Mining large data requires intensive computing resources and data mining expertise, which might be unavailable for many users. With widely available cloud computing resources, data mining tasks can now be moved to the cloud or outsourced to third parties to save costs. In this new paradigm, data and model confidentiality becomes the major concern to the data owner. Data owners have to understand the potential trade-offs among client-side costs, model quality, and confidentiality to justify outsourcing solutions. In this paper, we propose the RASP-Boost framework to address these problems in confidential cloud-based learning. The RASP-Boost approach works with our previous developed RAndom Space Perturbation (RASP) method to protect data confidentiality and uses the boosting framework to overcome the difficulty of learning high-quality classifiers from RASP perturbed data. We develop several cloudclient collaborative boosting algorithms. These algorithms require low client-side computation and communication costs. The client does not need to stay online in the process of learning models. We have thoroughly studied the confidentiality of data, model, and learning process under a practical security model. Experiments on public datasets show that the RASP-Boost approach can provide high-quality classifiers, while preserving high data and model confidentiality and requiring low client-side costs.

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.

Running MapReduce programs in the cloud introduces this unique problem: how to optimize resource provisioning to minimize the monetary cost or job finish time for a specific job? We study the whole process of MapReduce processing and build up a cost function that explicitly models the relationship among the time cost, the amount of input data, the available system resources (Map and Reduce slots), and the complexity of the Reduce function for the target MapReduce job. The model parameters can be learned from test runs. Based on this cost function, we can solve a number of decision problems, such as the optimal amount of resources that can minimize monetary cost within a job finish deadline, minimize time cost under a certain monetary budget, or find the optimal tradeoffs between time and monetary costs. Experimental results show that the proposed approach performs well on a number of sample MapReduce programs in both the in-house cluster and Amazon EC2. We also conducted a variance analysis on different components of the MapReduce workflow to show the possible sources of modeling error. Our optimization results show that with the proposed approach we can save a significant amount of time and money, compared to randomly selected settings.

Mining large data requires intensive computing resources and data mining expertise, which might not be available for many users. With the development of cloud computing and services computing, data mining tasks can now be moved to the cloud or outsourced to third parties to save costs. In this new paradigm, data and model confidentiality becomes the major concern to the data owner. Meanwhile, users are also concerned about the potential tradeoff among costs, model quality, and confidentiality. In this paper, we propose the PerturBoost framework to address the problems in confidential cloud or outsourced learning. PerturBoost combined with the random space perturbation (RASP) method that was also developed by us can effectively protect data confidentiality, model confidentiality, and model quality with low client-side costs. Based on the boosting framework, we develop a number of base learner algorithms that can learn linear classifiers from the RASP-perturbed data. This approach has been evaluated with public datasets. The result shows that the RASP-based PerturBoost can provide model accuracy very close to the classifiers trained with the original data and the AdaBoost method, with high confidentiality guarantee and acceptable costs.

This poster presents a preliminary study on the PerturBoost approach that aims to provide efficient and secure classifier learning in the cloud with both data and model privacy preserved.

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.

Range query is one of the most frequently used queries for online data analytics. Providing such a query service could be expensive for the data owner. With the development of services computing and cloud computing, it has become possible to outsource large databases to database service providers and let the providers maintain the range-query service. With outsourced services, the data owner can greatly reduce the cost in maintaining computing infrastructure and data-rich applications. However, the service provider, although honestly processing queries, may be curious about the hosted data and received queries. Most existing encryption based approaches require linear scan over the entire database, which is inappropriate for online data analytics on large databases. While a few encryption solutions are more focused on efficiency side, they are vulnerable to attackers equipped with certain prior knowledge. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. We use RASP to generate indexable auxiliary data that is resilient to prior knowledge enhanced attacks. Range queries are securely transformed to the encrypted data space and then efficiently processed with a two-stage processing algorithm. We thoroughly studied the potential attacks on the encrypted data and queries at three different levels of prior knowledge available to an attacker. Experimental results on synthetic and real datasets show that this encryption approach allows efficient processing of range queries with high resilience to attacks.