TY  - JOUR
T1  - Secure Multi-Party Computation of Correlation Coefficients
AU  - Hong, Sun Kyong 
AU  - Kim, Sang Pil 
AU  - Lim, Hyo Sang 
AU  - Moon, Yang Sae 
JO  - Journal of KIISE, JOK
PY  - 2014
DA  - 2014/9/14
DO  - 
KW  - secure multi party computation
KW  - privacy preserving data mining
KW  - pearson correlation coefficients
KW  - rank correlation coefficients
KW  - secure correlation computation
AB  - In this paper, we address the problem of computing Pearson correlation coefficients and Spearman’s rank correlation coefficients in a secure manner while data providers preserve privacy of their own data in distributed environment. For a data mining or data analysis in the distributed environment, data providers(data owners) need to share their original data with each other. However, the original data may often contain very sensitive information, and thus, data providers do not prefer to disclose their original data for preserving privacy. In this paper, we formally define the secure correlation computation, SCC in short, as the problem of computing correlation coefficients in the distributed computing environment while preserving the data privacy (i.e., not disclosing the sensitive data) of multiple data providers. We then present SCC solutions for Pearson and Spearman’s correlation coefficients using secure scalar product. We show the correctness and secure property of the proposed solutions by presenting theorems and proving them formally. We also empirically show that the proposed solutions can be used for practical applications in the performance aspect.