Compatible anonymization of data sets of different sources

Abstract

Creating compatible anonymized data sets by performing with machine learning equipment that operates a machine learning model by defining data types of variables of a data set; identifying quasi-identifiers for the data set; defining reidentification sensitivity of all or any targeted subset of the individual variables and quasi-identifiers; defining missing data handling rules for the individual variables; defining allowed data transformations including generalization and use of synthesized data; optimizing quasi-identifier selection, use of synthesized data and a choice of data transformations to minimize information loss and maximize privacy metrics based on the data set; the allowed data transformations; and the missing data handling rules; training the machine learning model using the data set according to the defined data types; the optimized quasi-identifier selection; the optimized use of synthesized data; and the choice of data transformations; and anonymizing the data set using the training of the machine learning model.