchain technology and Interplanetary File System (IPFS). In this framework, we firstly introduce a cross-blockchain module to realize cross-domain data sharing and solve the problem of data isolation between different data domains in IoT. Then IPFS is used to store the shared data, avoiding the risk of centralized storage. Combining symmetric encryption algorithm with ciphertext policy attribute based encryption (CP-ABE) algorithm, the fine-grained access control of shared data is guaranteed. In addition, the blockchain is applied to store the decryption key and the storage address of the original data, which records the authorization operation of access transactions and audits the access behavior of users. Experimental results show that the proposed scheme can provide higher performance compared to centralized access control methods.
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Acknowledgments
This work was supported by the National Key R &D Program of China under Grant 2020YFB1005604, the National Natural Science Foundation of China under Grant No.61902021 and No.62272031, the Beijing Natural Science Foundation under Grant No.4212008, the Open Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China under Grant No. ISECCA-202101.
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Cui, J., Duan, L., Li, M., Wang, W. (2023). A Fine-Grained Access Control Framework for Data Sharing in IoT Based on IPFS and Cross-Blockchain Technology. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_41
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DOI: https://doi.org/10.1007/978-3-031-28124-2_41
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