Method for learning parameters of a third convolutional neural network, CNN, for classifying personal data, the personal data being images of faces, the method comprising: - a step consisting, for each of a first and a second server (1a, 1b) each having an independent database of already classified confidential training data, to learn, on the basis of its independent database of already classified confidential training data, the parameters of a first CNN of a plurality of first CNNs for extracting features from personal data, each first CNN corresponding to one of the independent databases of already classified confidential training data; - a step consisting, for each server of the first and second servers (1a, 1b) or for a third server, in learning, on the basis of a database of already classified public training data, the parameters of a final fully connected layer, FC, of a plurality of second CNNs each corresponding to one of the plurality of first CNNs to which said fully connected layer FC has been added, the parameters of the first CNNs being kept, the step being implemented using the same database of already classified public training data so as to learn the parameters of the plurality of second CNNs; - one of the first, second and third servers (1a, 1b, 1c) implementing steps of: each second CNN of the plurality of second CNNs trained at the end of the step independently classifying data of a second database of not yet classified public data, and aggregating the results of the classification; learning, on the basis of the second database of at present classified public training data, the parameters of the third CNN comprising a final fully connected layer, FC. - a step consisting, for each of a first and a second server (1a, 1b) each having an independent database of already classified confidential training data, to learn, on the basis of its independent database of already classified confidential training data, the parameters of a first CNN of a plurality of first CNNs for extracting features from personal data, each first CNN corresponding to one of the independent databases of already classified confidential training data; - a step consisting, for each server of the first and second servers (1a, 1b) or for a third server, in learning, on the basis of a database of already classified public training data, the parameters of a final fully connected layer, FC, of a plurality of second CNNs each corresponding to one of the plurality of first CNNs to which said fully connected layer FC has been added, the parameters of the first CNNs being kept, the step being implemented using the same database of already classified public training data so as to learn the parameters of the plurality of second CNNs; - one of the first, second and third servers (1a, 1b, 1c) implementing steps of: each second CNN of the plurality of second CNNs trained at the end of the step independently classifying data of a second database of not yet classified public data, and aggregating the results of the classification; learning, on the basis of the second database of at present classified public training data, the parameters of the third CNN comprising a final fully connected layer, FC. each second CNN of the plurality of second CNNs trained at the end of the step independently classifying data of a second database of not yet classified public data, and aggregating the results of the classification; learning, on the basis of the second database of at present classified public training data, the parameters of the third CNN comprising a final fully connected layer, FC.