Method for training neural network by using de-identified image and server providing same

An iterative neural network training server using a de-identified image, the server comprising during each iteration of the iterative neural network training method:

• a de-identification unit configured to encode a first image represented by a vector of n-th dimensions into a second image of predetermined p-th dimensions, and then decode the second image into a third image of q-th dimensions, wherein the third image corresponds to the de-identified image of the first image;
• and
• a training unit configured to input the third image to the neural network and extract an object information included in the third image to train at least one parameter information of the neural network and de-identification parameter information by using an error of the extracted object information compared with a ground truth label associated with the first image, and a backpropagated error obtained by first updating the at least one parameter information of the neural network, and then continuing to backpropagate the error of the extracted object information through the decoder and the encoder,
• wherein the error of the extracted object information updates the at least one parameter information in the neural network, and the backpropagated error updates the de-identification parameter information to determine a compression rate of the encoding computation, and
• wherein, during service operation, the first image is deleted, and only the second image and the ground truth label associated with the first image are stored in a database, and a plurality of the stored second images accumulated over a plurality of service operations are bulk-decoded in a batch to generate the third image for further training of the neural network using the third image.