Privacy-Preserving Sphere Data Outsourced Query Search In Cloud

With the popularity of cloud computing in recent decades,many Internet giants manage to deploy cloud infrastruction themselves and to provide convenient,fast and efficient service for developers.Developers(Data Owner)need to store their data in the cloud.Many of these also need to store spatial data into cloud services,such as Gao De map,Bai Du map and Square.However,the geographic information data will be exposed to the untrusted cloud server.Therefore,the research of secure outsourcing spatial database has become a hotspot.Several schemes that protect user location information have been proposed.However,a large number of works about the hotspot do not involve how to securely update data after securely outsourcing to cloud.In the other hand,these works are all based on two-dimensional space.What's more,just implementing the privacy protection of outsourced data cannot be applied to practical applications,because any user that have the secret key of data objects can access all the spatial data.It is relatively easy to solve this problem in plain-text,that is,store access control table(ACL)to cloud to record the corresponding data permissions of each user,but ACL table with plain-text cannot be stored in untrusted cloud.Based on the above reasons,we need to solve the following two problems:(1)In a single-user role scenario,how to protect the privacy of the data stored in the cloud server and achieve correctly and efficiently retrieval and update of the data in the three-dimensional spherical data?(2)How to achieve secure access control under the scenario of multiple user roles?The work in this thesis focuses on the above two problems:For the first problem,we consider the 3D spherical data with immense application prospects in the geometric information system(GIS),and investigate its privacy-preserving query problem.By using an approximately distance-preserving 3D-2D projection method,we firstly project 3D spatial points to six 2D planes.Then we utilize Hilbert space-filling curve to encode the 2D points into 1D Hilbert values.After that,we build an encrypted spatial index tree using B~+-tree and order-preserving encryption(OPE).Finally,we send encrypted spatial index and corresponding encrypted data objects using symmetric encryption technology(AES)to cloud.Our scheme supports efficient point query,arbitrary polygon query,as well as dynamic updating in the encrypted domain.We also analyze the security of the solution and perform a comparative experiment on the real data set to reflect the advantages of our scheme.Theoretical analysis and experimental results on real-word datasets demonstrate its satisfactory tradeoff between security and efficiencyIn fact,the second problem is also a secure outsourcing problem,so this thesis is based on a typical data flow model in outsourcing system,and scrambling ACL table with Bloom Function.We use the matrix with pseudo inverse nature as the secret key to encrypt the ACL table that had been scrambled,then we get an enable-indexing ACL table and outsource it to cloud,so it can achieve secure access control.After that,we analyze the performance,error rate and security of the scheme,and propose a scheme with zero error rate.In the end,we perform experiments to discuss the problem of optimal parameters which are well-balanced in dealing with the contradiction between security and efficient under different scenarios.