Fine-grained Access Control And Sharing Method For Learning Resources Of Blockchain

Under the background of the era of educational informatization 2.0,various online learning resources are becoming more and more abundant.In digital learning scenarios,the safe and efficient sharing of learning resources has become an urgent issue.Access control has always been a popular direction in the field of privacy protection and sharing.With the rise of blockchain technology,data sharing between organizations is more established in distributed blocks that are decentralized,immutable and low in cost of trust.On the chain ledger,however,the traditional blockchain technology does not well support the storage and on-chain of massive data,and all data on the chain is completely open and transparent to participants,and cannot meet the user's needs for privacy protection.Therefore,this article designs a fine-grained access control and sharing method for learning resources of blockchain(BC-LRFGAC).This method extends the storage form of the blockchain to support the uploading and sharing of various types of learning resources,ensures that the traceable data during the on-chain process cannot be tampered with,and can meet the requirements of achieving fast file access control in the blockchain environment Update and user attribute revocation.The article focuses on the following:(1)Proposed a fine-grained access control method bc-lrfgac for learning resources in blockchain,which integrates PKI verification system and distributed storage system of distributed CA nodes to realize fine-grained access control for learning resources in the alliance blockchain and significantly improve data sharing efficiency and security.(2)To solve the storage limitation of the federated block chain Hyperledger Fabric,this paper designs an IPFS-BCSEM(Inter-Planetary File System-Blockchain Storage Extension Method)protocol,which has more distributed storage capability and less storage overhead in the learning resource upload to blockchain.(3)The introduction of the AA attribute authorization mechanism in the blockchain effectively reduces the credible prerequisites of the AA institution in the access control system.Performing the pre-decryption operation in the CA cluster eliminates the need to update the original ciphertext when updating the attributes,which greatly improves the efficiency of on-chain data access control.Finally,in order to verify the correctness and security of the BC-LRFGAC method,this paper builds a complete prototype system based on the Hyperledger Fabric license chain,and uses the Hyperledger Caliper evaluation model to perform rigorous physical resource consumption and performance in this experiment Evaluation of indicators.Experiments show that the method in this paper achieves a good balance in ensuring performance indicators,security indicators,and resource consumption.Among them,the throughput of user access control attribute revocation has reached 44tps,and the query throughput has reached 457tps.There is no significant gap in control methods.