CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to Korean Provisional Patent Application No. 10-2020-0137124, filed on Oct. 21, 2020, the entire contents of which are incorporated herein by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates to a method for training a user learning network; and more particularly, to the method for training the user learning network to be used for recognizing obfuscated data created by obfuscating, e.g., anonymizing or concealing, original data to protect personal information and a user learning device using the same, and to a method for testing a trained user learning network and a testing device using the same.

BACKGROUND OF THE DISCLOSURE

Big data refers to data including all of unstructured data and semi-structured data not utilized so far, like e-commerce data, metadata, web log data, radio frequency identification (RFID) data, sensor network data, social network data, data of Internet text and documents, Internet search indexing data, as well as all of structured data used by conventional enterprises or public institutions. Data as such is referred to as the big data in the sense that common software tools and computer systems cannot easily handle such a huge volume of data.

And, although such a big data may have no meaning by itself, it can be useful for generation of new data, judgment or prediction in various fields through machine learning on patterns and the like.

Recently, due to the strengthening of a personal information protection act, it is required to delete information, that can be used for identifying individuals, from the data or to acquire consent of the individuals in order to trade or share such a big data. However, it is not easy to check if any information that can be used for identifying the individuals is present in such a large amount of the big data, and it is impossible to obtain the consent of every individual. Therefore, various techniques for such purposes have emerged.

As an example of a related prior art, a technique is disclosed in Korean Patent Registration No. 10-1861520. According to this technique, a face-concealing method is provided which includes a detection step of detecting a facial region of a person in an input image to be transformed, a first concealing step of transforming the detected facial region into a distorted first image that does not have a facial shape of the person so that the person in the input image is prevented from being identified, and a second concealing step of generating a second image having a predetermined facial shape based on the first image, transforming the first image into the second image, in the input image, where the second image is generated to have a facial shape different from that of the facial region detected in the detection step.

However, according to conventional techniques as well as the technique described above, it is determined whether identification information such as faces, text, etc. is included in the data, and then a portion corresponding to the identification information is masked or blurred. As a result, a machine learning algorithm cannot utilize such data due to distortion of original data. Also, in some cases, the data may contain unexpected identification information which cannot be concealed, e.g., anonymized. In particular, a conventional security camera performs an anonymizing process by blurring every pixel changed between frames due to a target to be anonymized moving between the frames in a video, and if the anonymizing process is performed in this manner, critical information such as facial expression of an anonymized face becomes different from information contained in an original video, and also, personal identification information overlooked during face detection may remain on the original video.

Therefore, a data obfuscation technique for generating obfuscated data by obfuscating the original data such that a result of inputting the original data into a learning model and a result of inputting the obfuscated data into the learning model are same as or similar to each other is in development.

However, if a user learning network is trained by using the obfuscated data in order to recognize such obfuscated data, the user learning network trained with the obfuscated data may not be able to recognize the original data.

In addition, it is not efficient to train the user learning network by using the original data and the obfuscated data at the same time in case the original data with a large amount have been accumulated.

SUMMARY OF THE DISCLOSURE

It is an object of the present disclosure to solve all the aforementioned problems.

It is another object of the present disclosure to allow inference on original data by a user learning network trained with obfuscated data, e.g., anonymized data or concealed data.

It is still another object of the present disclosure to train the user learning network by using both of the obfuscated data and original data.

In order to accomplish the objects above, distinctive structures of the present disclosure are described as follows.

In accordance with one aspect of the present disclosure, there is provided a method for training a user learning network to be used for recognizing obfuscated data, created by obfuscating original data through an obfuscation network having been trained to obfuscate the original data to generate the obfuscated data such that a trained provider learning network recognizes the obfuscated data as same as the original data and such that the original data is indistinguishable from the obfuscated data, to protect personal information, including steps of: (a) a user learning device performing or supporting another device to perform (i) a process of acquiring (1_l)-st obfuscated data for training to (1_m)-th obfuscated data for training from a data provider, wherein an obfuscation network generates the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training respectively by obfuscating (1_l)-st original data for training to (1_m)-th original data for training and wherein m is an integer equal to or greater than 1, (ii) a process of inputting (1_i)-th obfuscated data for training, among the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training, into a user learning network, to thereby allow the user learning network to apply a learning operation to the (1_i)-th obfuscated data for training and thus to generate first characteristic information for training, via at least one user task layer, to be used for performing a task of the user learning network, and at least one first user batch normalizing layer which adjusts a first average and a first variance of first outputs from the at least one user task layer, and (iii) a process of updating task parameters of the at least one user task layer and first batch parameters of the at least one first user batch normalizing layer such that a first error, calculated by referring to (1) the first characteristic information for training or a first task specific output for training created by using the first characteristic information for training and (2) a first ground truth of the (1_i)-th obfuscated data for training, is minimized, to thereby train the user learning network; and (b) the user learning device performing or supporting another device to perform (i) a process of acquiring (2_l)-st original data for training to (2_n)-th original data for training from a user, wherein n is an integer equal to or greater than 1, (ii) a process of inputting (2_j)-th original data for training, among the (2_l)-st original data for training to the (2_n)-th original data for training, into the user learning network, to thereby allow the user learning network to apply the learning operation to the (2_j)-th original data for training and thus to generate second characteristic information for training, via the at least one user task layer and at least one second user batch normalizing layer which adjusts a second average and a second variance of second outputs from the at least one user task layer, and (iii) a process of updating the task parameters of the at least one user task layer and second batch parameters of the at least one second user batch normalizing layer such that a second error, calculated by referring to (1) the second characteristic information for training or a second task specific output for training created by using the second characteristic information for training and (2) a second ground truth of the (2_j)-th original data for training, is minimized, to thereby train the user learning network.

As one example, the method further includes a step of: (c) the user learning device performing or supporting another device to perform (i) a process of acquiring (3_l)-st original data for training to (3_o)-th original data for training from the data provider, wherein o is an integer equal to or greater than 1 and equal to or less than m and wherein the (3_l)-st original data for training to the (3_o)-th original data for training are at least part of the (1_l)-st original data for training to the (1_m)-th original data for training, (ii) a process of inputting (3_k)-th original data for training, among the (3_l)-st original data for training to the (3_o)-th original data for training into the user learning network, to thereby allow the user learning network to apply the learning operation to the (3_k)-th original data for training and thus to generate third characteristic information for training, via the at least one user task layer and the at least one second user batch normalizing layer, and (iii) a process of updating the task parameters of the at least one user task layer and the second batch parameters of the at least one second user batch normalizing layer such that a third error, calculated by referring to (1) the third characteristic information for training or a third task specific output for training created by using the third characteristic information for training and (2) a third ground truth of the (3_k)-th original data for training, is minimized, to thereby train the user learning network.

As one example, the method further includes a step of (d) the user learning device performing or supporting another device to perform (i) a process of acquiring (4_l)-st original data for training to (4_p)-th original data for training from the user, wherein p is an integer equal to or greater than 1, (ii) a process of inputting (4_s)-th original data for training, among the (4_l)-st original data for training to the (4_p)-th original data for training, into the user learning network, to thereby allow the user learning network to apply the learning operation to the (4_s)-th original data for training and thus to generate fourth characteristic information for training, via the at least one user task layer and the at least one second user batch normalizing layer, (iii) a process of inputting the (4_s)-th original data for training into the obfuscation network, to thereby allow the obfuscation network to obfuscate the (4_s)-th original data for training and thus to generate (4_s)-th obfuscated data for training, (iv) a process of inputting the (4_s)-th obfuscated data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the (4_s)-th obfuscated data for training and thus to generate fifth characteristic information for training, and (v) a process of updating the task parameters of the at least one user task layer and the second batch parameters of the at least one second user batch normalizing layer such that a fourth error, calculated by referring to the (v-1) the fourth characteristic information for training and the fifth characteristic information for training or (v-2) (v-2-a) a fourth task specific output for training created by using the fourth characteristic information for training and (v-2-b) a fifth task specific output for training created by using the fifth characteristic information for training, is minimized, to thereby train the user learning network.

As one example, the (4_l)-st original data for training to the (4_p)-th original data for training are unlabeled data whose ground truths are not labeled, and wherein the user learning device uses or supports another device to use the fifth characteristic information for training or the fifth task specific output for training as pseudo ground truths of the (4_s)-th original data for training which are at least part of the unlabeled data.

As one example, a provider learning device has trained the obfuscation network by performing or supporting another device to perform (i) a process of inputting sixth original data for training into the obfuscation network, to thereby allow the obfuscation network to obfuscate the sixth original data for training and thus to generate sixth obfuscated data for training, (ii) (ii-1) a process of inputting the sixth obfuscated data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the sixth obfuscated data for training and thus to generate sixth characteristic information for training and (ii-2) a process of inputting the sixth original data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the sixth original data for training and thus to generate seventh characteristic information for training, and (iii) a process of training the obfuscation network such that a sixth error, calculated by referring to the sixth characteristic information for training and the seventh characteristic information for training or by referring to a sixth task specific output for training created by using the sixth characteristic information for training, is minimized and such that a seventh error, calculated by referring to the sixth original data for training and the sixth obfuscated data for training, is maximized.

As one example, the trained provider learning network includes a first trained provider learning network to a u-th trained provider learning network performing respective provider tasks different from one another and wherein u is an integer equal to or greater than 1, and wherein the provider learning device performs or supports another device to perform a process of training the obfuscation network by using an average error created by averaging provider network errors respectively corresponding to the first trained provider learning network to the u-th trained provider learning network or a process of training the obfuscation network by sequentially using the first trained provider learning network to the u-th trained provider learning network.

As one example, a provider learning device has trained the obfuscation network by performing or supporting another device to perform (i) a process of inputting eighth original data for training into the obfuscation network, to thereby allow the obfuscation network to obfuscate the eighth original data for training and thus to generate eighth obfuscated data for training, (ii) (ii-1) a process of inputting the eighth obfuscated data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the eighth obfuscated data for training and thus to generate eighth characteristic information for training, via at least one provider task layer and at least one third provider batch normalizing layer which adjusts a third average and a third variance of third outputs from the at least one provider task layer and (ii-2) a process of inputting the eighth original data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the eighth original data for training and thus to generate ninth characteristic information for training, via the at least one provider task layer and at least one fourth provider batch normalizing layer which adjusts a fourth average and a fourth variance of fourth outputs from the at least one provider task layer, (iii) a process of training the obfuscation network such that at least one of (iii-1) an eighth error, calculated by referring to the eighth characteristic information for training and the ninth characteristic information for training, and (iii-2) a ninth error, calculated by referring to an eighth task specific output for training created by using the eighth characteristic information for training, is minimized and such that a tenth error, calculated by referring to the eighth original data for training and the eighth obfuscated data for training, is maximized.

As one example, the user learning network includes a first user learning network to a t-th user learning network performing respective tasks different from one another and wherein t is an integer equal to or greater than 1, and wherein the user learning device performs or supports another device to perform a process of training the first user learning network to the t-th user learning network such that user network errors respectively corresponding to the first user learning network to the t-th user learning network are minimized or a process of training the first user learning network to the t-th user learning network such that an average error created by averaging the user network errors is minimized.

In accordance with another aspect of the present disclosure, there is provided a method for testing a user learning network trained to recognize obfuscated data, created by obfuscating original data through an obfuscation network having been trained to obfuscate the original data such that a trained provider learning network recognizes the obfuscated data as same as the original data and such that the original data is indistinguishable from the obfuscated data, to protect personal information, including steps of: (a) on condition that a user learning device has trained a user learning network by performing or supporting another device to perform (i) a process of acquiring (1_l)-st obfuscated data for training to (1_m)-th obfuscated data for training from a data provider, wherein an obfuscation network generates the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training respectively by obfuscating (1_l)-st original data for training to (1_m)-th original data for training and wherein m is an integer equal to or greater than 1, (ii) a process of inputting (1_i)-th obfuscated data for training, among the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training, into the user learning network, to thereby allow the user learning network to apply a learning operation to the (1_i)-th obfuscated data for training and thus to generate first characteristic information for training, via at least one user task layer, to be used for performing tasks of the user learning network, and at least one first user batch normalizing layer which adjusts a first average and a first variance of first outputs from the at least one user task layer, (iii) a process of updating task parameters of the at least one user task layer and first batch parameters of the at least one first user batch normalizing layer such that a first error, calculated by referring to (1) the first characteristic information for training or a first task specific output for training created by using the first characteristic information for training and (2) a first ground truth of the (1_i)-th obfuscated data for training, is minimized, (iv) a process of acquiring (2_l)-st original data for training to (2_n)-th original data for training from a user, wherein n is an integer equal to or greater than 1, (v) a process of inputting (2_j)-th original data for training, among the (2_l)-st original data for training to the (2_n)-th original data for training, into the user learning network, to thereby allow the user learning network to apply the learning operation to the (2_j)-th original data for training and thus to generate second characteristic information for training, via the at least one user task layer and at least one second user batch normalizing layer which adjusts a second average and a second variance of second outputs from the at least one user task layer, and (vi) a process of updating the task parameters of the at least one user task layer and second batch parameters of the at least one second user batch normalizing layer such that a second error, calculated by referring to (1) the second characteristic information for training or a second task specific output for training created by using the second characteristic information for training and (2) a second ground truth of the (2_j)-th original data for training, is minimized, to thereby train the user learning network, a testing device performing or supporting another device to perform a process of acquiring test data; and (b) the testing device performing or supporting another device to perform (i) if the test data is determined as original data for testing, a process of inputting the test data into the user learning network, to thereby allow the user learning network to apply the learning operation to the test data and thus to generate first characteristic information for testing, via the at least one user task layer and the at least one second user batch normalizing layer and (ii) if the test data is determine d as obfuscated data for testing, a process of inputting the test data into the user learning network, to thereby allow the user learning network to apply the learning operation to the test data and thus to generate second characteristic information for testing, via the at least one user task layer and the at least one first user batch normalizing layer.

In accordance with still another aspect of the present disclosure, there is provided a user learning device for training a user learning network to be used for recognizing obfuscated data, created by obfuscating original data through an obfuscation network having been trained to obfuscate the original data to generate the obfuscated data such that a trained provider learning network recognizes the obfuscated data as same as the original data and such that the original data is indistinguishable from the obfuscated data, to protect personal information, including: at least one memory that stores instructions; and at least one processor configured to execute the instructions to perform or support another device to perform: (I) (i) a process of acquiring (1_l)-st obfuscated data for training to (1_m)-th obfuscated data for training from a data provider, wherein an obfuscation network generates the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training respectively by obfuscating (1_l)-st original data for training to (1_m)-th original data for training and wherein m is an integer equal to or greater than 1, (ii) a process of inputting (1_i)-th obfuscated data for training, among the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training, into a user learning network, to thereby allow the user learning network to apply a learning operation to the (1_i)-th obfuscated data for training and thus to generate first characteristic information for training, via at least one user task layer, to be used for performing a task of the user learning network, and at least one first user batch normalizing layer which adjusts a first average and a first variance of first outputs from the at least one user task layer, and (iii) a process of updating task parameters of the at least one user task layer and first batch parameters of the at least one first user batch normalizing layer such that a first error, calculated by referring to (1) the first characteristic information for training or a first task specific output for training created by using the first characteristic information for training and (2) a first ground truth of the (1_i)-th obfuscated data for training, is minimized, to thereby train the user learning network and (II) (i) a process of acquiring (2_l)-st original data for training to (2_n)-th original data for training from a user, wherein n is an integer equal to or greater than 1, (ii) a process of inputting (2_j)-th original data for training, among the (2_l)-st original data for training to the (2_n)-th original data for training, into the user learning network, to thereby allow the user learning network to apply the learning operation to the (2_j)-th original data for training and thus to generate second characteristic information for training, via the at least one user task layer and at least one second user batch normalizing layer which adjusts a second average and a second variance of second outputs from the at least one user task layer, and (iii) a process of updating the task parameters of the at least one user task layer and second batch parameters of the at least one second user batch normalizing layer such that a second error, calculated by referring to (1) the second characteristic information for training or a second task specific output for training created by using the second characteristic information for training and (2) a second ground truth of the (2_j)-th original data for training, is minimized, to thereby train the user learning network.

As one example, the processor further performs or supports another device to perform: (III) (i) a process of acquiring (3_l)-st original data for training to (3_o)-th original data for training from the data provider, wherein o is an integer equal to or greater than 1 and equal to or less than m and wherein the (3_l)-st original data for training to the (3_o)-th original data for training are at least part of the (1_l)-st original data for training to the (1_m)-th original data for training, (ii) a process of inputting (3_k)-th original data for training, among the (3_l)-st original data for training to the (3_o)-th original data for training into the user learning network, to thereby allow the user learning network to apply the learning operation to the (3_k)-th original data for training and thus to generate third characteristic information for training, via the at least one user task layer and the at least one second user batch normalizing layer, and (iii) a process of updating the task parameters of the at least one user task layer and the second batch parameters of the at least one second user batch normalizing layer such that a third error, calculated by referring to (1) the third characteristic information for training or a third task specific output for training created by using the third characteristic information for training and (2) a third ground truth of the (3_k)-th original data for training, is minimized, to thereby train the user learning network.

As one example, the processor further performs or supports another device to perform: (IV) (i) a process of acquiring (4_l)-st original data for training to (4_p)-th original data for training from the user, wherein p is an integer equal to or greater than 1, (ii) a process of inputting (4_s)-th original data for training, among the (4_l)-st original data for training to the (4_p)-th original data for training, into the user learning network, to thereby allow the user learning network to apply the learning operation to the (4_s)-th original data for training and thus to generate fourth characteristic information for training, via the at least one user task layer and the at least one second user batch normalizing layer, (iii) a process of inputting the (4_s)-th original data for training into the obfuscation network, to thereby allow the obfuscation network to obfuscate the (4_s)-th original data for training and thus to generate (4_s)-th obfuscated data for training, (iv) a process of inputting the (4_s)-th obfuscated data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the (4_s)-th obfuscated data for training and thus to generate fifth characteristic information for training, and (v) a process of updating the task parameters of the at least one user task layer and the second batch parameters of the at least one second user batch normalizing layer such that a fourth error, calculated by referring to the (v-1) the fourth characteristic information for training and the fifth characteristic information for training or (v-2) (v-2-a) a fourth task specific output for training created by using the fourth characteristic information for training and (v-2-b) a fifth task specific output for training created by using the fifth characteristic information for training, is minimized, to thereby train the user learning network.

As one example, the (4_l)-st original data for training to the (4_p)-th original data for training are unlabeled data whose ground truths are not labeled, and wherein the processor uses or supports another device to use the fifth characteristic information for training or the fifth task specific output for training as pseudo ground truths of the (4_s)-th original data for training which are at least part of the unlabeled data.

As one example, a provider learning device has trained the obfuscation network by performing or supporting another device to perform (i) a process of inputting sixth original data for training into the obfuscation network, to thereby allow the obfuscation network to obfuscate the sixth original data for training and thus to generate sixth obfuscated data for training, (ii) (ii-1) a process of inputting the sixth obfuscated data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the sixth obfuscated data for training and thus to generate sixth characteristic information for training and (ii-2) a process of inputting the sixth original data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the sixth original data for training and thus to generate seventh characteristic information for training, and (iii) a process of training the obfuscation network such that a sixth error, calculated by referring to the sixth characteristic information for training and the seventh characteristic information for training or by referring to a sixth task specific output for training created by using the sixth characteristic information for training, is minimized and such that a seventh error, calculated by referring to the sixth original data for training and the sixth obfuscated data for training, is maximized.

As one example, the trained provider learning network includes a first trained provider learning network to a u-th trained provider learning network performing respective provider tasks different from one another and wherein u is an integer equal to or greater than 1, and wherein the provider learning device performs or supports another device to perform a process of training the obfuscation network by using an average error created by averaging provider network errors respectively corresponding to the first trained provider learning network to the u-th trained provider learning network or a process of training the obfuscation network by sequentially using the first trained provider learning network to the u-th trained provider learning network.

As one example, a provider learning device has trained the obfuscation network by performing or supporting another device to perform (i) a process of inputting eighth original data for training into the obfuscation network, to thereby allow the obfuscation network to obfuscate the eighth original data for training and thus to generate eighth obfuscated data for training, (ii) (ii-1) a process of inputting the eighth obfuscated data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the eighth obfuscated data for training and thus to generate eighth characteristic information for training, via at least one provider task layer and at least one third provider batch normalizing layer which adjusts a third average and a third variance of third outputs from the at least one provider task layer and (ii-2) a process of inputting the eighth original data for training into the trained provider learning network, to thereby allow the trained provider learning network to apply the learning operation to the eighth original data for training and thus to generate ninth characteristic information for training, via the at least one provider task layer and at least one fourth provider batch normalizing layer which adjusts a fourth average and a fourth variance of fourth outputs from the at least one provider task layer, (iii) a process of training the obfuscation network such that at least one of (iii-1) an eighth error, calculated by referring to the eighth characteristic information for training and the ninth characteristic information for training, and (iii-2) a ninth error, calculated by referring to an eighth task specific output for training created by using the eighth characteristic information for training, is minimized and such that a tenth error, calculated by referring to the eighth original data for training and the eighth obfuscated data for training, is maximized.

As one example, the user learning network includes a first user learning network to a t-th user learning network performing respective tasks different from one another and wherein t is an integer equal to or greater than 1, and wherein the processor performs or supports another device to perform a process of training the first user learning network to the t-th user learning network such that user network errors respectively corresponding to the first user learning network to the t-th user learning network are minimized or a process of training the first user learning network to the t-th user learning network such that an average error created by averaging the user network errors is minimized.

In accordance with still yet another aspect of the present disclosure, there is provided a testing device for testing a user learning network trained to recognize obfuscated data, created by obfuscating original data through an obfuscation network having been trained to obfuscate the original data such that a trained provider learning network recognizes the obfuscated data as same as the original data and such that the original data is indistinguishable from the obfuscated data, to protect personal information, including: at least one memory that stores instructions; and at least one processor configured to execute the instructions to perform or support another device to perform: (I) on condition that a user learning device has trained a user learning network by performing or supporting another device to perform (i) a process of acquiring (1_l)-st obfuscated data for training to (1_m)-th obfuscated data for training from a data provider, wherein an obfuscation network generates the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training respectively by obfuscating (1_l)-st original data for training to (1_m)-th original data for training and wherein m is an integer equal to or greater than 1, (ii) a process of inputting (1_i)-th obfuscated data for training, among the (1_l)-st obfuscated data for training to the (1_m)-th obfuscated data for training, into the user learning network, to thereby allow the user learning network to apply a learning operation to the (1_i)-th obfuscated data for training and thus to generate first characteristic information for training, via at least one user task layer, to be used for performing tasks of the user learning network, and at least one first user batch normalizing layer which adjusts a first average and a first variance of first outputs from the at least one user task layer, (iii) a process of updating task parameters of the at least one user task layer and first batch parameters of the at least one first user batch normalizing layer such that a first error, calculated by referring to (1) the first characteristic information for training or a first task specific output for training created by using the first characteristic information for training and (2) a first ground truth of the (1_i)-th obfuscated data for training, is minimized, (iv) a process of acquiring (2_l)-st original data for training to (2_n)-th original data for training from a user, wherein n is an integer equal to or greater than 1, (v) a process of inputting (2_j)-th original data for training, among the (2_l)-st original data for training to the (2_n)-th original data for training, into the user learning network, to thereby allow the user learning network to apply the learning operation to the (2_j)-th original data for training and thus to generate second characteristic information for training, via the at least one user task layer and at least one second user batch normalizing layer which adjusts a second average and a second variance of second outputs from the at least one user task layer, and (vi) a process of updating the task parameters of the at least one user task layer and second batch parameters of the at least one second user batch normalizing layer such that a second error, calculated by referring to (1) the second characteristic information for training or a second task specific output for training created by using the second characteristic information for training and (2) a second ground truth of the (2_j)-th original data for training, is minimized, to thereby train the user learning network, a process of acquiring test data, and (II) (i) if the test data is determined as original data for testing, a process of inputting the test data into the user learning network, to thereby allow the user learning network to apply the learning operation to the test data and thus to generate first characteristic information for testing, via the at least one user task layer and the at least one second user batch normalizing layer and (ii) if the test data is determined as obfuscated data for testing, a process of inputting the test data into the user learning network, to thereby allow the user learning network to apply the learning operation to the test data and thus to generate second characteristic information for testing, via the at least one user task layer and the at least one first user batch normalizing layer.

In addition, recordable media that are readable by a computer for storing a computer program to execute the method of the present disclosure are further provided.

