A method for concealing original data to protect personal information, comprising steps of: (a1) acquiring an original face image; (a2) by a data obfuscation device , inputting the original image (X) or a modified image, generated from the original image, into a learning network , and allowing the learning network to: (i) apply a network operation to the original image or the modified image using one or more learned parameters of the learning network and thus to (ii) output characteristic information on the original image (X) or the modified image; and (b) the data obfuscation device updating the original image (X) or the modified image via backpropagation using at least part of (i) one or more 1-st losses calculated by referring to the characteristic information and its corresponding at least one 1-st ground truth, and (ii) one or more 2-nd losses calculated by referring to (ii-1) at least one task specific output generated by using the characteristic information and (ii-2) at least one 2-nd ground truth corresponding to the task specific output, wherein the task specific output comprises a probability of a class for classification and/or coordinates resulting from regression for location detection, and wherein the characteristic information comprises features or logits of the original image or feature values related to features in the original image, or the logits including values related to at least one of vectors, matrices, and coordinates related to the certain features, to thereby generate an obfuscated image corresponding to the original image. (a1) acquiring an original face image; (a2) by a data obfuscation device , inputting the original image (X) or a modified image, generated from the original image, into a learning network , and allowing the learning network to: (i) apply a network operation to the original image or the modified image using one or more learned parameters of the learning network and thus to (ii) output characteristic information on the original image (X) or the modified image; and (i) apply a network operation to the original image or the modified image using one or more learned parameters of the learning network and thus to (ii) output characteristic information on the original image (X) or the modified image; and (b) the data obfuscation device updating the original image (X) or the modified image via backpropagation using at least part of (i) one or more 1-st losses calculated by referring to the characteristic information and its corresponding at least one 1-st ground truth, and (ii) one or more 2-nd losses calculated by referring to (ii-1) at least one task specific output generated by using the characteristic information and (ii-2) at least one 2-nd ground truth corresponding to the task specific output, (i) one or more 1-st losses calculated by referring to the characteristic information and its corresponding at least one 1-st ground truth, and (ii) one or more 2-nd losses calculated by referring to (ii-1) at least one task specific output generated by using the characteristic information and (ii-2) at least one 2-nd ground truth corresponding to the task specific output, (ii-1) at least one task specific output generated by using the characteristic information and (ii-2) at least one 2-nd ground truth corresponding to the task specific output, wherein the task specific output comprises a probability of a class for classification and/or coordinates resulting from regression for location detection, and wherein the characteristic information comprises features or logits of the original image or feature values related to features in the original image, or the logits including values related to at least one of vectors, matrices, and coordinates related to the certain features, to thereby generate an obfuscated image corresponding to the original image. <EOS>