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Content Provider | IEEE Xplore Digital Library |
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Author | Shuguo Han Wee Keong Ng Yu, P.S. |
Copyright Year | 2009 |
Abstract | In this paper, we propose secure protocols to perform Singular Value Decomposition (SVD) for two parties over horizontally and vertically partitioned data. We propose various secure building blocks for the computations of QR algorithm so that it is privacy-preserving. Some of the proposed secure building blocks include Secure Matrix Multiplication, $(x+y)^{-1}$, and $\sqrt{x+y}$. Together, they allow us to derive Privacy-Preserving SVD (PPSVD) based on a privacy-preserving QR algorithm. Finally we conduct experiments to evaluate the proposed secure building blocks and protocols. The results show that the proposed protocols for SVD achieve high accuracy for matrices of small and medium size. |
Starting Page | 1267 |
Ending Page | 1270 |
File Size | 235120 |
Page Count | 4 |
File Format | |
ISBN | 9781424434220 |
ISSN | 10844627 |
DOI | 10.1109/ICDE.2009.217 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-03-29 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Singular value decomposition Matrix decomposition Covariance matrix Eigenvalues and eigenfunctions Cryptography Data mining Data engineering Partitioning algorithms Cryptographic protocols Symmetric matrices Secure Building Blocks Privacy Singular Value Decomposition SVD QR algorithm |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Information Systems Software |
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