Research On Privacy Protection And Access Control Technology In Data Sharing

With the rapid development of information application technology,complete data has become a necessary prerequisite for the development of various industries.In this context,data sharing has also become one of the popular applications of cloud storage technology.However,due to the huge value inherent in the data,the security problems in the data sharing process have become more and more serious.Malicious users,malicious cloud storage servers,and hackers will spy on users' privacy through various methods.The two common situations are inferring the content of user access data through user data access patterns and mining sensitive user information through published data.Regarding the protection of user data access patterns,inadvertent random access technologies are usually used to hide user access patterns.However,these technologies cannot be applied in multi-user scenarios and cannot achieve fine-grained management of data access permissions.In response to the above problems,this article proposes three attribute-based broadcast-encrypted multi-user ORAM data sharing schemes.The main work content is as follows:1.Research the principle of the classic ORAM framework so far,analyze its performance and characteristics,combine the multi-user data sharing scenario,and select path ORAM as the basic framework of the program.Understand the basic principles of attribute-based broadcast encryption algorithms.2.Propose the idea of multi-user ORAM data sharing basic scheme based on attributebased broadcast encryption,and build a system model composed of third-party trusted agents,users,and cloud storage servers to realize multi-user shared data access mode protection and revocable users Access control.Facing two different data access application scenarios,two multi-user shared ORAM solutions with controllable read and write permissions are proposed on the basis of the basic solution.After analysis and discussion,it is proved that the above scheme satisfies CPA security and has good communication complexity.Finally,it is proved by experimental simulation that this scheme has better average user access efficiency when dealing with simultaneous access by multiple users,and has certain practicability.Regarding the protection of sensitive information contained in published data,one of the most commonly used methods is data anonymization.However,concealing some features will cause the subsequent mining value of the published data to be greatly reduced,which violates the original intention of data sharing.In response to the above problems,this paper proposes a K-anonymous feature selection algorithm based on extreme gradient enhancement of feature importance.The main work content is as follows:1.Analyze the most classic three types of data anonymization technologies,and combined with the background that relational data tables are most frequently encountered with link attacks,select K-anonymity technology as the feature selection algorithm for this project.Research the basic principles and features of extreme gradient boosting algorithms and feature importance indicators.2.Propose a K-anonymous feature selection algorithm based on extreme gradients to enhance the importance of features.The algorithm uses feature importance to measure feature value,and combines K anonymity feature selection algorithm to protect private information,thereby simultaneously achieving data sensitive information and data value protection.After a comparative analysis and discussion of the simulation experiment results on the real data set,the correctness and efficiency of the scheme is proved.