Research And Application Of Privacy-Preserving Query On Multidimensional Data For Cloud Storage

In Cloud Storage, data owner uploads its encrypted data and index to Cloud Service Provider(CSP), and CSP indexes encrypted data when user wants to query the data in CSP, and then send query results to the user. This method can provide confidentiality for data. However, due to semi-trusted and curious CSP, it could be privacy disclosures which are caused by a lot requests for data queries. So an excellent query method must provide efficient query processing, and it also can prevent the privacy of data and query from disclosure. Multidimensional data is a kind of data which is important and universal, and it is popular in academia to research the query processing for multidimensional data. This paper makes the following improvements of the algorithm for privacy-preserving multidimensional query on real-time data and scenario of multi-servers:1.To resolve the privacy-preserving outsource problem for real-time data, we propose a scheme for realizing privacy-preserving multidimensional range query on real-time data. In trivial scheme, data owner encrypts, generates index and upload the data that are collected by data collectors. In this paper, we divide time into N epochs and adopt key-insulated technology which supports periodical key update to bucketization method. Our scheme radically reduces the cost of data owner in the sense that data collectors undertake most of the works including collecting data, generating index and uploading data which are responsible by data owner in trivial method. Additional, in our method, keys of each epoch can be calculated from keys of the previous epoch, and key distribution of data owner is simple for the cycle of key distribution are N epochs.Moreover, we realize the integrity verification of query results which can verify whither semi-trusted CSP return correct query results or not.2.Base on the above method, we also propose an improved scheme. Comparing with above scheme, our improved scheme adopt key-evolving technology which supports periodical key update to bucketization method, and the key update work in this scheme does not require an extra device. As for experiments, comparing with above scheme, the cost of data outsourcing in this scheme is increased, but the cost of query processing in this scheme is decreased.3.To resolve the index in encrypted data of multi-servers, we propose a scheme forrealizing privacy-preserving multidimensional range query on multi-servers. We propose a bucketization method which is suitable for data outsourcing on multi-servers.The data processing in this method can ensure aggregation and uniformity for storing data, which guarantee efficient query processing. And it also resolve the load unbalancing problem and reduce the number of false positive in query results which are caused by uneven-storing data. Moreover, this method import a trusted extra device that is responsible for data index, which radically reduces the cost of cloud side and the possibility of information disclosure.