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With the development of sensor network, mobile computing, and webapplications, data are now collected from many distributed sources to form bigdatasets. Such datasets can be hosted in the cloud to achieve economicalprocessing. However, these data might be highly sensitive requiring securestorage and processing. We envision a cloud-based data storage and processingframework that enables users to economically and securely share and handle bigdatasets. Under this framework, we study the matrix-based data miningalgorithms with a focus on the secure top-k eigenvector algorithm. Our approachuses an iterative processing model in which the authorized user interacts withthe cloud to achieve the result. In this process, both the source matrix andthe intermediate results keep confidential and the client-side incurs lowcosts. The security of this approach is guaranteed by using Paillier Encryptionand a random perturbation technique. We carefully analyze its security under acloud-specific threat model. Our experimental results show that the proposedmethod is scalable to big matrices while requiring low client-side costs.

Secure Computation of Top-K Eigenvectors for Shared Matrices in the Cloud