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 m

Abstract: The present invention is concerned with a control device for medium flows for a regenerative heat exchanger. The device consists of a cell (7) divided into compartments (8) and acting as a heat exchanger, and at the ends of the cell, there are control means (11, 12) for guiding the medium flows alternatingly into the different compartments (8) of the cell (7). In prior art, unreasonably high capacities of actuating devices have been required for controlling the flows of media, and the control devices have increased the length of the heat exchanger considerably. These drawbacks are avoided if the control means consist of a slide (11, 12) displaceable transversely in relation to the partition walls (9) of the cell, the said slide (11, 12) being provided with openings (13) and with blocking members (14) between the openings for the purpose of alternatingly opening and closing connections between medium flow ducts (2, 3, 5, 6) and compartments (8) in the cell.