Document Type
Working Paper
Date
2004
Keywords
privacy, security, multivariate statistical analysis, secure multi-party computation
Language
English
Disciplines
Computer Sciences
Description/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.
Recommended Citation
Du, Wenliang; Han, Yunghsiang S.; and Chen, Shigang, "Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification" (2004). Electrical Engineering and Computer Science - All Scholarship. 12.
https://surface.syr.edu/eecs/12
