| As the amount of data in various industries increases,data outsourcing storage has developed rapidly.However,there are still large security issues in data outsourcing storage,and cloud storage is a new type of data outsourcing.There are naturally more security issues.Unlike traditional hosted outsourced storage on the server side,cloud storage is relatively more open and data is more easily accessible to attackers.This way data outsourcing is more vulnerable to a variety of attacks and threats in the cloud.Currently,the main problem facing data outsourcing storage is security.On the other hand,privacy data protection in the current cloud environment Most of the methods use traditional encryption technology,resulting in the lack of effective technical means to ensure high availability of data.This paper aims to provide efficient and secure data protection for the problems in the storage of private data in the cloud environment.Privacy data fragmentation storage model,BEA(Bond Energy Algorithm,BEA)algorithm to improve privacy in fragment storage,information entropy quantization privacy technology and user-based requirements Extensive and in-depth research has been carried out by selecting encryption optimization techniques.The main research results obtained include:1.We proposed a cloud environment privacy data fragment storage model.This model combines the characteristics of data segmentation in the cloud environment,taking into account the characteristics of the fragmentation data and Factors such as the full life cycle characteristics of fragmented data,analyze the basic requirements of private data in the fragment storage process,and define,describe and analyze data fragmentation,data distribution,data manipulation and data destruction.A basic data fragmentation storage model was established,and corresponding evaluation indicators different from the traditional methods were given.Based on the basic model,the key technologies of vertical segmentation and selective encryption for cloud data privacy data segmentation protection are further elaborated.Using this model can effectively protect the privacy of fragmented data and meet the privacy needs of users.2.Design and implementation of a vertical segmentation optimization method based on BEA algorithm to achieve efficient protection of private information.This paper chooses BEA algorithm and AVP(Adaptable vertical partitioning)algorithm as the privacy protection algorithm for optimizing vertical segmentation.Firstly,the vertical segmentation of two vertical segmentation algorithms is analyzed.The AVP algorithm uses the exhaustive method to merge in the process of merging calculation,which seriously reduces the efficiency of the AVP algorithm.Based on this,the BEA-AVP privacy protection vertical sharding method is proposed.That is,the BEA algorithm is used to sort the non-privacy attributes and then the AVP algorithm is used to merge the adjacent nodes to generate the fragments,and finally the privacy attributes are added to the fragment set one by one.BEA-AVP privacy protection vertical fragmentation algorithm is not the same as other fragmentation methods.However,by reducing the number of nodes in the AVP merging process,the efficiency of sharding is greatly improved,especially in the case of a large number of attributes,the sharding comparison speed is more obvious.Finally,the possible shortcomings of other vertical privacy segmentation methods combining BEA and AVP are analyzed.The specific technical implementation of the BEA-AVP privacy protection vertical fragmentation algorithm is designed,and the experimental test analysis shows that the improved privacy protection method can efficiently segment the data and achieve the goal of privacy protection,providing privacy data fragmentation storage.The key technology.3.Design and implementation of a quantitative vertical segmentation method based on information entropy.Firstly,the role of information entropy in quantifying the amount of private information is analyzed.Using information entropy,the private data before and after fragmentation can be quantified and compared.The goal of implementing vertical segmentation based on information entropy method is given.Then,a concrete example is given for information entropy to measure privacy,and the automatic acquisition of privacy constraints is realized through information entropy.At the same time,the information entropy is used to realize the calculation of the minimum number of fragments.Finally,in order to minimize the amount of data information after fragmentation,the minimum entropy fragmentation algorithm is realized by information entropy.Through experiments,The vertical sharding method based on information entropy is effective,and the sharding time is linear with the number of attributes and the number of records,and the sharding efficiency is faster than the traditional method.4.Design and implementation of a selective encryption optimization technology based on user requirements.Firstly,the application requirements of selecting encryption in fragmented storage are analyzed.The selection and encryption technology is used to realize the user's access control requirements.A selective encryption improvement technique based on user access requirements is proposed to optimize the number of nodes in the strategy map and reduce unnecessary intermediate nodes to reduce the number of key evolutions.Finally,through experiments to simulate the different user access requirements,the difference in time required by different encryption strategies,the improved user-based selection encryption technology can be more efficient to achieve user-based access mode optimization.The above research results are aimed at the key technologies in the cloud environment privacy data fragmentation storage,and provide users with an efficient and adaptive protection method while ensuring the privacy protection requirements.It can effectively support the fragmentation storage of private data in the cloud environment and improve the security threats faced by private data outsourcing storage. |
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