title: Privacy-preserving Kruskal-Wallis test.
abstract: Statistical tests are powerful tools for data analysis. Kruskal-Wallis test is a non-parametric statistical test that evaluates whether two or more samples are drawn from the same distribution. It is commonly used in various areas. But sometimes, the use of the method is impeded by privacy issues raised in fields such as biomedical research and clinical data analysis because of the confidential information contained in the data. In this work, we give a privacy-preserving solution for the Kruskal-Wallis test which enables two or more parties to coordinately perform the test on the union of their data without compromising their data privacy. To the best of our knowledge, this is the first work that solves the privacy issues in the use of the Kruskal-Wallis test on distributed data.
authors.affiliation: Department of Computer Science and Engineering, SUNY at Buffalo, United States.
authors.forename: Suxin
authors.initials: S
authors.lastname: Guo
authors.forename: Sheng
authors.initials: S
authors.lastname: Zhong
authors.forename: Aidong
authors.initials: A
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003195:Computer Communication Networks; D003219:Confidentiality; D003627:Data Interpretation, Statistical; D057225:Data Mining; D006801:Humans; D018709:Statistics, Nonparametric
keywords: Data security; Kruskal–Wallis test; Statistical test