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