Abstract
Cyber-physical-social big data concern heterogeneous, multiaspect, large-volume data generated in cyber-physical-social systems (CPSS). Orthogonal tensor SVD (OTSVD) has emerged as a powerful tool to reduce cyber-physical-social big data. In this work, we propose an improved secure high-order-Lanczos based OTSVD for cyber-physical-social big data reduction in clouds. Specifically, to take advantage of the parallel processing capability of cloud computing, the improved secure high-order Lanczos algorithm is derived by restructuring the original high-order Lanczos algorithm such that only one synchronization point per iteration is required. To protect data privacy, the improved secure high-order-Lanczos based OTSVD employs homomorphic encryption integrated with batching technique, and garbled circuits, and makes all computations of the OTSVD algorithm in clouds come true. To our knowledge, this is the first study to efficiently tackle big data reduction in clouds in a privacy-preserving manner. Finally, we prove that our improved approach is secure in semi-trusted model. And we evaluate the proposed improved secure OTSVD on real datasets. The results show that our proposed improved secure approach is efficient and scalable for cyber-physical-social big data reduction.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.