Automatic pseudonymization technique recommendation method using artificial intelligence

Abstract

To provide an automatic pseudonymization technique recommendation method using artificial intelligence.SOLUTION: The method includes steps of: receiving an input of a training dataset including a plurality of pieces of data including a column name, a data type, a pseudonymization technique recommendation; adding, for each of the plurality of pieces of data, a data type vector obtained from the data type to a word vector obtained corresponding to the column name to obtain a numeric vector, and labeling the numeric vector with the pseudonymization technique recommendation to generate a plurality of pieces of training data; training a first learning model using the plurality of pieces of training data so that the first learning model outputs a pseudonymization technique recommendation in response to an input of the numeric vector; obtaining a numeric vector corresponding to each column of the dataset to be pseudonymized; and inputting, into the trained first learning model, the numeric vector obtained corresponding to each column of the dataset to be pseudonymized to obtain a pseudonymization technique recommendation for each column of the dataset to be pseudonymized.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to a method for recommending a kana processing technique, and more particularly to a method for recommending an automatic kana processing technique using artificial intelligence. [Background technology]

[0002] In the past, users selected and used pseudonymization methods based on their own common sense, learning, or preferences. For example, for a string "name," one user might choose masking, another might choose two-way encryption, and still another might choose a heuristic pseudonymization method. Different pseudonymization methods have been selected and used based on the personal experience and preferences of the user who selected the pseudonymization method.

[0003] The problem with the conventional method of selecting a pseudonym is that the selection of the method is mainly based on the experience and preferences of each user, so there is a need to transfer the work whenever the person in charge changes, which causes continuous human and time consumption. Also, it is time-consuming to search all the past processing history to refer to the past method for a specific column. Summary of the Invention [Problem to be solved by the invention]

[0004] The technical problem that the present invention aims to solve is to provide a method for recommending an automatic kana processing technique using artificial intelligence. [Means for solving the problem]

[0005] In order to solve the above-mentioned technical problems, a method for recommending an automatic kana processing method using artificial intelligence, which is realized by a computing device according to the present invention, includes the steps of receiving an input of a learning dataset including a plurality of data including a column name, a data type, and a recommended kana processing method, and for each of the plurality of data, obtaining a numeric vector by adding a data type vector obtained from the data type to a word vector obtained corresponding to the column name, labeling the numeric vector with the recommended kana processing method, and generating a plurality of learning data, and a step of training a first learning model using the plurality of learning data so as to output a recommended kana processing method when a numeric vector is input, a step of obtaining a numeric vector corresponding to each column of a dataset to be subjected to kana processing, and a step of inputting the numeric vector obtained corresponding to each column of the dataset to be subjected to kana processing into the trained first learning model, thereby obtaining a recommended kana processing method for each column of the dataset to be subjected to kana processing.

[0006] The step of obtaining a numeric type vector corresponding to each column of the dataset to be subjected to kana processing may include a step of inputting column names corresponding to each column of the dataset to be subjected to kana processing into a second learning model, and obtaining a word vector for each column of the dataset to be subjected to kana processing; a step of obtaining a data type vector corresponding to the data type of each column of the dataset to be subjected to kana processing; and a step of adding a data type vector to the word vector obtained for each column of the dataset to be subjected to kana processing, and obtaining a numeric type vector corresponding to each column of the dataset to be subjected to kana processing.

[0007] The plurality of data included in the learning dataset may further include industry classification information.

[0008] The numeric vector obtained in the step of generating the learning data may further include an industrial classification vector obtained from the industrial classification information.

[0009] The step of obtaining a numeric type vector corresponding to each column of the dataset to be subjected to kana processing may include a step of inputting column names corresponding to each column of the dataset to be subjected to kana processing into a second learning model and obtaining a word vector for each column of the dataset to be subjected to kana processing; a step of obtaining a data type vector corresponding to the data type of each column of the dataset to be subjected to kana processing; a step of obtaining an industrial classification vector corresponding to industrial classification information previously set for the dataset to be subjected to kana processing; and a step of obtaining a numeric type vector corresponding to each column of the dataset to be subjected to kana processing by adding a data type vector and an industrial classification vector to the word vector obtained for each column of the dataset to be subjected to kana processing.

[0010] The method may further include training or retraining the second learning model using a plurality of column names from the training dataset.

[0011] The first training model may be a decision tree model, and the second training model may be a word embedding model.

[0012] The word embedding model may be a FastText model.The decision tree model may be a Gradient Boosting Trees model.

[0013] The method may further include performing pseudonym processing on the dataset to be pseudonymized according to a recommended pseudonym processing technique obtained for each column of the dataset to be pseudonymized.

[0014] An embodiment of the present invention for solving the above technical problem may include a computer-readable recording medium having a program recorded thereon for causing a computer to execute the method.

[0015] A computing device according to an embodiment of the present invention for solving the above technical problem includes a processor and a memory for storing instructions or programs executable by the processor, and can perform the method when the instructions or programs are executed by the processor. Effect of the Invention

[0016] According to the present invention, there is an advantage that a kana processing method can be automatically recommended to a user who is not familiar with kana processing tasks, based on a history of past applications of kana processing methods. [Brief description of the drawings]

[0017] [Figure 1] 1 is a schematic diagram illustrating a system for recommending an automatic kana processing method using artificial intelligence according to an embodiment of the present invention. FIG. [Diagram 2] FIG. 2 is a block diagram illustrating a schematic configuration of a first computing device according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a block diagram illustrating a schematic configuration of a second computing device according to an embodiment of the present invention. [Figure 4] 1 is a flowchart illustrating a learning process of an artificial intelligence-based learning model according to an embodiment of the present invention. [Diagram 5] 1 conceptually illustrates data processed during the learning process of an artificial intelligence-based learning model according to one embodiment of the present invention. [Figure 6] 4 is a flowchart illustrating a process for recommending a kana processing method using artificial intelligence according to an embodiment of the present invention. [Figure 7] 1 conceptually illustrates data processed in the process of recommending a kana processing method using artificial intelligence according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present invention pertains can easily carry out the embodiments.

[0019] The terms used in the present specification are for the purpose of describing the embodiments and are not intended to limit the present invention. In the present specification, the singular includes the plural unless otherwise specified. The terms "comprises" and / or "comprising" used in the specification do not exclude the presence or addition of one or more other components other than the components mentioned. The same reference numerals throughout the specification refer to the same components, and "and / or" includes each and every combination of one or more of the components mentioned. Even if "first", "second", etc. are used to describe various components, it is understood that these components are not limited by these terms. These terms are used merely to distinguish one component from another component. Therefore, it is understood that the first component mentioned below may also be the second component within the technical concept of the present invention.

[0020] In this specification, the term "computing device" includes all kinds of devices that can perform computation and provide a result to a user. For example, the computing device may include not only a desktop PC, a notebook computer, and a server computer, but also a smart phone, a tablet PC, a cellular phone, a PCS phone (Personal Communication Service phone), a synchronous / asynchronous IMT-2000 (International Mobile Telecommunication-2000) mobile terminal, a Palm PC (Palm Personal Computer), a personal digital assistant (PDA), and the like.

[0021] FIG. 1 is a schematic diagram showing a system for recommending an automatic kana processing method using artificial intelligence according to an embodiment of the present invention.

[0022] Referring to FIG. 1, a recommendation system for an automatic kana processing technique using artificial intelligence according to the present invention may include a first computing device 100 and a second computing device 200.

[0023] The first computing device 100 and the second computing device 200 may exchange various types of information and data via a communications network.

[0024] The communication network may include a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), the Internet, 2G, 3G, 4G, LTE mobile communication networks, Bluetooth (registered trademark), Wi-Fi (registered trademark), Wibro, satellite communication networks, and LPWA (Low Power Wide Area) networks such as LoRa and Sigfox, and the communication method may be any method, whether wired or wireless.

[0025] When the first computing device 100 is requested by the second computing device 200 to recommend a kana processing method for a dataset to be subjected to kana processing, the first computing device 100 can generate and provide information on recommended kana processing methods for each column of the dataset (hereinafter referred to as "recommended kana processing method information").

[0026] The second computing device 200 may receive recommendations for pseudonym processing techniques for the data set from the first computing device 100 and perform pseudonym processing operations for the data set.

[0027] FIG. 2 is a block diagram illustrating a schematic configuration of a first computing device according to an embodiment of the present invention.

[0028] 2, the first computing device 100 may include at least one processor 110 and at least one memory 120. In addition, the first computing device 100 may further include a communication device, a user input / output device, and the like.

[0029] The processor 110 can execute instructions and / or computer programs stored in the memory 120 to perform an artificial intelligence-based method for recommending automatic kana processing techniques on the first computing device 100.

[0030] The memory 120 is a recording medium readable by the processor 110 and may store an operating system and at least one instruction and / or program, etc. The memory 120 may also store various information and data related to the execution of the method for recommending an automatic pseudonym processing technique using artificial intelligence in the first computing device 100.

[0031] FIG. 3 is a block diagram illustrating a schematic configuration of a second computing device according to an embodiment of the present invention.

[0032] 3, the second computing device 200 may include at least one processor 210 and at least one memory 220. In addition, the second computing device 200 may further include a communication device, a user input / output device, and the like.

[0033] The processor 210 executes instructions and / or computer programs stored in the memory 220 to perform pseudonym processing on the dataset to be pseudonym processed in accordance with the recommended pseudonym processing method information provided by the first computing device 100 after the second computing device 200 makes a request to the first computing device 100 for a recommendation of a pseudonym processing method for the dataset to be pseudonym processed.

[0034] The memory 220 is a recording medium readable by the processor 210 and may store an operating system and at least one or more instructions and / or programs, etc. The memory 220 may also store various information and data related to performing pseudonym processing on a dataset to be pseudonymized in the second computing device 200.

[0035] FIG. 4 is a flowchart for explaining the learning process of an artificial intelligence-based learning model according to one embodiment of the present invention, and FIG. 5 conceptually shows data processed during the learning process of an artificial intelligence-based learning model according to one embodiment of the present invention.

[0036] 4 and 5, first, the first computing device 100 may receive a training dataset 500 for training the first learning model and / or the second learning model (S410). Here, the first learning model and the second learning model may be an artificial intelligence-based neural network model. Hereinafter, the first learning model is referred to as a kana processing method recommendation model, and the second learning model is referred to as a word embedding model.

[0037] The data set 500 input in step S410 may include a plurality of data items, each of which corresponds to a column name, a data type, and a recommended kana processing method, as shown in Table 1, for example.

[0038] [Table 1]

[0039] Table 1 illustrates n pieces of data including column names, data types, and recommended kana processing methods. Such a plurality of data may be created by a data kana processing expert or may be created based on existing kana processed data. For example, a method in which an expert matches a column name-data type pair with a recommended kana processing method may be used, or a kana processing method applied to each column of a kana processed dataset may be analyzed to provide a plurality of data as illustrated in Table 1. Of course, training datasets created by various methods other than those described here may be used to train the kana processing method recommendation model and / or word embedding model.

[0040] The first computing device 100 then separates (S420) column names, data types, and recommended kana processing techniques 510, 520, 530 from the dataset 500 input in step S410, and can train the word embedding model 111 using the multiple column names 510 separated from the multiple data (S430). In step S430, the word embedding model 111 can be trained to output a quantified vector (word vector) corresponding to the input column name, and if the word embedding model 111 has already been trained, it can be retrained in step S430.

[0041] Here, word embedding is a method of expressing words as quantified vectors, and can be realized using word embedding models such as Word2Vec, FastText, Glove (Global Vectors for Word Representation), etc. Among the word embedding models, the FastText model, which is strong in recognizing words themselves, can be used to convert column names consisting of Hangul character strings into numerical data.

[0042] The FastText model divides words into individual characters and creates information for each character based on information from surrounding characters. In this way, the FastText model creates vector data based on the relationships between characters, so if the characters used in two different words are similar, the vector data will also be similar.

[0043] After the learning of the word embedding model 111 is completed, the first computing device 100 can input each of the multiple column names 510 previously separated from the multiple data as input data to the word embedding model 111, and obtain multiple word vectors 530 for each column name 510 (S440).

[0044] Thereafter, in step S440, the first computing device 100 can add a data type vector corresponding to the data type 520 corresponding to each column name to the word vector 540 obtained for each column name to create a plurality of numeric type vectors 550 (S450). For example, the numeric type vector 550 can be generated by adding a data type of "0" on the numeric side and a data type of "1" on the character side to each word vector 530. Specifically, in the word vector obtained for the column name "Name" in Table 1, the data type is "character", so a numeric type vector can be created by adding "1".

[0045] Next, the first computing device 100 can label the recommended kana processing method 530 for each of the multiple numeric type vectors 550 created in step S450, and generate multiple training data 560 (S460). For example, if there are n recommended kana processing methods, the labeling data can be created using a one-hot encoding method, which is an n-dimensional vector in which the position value corresponding to the recommended kana processing method is set to "1" and the remaining position values ​​are set to "0".

[0046] Finally, the first computing device 100 can train a recommended model of the kana processing method using the multiple training data 560 generated in step S460 (S470). In step (S470), the recommended model of the kana processing method can be trained so that when a numeric vector obtained corresponding to a column name and a data type is input, a recommended kana processing method is output correspondingly. Here, the recommended model of the kana processing method can use an artificial intelligence neural network model based on learning with teacher data. For example, a map learning model such as a decision tree, Random Forest, Gradient Boosting Trees, CART (Classification and Regression Trees), ID3 (Iterative Dichotomiser 3), C5.0 (Successor of ID3, C4.5), etc. can be used. In particular, in this embodiment, in order to realize a more accurate decision tree model, a gradient boosting tree model to which the gradient boosting technology, which is an ensemble technology, is applied, can be used.

[0047] FIG. 6 is a flowchart showing a process for recommending a kana processing method using artificial intelligence according to one embodiment of the present invention, and FIG. 7 conceptually shows data processed in the process for recommending a kana processing method using artificial intelligence according to one embodiment of the present invention.

[0048] Referring to FIG. 6, first, the first computing device 100 may receive a request for a recommendation of a pseudonym processing method from the second computing device 200 (S610). In step (S610), the second computing device 200 may obtain a column name and a data type for each column of the dataset to be pseudonym processed, and transfer data 700 including the column name and data type corresponding to each column to the first computing device 100. Of course, according to an embodiment, the second computing device 200 may immediately transfer the dataset to be pseudonym processed to the first computing device 100, and the first computing device 100 may directly obtain data 700 including the column name and data type corresponding to each column of the dataset to be pseudonym processed.

[0049] The data 700 can be obtained from the data set to be kana processed using the following method. For example, in the case of an Excel data set such as Table 2, the contents of each cell in the first row can be checked to determine the column names, and the data values ​​from the second row to a predetermined row for each column can be checked to determine the data type.

[0050] [Table 2]

[0051] Referring to Table 2, in the first row, we can find the column names "ID", "Name", "Age", "Gender", and "Email". Then, we check the data values ​​of each column. We can see that the data types of the first and third columns are "Numeric", and the data types of the second, fourth, and fifth columns are "Character".

[0052] Next, the first computing device 100 can input the obtained column names 710 corresponding to each column of the dataset to be kana processed into the word embedding model 111 to obtain word vectors 730 corresponding to each column (S620).

[0053] In addition, the first computing device 100 may add a data type vector corresponding to the data type 720 to the word vector 730 obtained for each column of the dataset to be kana processed, to create a numeric type vector 740 for each column of the dataset to be kana processed (S630).

[0054] Next, the first computing device 100 inputs the multiple numeric vectors 740 obtained corresponding to each column of the dataset to be processed into the previously trained recommended model 1130 of a kana processing method, and obtains a recommended kana processing method 750 for each column of the dataset to be processed (S640).

[0055] The first computing device 100 may then provide the second computing device 200 with the recommended pseudonym processing techniques obtained for each column of the dataset to be pseudonym processed (S650).

[0056] Next, the second computing device 200 can perform pseudonym processing for each column of the dataset to be pseudonym processed (S660) according to the recommended pseudonym processing method provided by the first computing device 100. According to an embodiment, before step (S660), the second computing device 200 can also provide recommended information of the pseudonym processing method to the user, so that the user can select whether to apply the recommended pseudonym processing method corresponding to each column.

[0057] Meanwhile, although an embodiment in which the first computing device 100 and the second computing device 200 are separated has been described above, it is also possible to integrate the first computing device 100 and the second computing device 200 into a single device, and realize the single computing device to perform the learning of the word embedding model and the kana processing method recommendation model, the generation of kana processing method recommendation information for the dataset to be kana processed using the learned model, and the kana processing work for the dataset to be kana processed.

[0058] Meanwhile, the data items that require pseudonymization may differ depending on the industry in which the data is used, and different pseudonymization processing methods may need to be applied. In consideration of this, according to an embodiment, each of the multiple data included in the learning dataset may further include industry classification information such as education, health / medical care, finance, and communications.

[0059] For each of the multiple data included in the training dataset, a word vector, a data type vector, and an industry classification vector can be obtained, and a numeric type vector can be obtained by concatenating the word vector, the data type vector, and the industry classification vector. For example, when there are n pieces of industry classification information, the industry classification vector can be created as a one-hot vector, which is an n-dimensional vector in which the position value corresponding to the industry classification information is set to "1" and the remaining position values ​​are set to "0" using one-hot encoding technology.

[0060] As described above, training data is generated by labeling a vector corresponding to a recommended kana processing method for a numeric vector including an industry classification vector, and a recommended model of a kana processing method is trained using the training data thus generated.

[0061] In addition, when the first computing device 100 receives a request for recommending a pseudonym processing method from the second computing device 200, it may also receive industrial classification information corresponding to the industrial field in which the dataset to be processed by pseudonym is utilized. In this case, the second computing device 200 may add an industrial classification vector when creating a numeric vector for each column of the dataset to be processed by pseudonym. This allows the second computing device 200 to recommend a pseudonym processing method according to the industrial field in which the dataset to be processed by pseudonym is utilized.

[0062] The embodiments described above may be implemented using hardware components, software components, and / or a combination of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computing devices, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications that run on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, the processing device may be described as being one in which one is used, but a person skilled in the art will understand that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, the processing device may include multiple processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

[0063] The software may include a computer program, code, instructions, or a combination of one or more of these, and may configure or instruct a processing device to operate as desired, either independently or collectively. The software and / or data may be permanently or temporarily embodied in some type of machine, component, physical device, virtual equipment, computer storage medium, or device to be analyzed by the processing device or to provide instructions or data to the processing device. The software may be distributed over network-coupled computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0064] The method according to the embodiment may be realized in the form of program instructions executable by various computer means and recorded on a computer readable medium. The computer readable medium may include, alone or in combination, program instructions, data files, data structures, and the like. The program instructions recorded on the medium may be those specially designed and constructed for the embodiment, or may be those known and available to those skilled in the art of computer software. Examples of computer readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The above hardware devices may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0065] As described above, although the embodiments have been described with limited drawings, those having ordinary skill in the art may apply various technical modifications and variations based on the above. For example, the described techniques may be performed in an order different from that described, and / or the components of the described systems, structures, devices, circuits, etc. may be combined or combined in a form different from that described, or may be replaced or substituted by other components or equivalents, and still achieve suitable results.

Claims

1. 1. A method for recommending automatic kana processing techniques using artificial intelligence implemented in a computing device including a processor and a memory storing instructions or programs executable by the processor, the method comprising: receiving, by the computing device, a training dataset including column names, data types, and a recommended pseudonym processing technique, the training dataset including a plurality of data; the computing device adds a data type vector obtained from the data type to a word vector obtained corresponding to the column name for each of the plurality of data to obtain a numeric type vector, and labels the numeric type vector with the recommended kana processing method to generate a plurality of training data; training a first learning model using the plurality of training data sets, the first learning model being configured to output a recommended kana processing technique when a numeric vector is input; determining, by the computing device, a numeric vector corresponding to each column of a dataset to be pseudonymized; The computing device inputs the obtained numeric vectors corresponding to each column of the dataset to be processed into the trained first learning model to obtain a recommended kana processing method for each column of the dataset to be processed; Recommendation methods for automated kana processing techniques using artificial intelligence, including:

2. The step of obtaining a numeric vector corresponding to each column of the data set to be kana processed includes: inputting column names corresponding to each column of the dataset to be processed by kana into a second learning model, and obtaining word vectors for each column of the dataset to be processed by kana; determining a data type vector corresponding to a data type of each column of the dataset to be kana processed; A method for recommending an automatic kana processing technique using artificial intelligence as described in claim 1, comprising the step of adding a data type vector to the word vector obtained for each column of the dataset to be subjected to kana processing, and obtaining a numeric type vector corresponding to each column of the dataset to be subjected to kana processing.

3. The plurality of data included in the learning dataset further includes industry classification information, The numerical vector obtained in the step of generating the learning data is further supplemented with an industrial classification vector obtained from the industrial classification information; The step of obtaining a numeric vector corresponding to each column of the data set to be kana processed includes: inputting column names corresponding to each column of the dataset to be processed by kana into a second learning model, and obtaining word vectors for each column of the dataset to be processed by kana; determining a data type vector corresponding to a data type of each column of the dataset to be kana processed; A step of obtaining an industrial classification vector corresponding to industrial classification information preset for the dataset to be subjected to the pseudonym processing; adding a data type vector and an industry classification vector to the word vector obtained for each column of the data set to be kana-processed, to obtain a numeric type vector corresponding to each column of the data set to be kana-processed; The method for recommending an automatic kana processing technique using artificial intelligence according to claim 1 , comprising:

4. The method for recommending an automatic kana processing technique using artificial intelligence according to any one of claims 1 to 3, further comprising a step of training or retraining a second learning model using a plurality of column names obtained from the training dataset.

5. the first learning model is a decision tree model; The method for recommending an automatic kana processing technique using artificial intelligence according to claim 4, wherein the second learning model is a word embedding model.

6. the word embedding model is a FastText model, The method for recommending automatic kana processing techniques using artificial intelligence according to claim 5, wherein the decision tree model is a Gradient Boosting Trees model.

7. The method for recommending an automatic kana processing technique using artificial intelligence as described in claim 1, further comprising a step of performing kana processing on the dataset to be processed according to the recommended kana processing technique obtained for each column of the dataset to be processed.

8. A computer-readable recording medium having recorded thereon a program for causing a computer to execute the method for recommending an automatic kana processing technique using artificial intelligence according to any one of claims 1 to 3.

9. 1. A computing device comprising: A processor; a memory for storing instructions or programs executable by said processor; A computing device, wherein when the instructions or programs are executed by the processor, the method for recommending automatic kana processing techniques using artificial intelligence according to any one of claims 1 to 3 is performed.

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