System and a method for performing a privacy-preserving distribution similarity tests between a plurality of datasets

Abstract

Method and servers for performing a privacy-preserving distribution similarity test between first and second data being available to a first and a second client respectively. The method includes generating a target upper boundary value, a target lower boundary value, and a target step size based on first and second boundary values and step-sizes received from the first and second clients receptively, generating non-sensitive testing values, causing generation of first and second Empirical Cumulative Distribution Function (eCDF) values by the first and second client respectively based on the non-sensitive testing values and on the first and second data respectively, acquiring first encrypted eCDF values from the first client, providing encrypted statistic data to the first client, acquiring a result of a privacy-preserving distribution similarity test from the first client, and identifying the first client and the second client as suitable participants for the federated learning task.