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| Title | a Privacy-Preserving Method for Distributed Regularized Least Squares (in Japanese) |
|---|---|
| Authors | Tota Suko 、Shunsuke Horii 、Manabu Kobayashi |
| Released Year | 2014 |
| Format | Conference |
| Category | Knowledge information processing |
| Jounal Name | Proceedings of the 37th Symposium on Information Theory and its Applications |
| Jounal Page | pp.300-305 |
| Published Year | 2014 |
| Published Month | 12 |
| Abstract （English） |
In this paper, we study a privacy-preserving linear regression analysis. We consider the situation that a number of users have different data. They don’t want to show their data each other, but they want to calculate a regularized least squares estimator using all users data. We propose a protocol of distributed calculation method that 2 parties calculate a regularized least squares estimator. We show security of privacy in the proposed protocol. |
| Note （English） |
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| Manuscript | |
| Presentation |
