Abstract
Multivariate statistical analysis is an important data 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.
Original language | English (US) |
---|---|
Pages | 222-233 |
Number of pages | 12 |
DOIs | |
State | Published - 2004 |
Event | Proceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States Duration: Apr 22 2004 → Apr 24 2004 |
Other
Other | Proceedings of the Fourth SIAM International Conference on Data Mining |
---|---|
Country/Territory | United States |
City | Lake Buena Vista, FL |
Period | 4/22/04 → 4/24/04 |
Keywords
- Multivariate statistical analysis
- Privacy
- Secure multi-party computation
- Security
ASJC Scopus subject areas
- General Mathematics