Survey On Security Causal Consistence Using Hash Graph

Data causal consistence in cloud storage environment refers to the need to ensure that the data and dependency series in other replicas can also be updated while updating the data and dependency sets in that node,the moudle could meet high availability and performance needs even in a distributed storage environment.This constraint requires the causal order of user events only when the effects of causal dependency are visible.At present,the security situation is becoming increasingly severe,and the security risks faced by data causal consistence are receiving more and more attention.Users store data in the cloud,but risks such as data leakage and tampering with sensitive data are unavoidable in the cloud service environment.At present,there are little research results that consider security risks in data causal consistence research.This paper mainly studies the secure data causal consistence storage model.The main tasks completed are as follows:(1)Design optimized causal consistency constraints and security verification mechanisms for user read and write operationsIn order to solve the problem of large performance overhead caused by causal consistence constraints in distributed storage,this paper proposes a causal consistency using partial stable vectors and sign of dependency series based on hybrid logic clock and data center stability vectors combined with the HashGraph consensus algorithm Model: CDH model(Causal consistence using part Data center stable vector and Hash of dependency series).We design a check value for the data accessed by the user while the user read and write operations,and provide a check value verification mechanism on the server and client respectively.At the same time,in order to reduce the communication overhead of data synchronization in large-scale distributed storage,the partial data center stability vector partDSV is used to provide a dependency series for incremental data update.Finally,a performance test interface is developed to provide user update visibility delay and throughput statistics,and assessment functions under security constraints for the data causal consistence model.(2)Optimize the way to synchronize data between data centersDrawing on the ideas of HashGraph consensus mechanism,data centers randomly synchronize the latest local stable state with the other replicas and the sign of data dependency series,the partitions also share the latest state of the data center.The node and the partitions would verificate the HDS of the latest status after local update with the received HDS.After the data centers and the partitions are updated internally,all the data centers would meet the causal consistence constraint.Finally,experiments have proved that the CDH model not only provides safe causal consistence constraint,and the time required to synchronize data between replicas to reach consensus is significantly reduced.