Research On Secure Query And Dynamic Update Method For Multi-dimensional Privacy Data

With the rapid development of mobile Internet and new-generation communication technologies,people generate a large amount of data every day,including private data containing sensitive user information.Most of these data are in the form of multiple dimensions(such as trade transaction data,document word frequency data,etc.)Stored in the cloud server.However,the multi-dimensional privacy data stored in the cloud is vulnerable to attacks by hackers or malicious administrators,leading to the leakage of sensitive user information.One of the most effective ways to solve this problem is to encrypt the multi-dimensional private data before storing it on the cloud server.However,how to efficiently calculate the encrypted data stored in the cloud is still a challenge.Based on the symmetric encryption technology,this article first stores the encrypted multi-dimensional privacy data in the cloud server,and when the user needs to query the data,the query request is sent to the data owner and the cloud server respectively.After the cloud server receives the query request,it directly queries the ciphertext data and sends the ciphertext result of the query to the user.At the same time,after the data owner receives the query request,the key is sent to the user,and the user decrypts the ciphertext inquired to obtain the final query result.The solution proposed in this paper can provide accurate and efficient multidimensional data query and update services under the premise of ensuring the privacy of data owners and users.First,this paper constructs a Skyline query scheme for multi-dimensional private data.This solution improves the existing B+ tree storage structure and combines it with a symmetric encryption algorithm to realize the secure storage of multi-dimensional private data.In the query preprocessing stage,the multi-dimensional privacy data to be queried is partitioned according to its dimensions,and unnecessary data is cut out in each partition based on the idea of pruning,so that accurate query results can be obtained without traversing all the data.This paper analyzes the security and performance of the scheme in detail,and shows that the scheme can meet the security query requirements of multi-dimensional private data.Comparative experiments are also conducted in a simulated environment.The experimental results show that,compared with the current mainstream models,the performance of the Skyline query scheme for multi-dimensional private data constructed in this paper is better than that of the existing schemes on all data sets.Users provide accurate and efficient query services.Secondly,this paper also constructs a dynamic update scheme for multidimensional privacy data.This solution can dynamically update the data to be queried in real time on the premise of ensuring the privacy of data owners and users,and will not affect the correctness of the original query results.In this solution,when the data to be queried is updated,only part of the data needs to be updated according to the latest query result.After the update is completed,the latest Skyline query result of the updated multi-dimensional privacy data can be directly obtained.This paper analyzes the security and performance of the scheme in detail,and shows that the scheme can meet the dynamic update requirements of multi-dimensional private data.Comparative experiments are also carried out in a simulated environment.The experimental results show that the performance of the dynamic update scheme for multi-dimensional private data constructed in this paper is better than the existing scheme on all data sets,and it can provide users with real-time,fast and accurate data.Update service.