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Big graphs, such as user interactions in social networks and customer rating matrices in collaborative filters, possess great values for both businesses and research. They are not only big but often keep evolving, which requires a large amount of computing resources to maintain. With the wide deployment of public cloud resources, owners of big graphs may want to use cloud resources to obtain storage and computation scalability. However, privacy and ownership of the graphs in the cloud has become a major concern. In this paper, we study privacy-preserving algorithms for one of the important graph analysis techniques—graph spectral analysis for outsourced graph in the cloud. The core operation: eigendecomposition of large matrix is also important to many data mining algorithms. We consider a cloud-centric framework with three collaborative parties: data contributors, data owner, and cloud provider. Graphs are represented as matrices such as adjacency matrix and Laplacian matrix, the elements of which are encrypted and submitted by distributed contributors. The data owner then interacts with the cloud-side programs to conduct spectral analysis, while protecting data privacy from the honest-but-curious cloud provider. For a $N\times N$N×N graph matrix, we aim to design algorithms with the cloud handling expensive storage and computation in $O(N^2)$O(N2) complexity, while data owner and data contributors' algorithms take only $O(N)$O(N). To achieve this goal, we develop the privacy-preserving versions of the two approximate eigendecomposition algorithms: the Lanczos algorithm and the Nyström algorithm, considering two encryption methods: additive homomorphic encryption (AHE) methods and somewhat homomorphic encryption (SHE) methods. Both dense and sparse matrices are studied, while sparse matrices also involve a differentially private data submission protocol to allow the trade-off between data sparsity and privacy. Experimental results show that the Nyströ algorithm with sparse encoding can dramatically reduce data owners' costs. SHE-based methods have lower computational time while AHE-based methods have lower communication/storage costs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In multiparty collaborative data mining, participants contribute their own data sets and hope to collaboratively mine a comprehensive model based on the pooled data set. How to efficiently mine a quality model without breaching each party's privacy is the major challenge. In this paper, we propose an approach based on geometric data perturbation and data mining service-oriented framework. The key problem of applying geometric data perturbation in multiparty collaborative mining is to securely unify multiple geometric perturbations that are preferred by different parties, respectively. We have developed three protocols for perturbation unification. Our approach has three unique features compared to the existing approaches: 1) with geometric data perturbation, these protocols can work for many existing popular data mining algorithms, while most of other approaches are only designed for a particular mining algorithm; 2) both the two major factors: data utility and privacy guarantee are well preserved, compared to other perturbation- based approaches; and 3) two of the three proposed protocols also have great scalability in terms of the number of participants, while many existing cryptographic approaches consider only two or a few more participants. We also study different features of the three protocols and show the advantages of different protocols in experiments. [ABSTRACT FROM AUTHOR]