Secure Multiparty Computation of Chi-Square Test Statistics and Contingency Coefficients
Sun-Kyung Hong, Hajin Kim, Sanghun Lee, Yang‐Sae Moon · 2017
Generally, in order to perform data mining, all the original data should be provided to the third party first. However, in case of privacy-preserving data mining, the data provider may not want to disclose sensitive data directly to the third party. Therefore, it is very important to compute chi-square test statistics and contingency coefficients, which are statistically very useful, while not disclosing original sensitive data. In this paper, we propose a novel solution to securely compute chi-square test statistics and contingency coefficients by using secure scalar products and proposing secure bitmap string operations. We prove the correctness and secureness of the proposed solution by presenting a formal theorem. Also, we empirically show the superiority and practicality of the proposed method.