Attribute access control scheme supporting data sensitivity grading
In the era of big data,heterogeneous data from multiple sources brings severe challenges to data security management.At the same time,the attribute-based encryption scheme for traditional ciphertext strategies exhibits poor performance in terms of user attribute revocation.Aiming at these problems,an attribute access control scheme that classifies data sensitivity for sensitive data groups is proposed in the paper.Firstly,we establish a data sensitivity classification and grading strategy.Then,we accu-rately assess and classify data sensitivity and propose differentiated encryption strategies for data with varying sensitivities.Addi-tionally,we achieve the revocability of CP-ABE encrypted user attributes based on the trapdoor collision feature of chameleon hash algorithm.The scheme is proven to satisfy IND-CPA security under the general group and random oracle models.Furthermore,performance analysis and experimental results show that the proposed scheme can improve the efficiency of data storage and en-cryption,reduce the burden of blockchain storage and computational costs when user attributes are revoked.As a result,this scheme dramatically improves the flexibility and security of data management.
data hierarchical classificationattribute-based encryptionattribute revocationchameleon hashingblockchain
万方数据
维普
