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Presentation
Secure Computation of Top-K Eigenvectors for Shared Matrices in the Cloud
Proceedings of the Sixth IEEE International Conference on Cloud Computing
  • James L. Powers, Wright State University - Main Campus
  • Keke Chen, Wright State University - Main Campus
Document Type
Conference Proceeding
Publication Date
1-1-2013
Abstract
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.
Comments

Presented at the Sixth IEEE International Conference on Cloud Computing, Santa Clara, CA, June 27-July 2, 2013.

DOI
10.1109/CLOUD.2013.122
Citation Information
James L. Powers and Keke Chen. "Secure Computation of Top-K Eigenvectors for Shared Matrices in the Cloud" Proceedings of the Sixth IEEE International Conference on Cloud Computing (2013) p. 155 - 162 ISSN: 9780769550282
Available at: http://works.bepress.com/keke_chen/42/