Algorithm Design For One-dimensional And Spatial Encrypted Data Range Query

With the rapid development of cloud computing and the popularity of cloud storage servers,users began to outsource local databases to cloud storage servers,thereby saving local storage overhead and avoiding cumbersome local data management and maintenance.Because cloud service providers are not completely reliable,outsourcing can pose a security risk to sensitive data of data owners and the privacy of data users.In order to ensure data security and personal privacy,data must be encrypted before being outsourced to the cloud server,and the encrypted data greatly reduces the original availability of the data,making it difficult for the cloud server to perform data query.Therefore,under the premise of ensuring the security of data outsourcing and the privacy of users,how to make full use of the powerful computing and storage capabilities of cloud servers to complete the efficient search of encrypted data is a problem worthy of further study.The data query problem in the outsourced database includes many aspects.This paper focuses on how to perform a fast range search on the encrypted outsourced data,that is,when the trusted data owner encrypts the data and outsources it to the cloud server,the cloud server can be accurately searched for data falling within the scope of the query given by the user without decrypting the encrypted data and not knowing the scope of the query.Based on the systematic research on data outsourcing security and user privacy,this thesis conducts in-depth research on the problem of encrypted data range search,and proposes different algorithms to deal with one-dimensional and multi-dimensional data range search problems.A range query for one-dimensional numeric data generally refers to a query for an attribute column value in the database.Based on the security index,security encryption algorithm and Bloom filter technology,the algorithm for supporting range query of onedimensional data proposed in this thesis can make the cloud server complete accurate and effective range search function,and ensure data security and user privacy.The algorithm does not need to perform complex ciphertext string matching operations,and does not need to worry about the security risks caused by the cloud server that is not completely trusted.The computing power of the cloud server can be fully utilized,and the algorithm can be applied in many practical scenarios.Through security analysis and simulation experiments,the algorithm has high security and search efficiency.Range queries for spatial data are generally based on location services(LBS)for a given query range of geographic location data.Location-based services can provide realtime perimeter search services based on specific query conditions provided by mobile terminal users.In traditional location-based services,cloud service providers need to maintain geodatabases locally(Geo-DB).)to get the real-time location of the mobile terminal user,but this also leads to the user's personal privacy is easily leaked.Aiming at this problem,based on the quad-coded index tree and Bloom filter technology,this thesis proposes a location-based range-based search algorithm with privacy protection.In this algorithm,the data owner uses the encrypted coded quadtree(ECQtree)to build a secure index for the data objects in the Geo-DB,enabling the user to efficiently query on the data that can be encrypted;at the same time,using Bloom filtering The method of mapping the user's query range to the Bloom filter generates a query trapdoor(BF-vector),which makes the user's location information hidden from the cloud server.The algorithm implements a range query for outsourced spatial data and can effectively protect user location privacy.The simulation results show that the proposed algorithm can effectively shorten the range query time of spatial data and reduce the computational cost of data owners and users.It is of great significance for the practical application based on LBS range search.