With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.

Analysis of big data has become an important problem for many business and scientific applications, among which clustering and visualizing clusters in big data raise some unique challenges. This demonstration presents the CloudVista prototype system to address the problems with big data caused by using existing data reduction approaches. It promotes a whole-big-data visualization approach that preserves the details of clustering structure. The prototype system has several merits. (1) Its visualization model is naturally parallel, which guarantees the scalability. (2) The visual frame structure minimizes the data transferred between the cloud and the client. (3) The RandGen algorithm is used to achieve a good balance between interactivity and batch processing. (4) This approach is also designed to minimize the financial cost of interactive exploration in the cloud. The demonstration will highlight the problems with existing approaches and show the advantages of the CloudVista approach. The viewers will have the chance to play with the CloudVista prototype system and compare the visualization results generated with different approaches.