Research And Implementation On Privacy-preserving Outsourced Query Mechanism For Multi-dimension Data

With the rapid development of mobile internet and Internet of Things,people's life equality is improved,and the data produced by daily life is significantly improved.A large amount of life data are transferred to the form of multi-dimension value and centralized stored by data owners.These data can be used to provide some query services for users,and this type of services can offer useful information for user's life decision,science research and so on,so it attracted improving interest recently.Nevertheless,this requires huge storage space and enormous computing resources,which are tremendous burdens on data owner,and the privacy of users' query information are revealed to data owners.With the development of cloud computing,it has become a trend that data owner outsources data to the cloud server,and the cloud server can provide query service according t o users' query request.Aiming at the privacy issue of data owner's data asset and users' query information during multi-dimension data outsourced query service,based on an improved 2DNF homomorphic encryption technology over composite order group,we propose a privacy-preserving framework towards multi-dimension data' outsource query service,and construct privacy-preserving query schemes.Specifically,the two following schemes are proposed.Firstly,we construct a privacy-preserving weighted Euclidean distance query scheme.The proposed scheme can guarantee the privacy of data owner and users,and the cloud server is able to provide Euclidean distance query service for users.Concretely,data owner first encrypt massive data and outsource them to the cloud server,then,the cloud server can provide query service for users,i.e.,compute and return accurate result according to users' encrypted query request.Detailed analysis shows that our scheme can resist various security threats,computational complexity analysis and comparasion shows that our proposed scheme is efficient.Then,we propose a privacy-preserving Skyline query scheme.With the proposed scheme,the cloud server can provide Skyline query service for users.Concretely,data owner's dataare encrypted and uploaded to the cloud server,when users are required to have Skyline query,they generate query request according to their demands,the cloud server traverses through all stored data to get the set of Skyline points.In addition,we analyze the security and computational complexity analysis of our scheme,and we conclude that our proposed scheme can provide secure and privacy-preserving skyline query service.Finally,we implement the weighted Euclidean distance query scheme and Skyline query scheme,and describe the development environment,test environment and implementation process in detail.Then,we analyze test results of data owners,users and cloud servers in both schemes,and evaluates the inntegrated performance in a real environment.Extensive simulation results demonstrate that our proposed schemes are highly efficient in terms of computation and communication costs,can provide accurate and efficient query service for users.