New privacy preserving clustering methods for secure multiparty computation
Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Yohtaro Miyanishi, Shinji Kitagami, Norio Shiratori · Artificial Intelligence Research · 2016
Many researches on privacy preserving data mining have been done. Privacy preserving data mining can be achieved in variousways by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. Further, in order to increase thesecurity of data mining, secure multiparty computation (SMC) has been introduced. Most of works in SMC are developed onapplying the model of SMC on different data distributions such as vertically, horizontally and arbitrarily partitioned data. Anothertype of SMC with sharing data itself to each party attracts attention, and some studies have been done. A simple method to sharedata was proposed and it was applied to statistical computation. However, for SMC, complicated computation such as data mininghas never been proposed. In the previous paper, we proposed a BP learning for SMC and showed the effectiveness of it. In thispaper, we propose clustering methods such as k-means and NG for SMC and show the effectiveness in numerical simulation.