Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004)
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| Venue: | In Proceedings of the 4th SIAM International Conference on Data Mining |
| Citations: | 45 - 1 self |
BibTeX
@INPROCEEDINGS{Du04privacy-preservingmultivariate,
author = {Wenliang Du and Shigang Chen and Yunghsiang S. Han},
title = {Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification},
booktitle = {In Proceedings of the 4th SIAM International Conference on Data Mining},
year = {2004},
pages = {222--233}
}
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Abstract
analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party multivariate statistical analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.
