KR101704702B1 - Tagging based personal data de-identification system and de-identification method of personal data - Google Patents

Abstract

A tagging-based personal information de-identification system includes a big data storage place, a personal information non-identification unit and a personal information storage place. The personal information non-identification unit comprises: a personal information searching part for receiving first data from the big data storage place and searching for personal information among the first data; a de-identification part for identifying the personal information among the first data by using a random token and a cipher text; and a tagging generation part for outputting second data obtained by replacing the personal information of the first data with tagging personal information to the big data storage place. The personal information storage place stores the de-identified personal information. So, the personal information can be protected.

Description

[0001] TAGGING BASED PERSONAL DATA DE-IDENTIFICATION SYSTEM AND DE-IDENTIFICATION METHOD OF PERSONAL DATA [0002]

The present invention relates to a tagging-based personal information non-discrimination system and a personal information non-discrimination method using the same, more specifically, to protect personal information by non-identifying personal information with random tokens and ciphertext, A tagging-based personal information non-discrimination system capable of utilizing data through a personal computer, and a personal information non-discrimination method using the same.

Big data is collected and utilized in various fields such as health, medical, public, distribution, marketing, and manufacturing. Personal information and privacy issues are emerging when these big data are utilized.

If the personal information in the big data is not identified, the semantic property of the personal information is lost and the data usability is low.

SUMMARY OF THE INVENTION Accordingly, the present invention has been made to solve the problems of the prior art personal information non-discrimination system and method, and it is an object of the present invention to protect private information by distinguishing personal information by random token and cipher text, Based personal information non-discrimination system capable of utilizing the data through a network.

Another object of the present invention is to provide a method of identifying personal information using the tagging-based personal information non-discrimination system.

A tagging-based personal information non-discrimination system according to an embodiment of the present invention includes a big data repository, a personal information non-discrimination unit, and a personal information repository. Wherein the personal information non-discrimination unit comprises: a personal information searching unit for receiving first data from the big data repository and searching for personal information among the first data; means for identifying the personal information among the first data as a random token and a cipher text And a tagging unit for outputting second data obtained by replacing the personal information with the tagging personal information among the first data to the big data store. The personal information store stores the non-identified personal information.

In an embodiment of the present invention, the non-identifying unit copies the type of the personal information and the contents of the personal information of the first data among the first data into the first memory area, And generates the ciphertext corresponding to the content of the personal information. The personal information store may store the random token and the cipher text.

In an embodiment of the present invention, the personal information store may store the ciphertext received by the non-identifying unit when the ciphertext received by the non-identifying unit does not overlap with ciphertexts previously stored in the personal information store have.

In one embodiment of the present invention, the personal information non-discrimination unit may further include an anonymization unit. Wherein the anonymizing unit copies the content of the personal information of the type of the personal information and the first data of the first data into the second memory area and generates anonymization data corresponding to the personal information of the first data can do. The anonymization data may be data representing the property of an individual irreversibly extracted from the personal information of the first data.

In one embodiment of the present invention, the tagging personal information includes at least one of the type of the personal information among the first data, the random token of the personal information among the first data, And may include the anonymization data.

In one embodiment of the present invention, the tagging-based personal information non-discrimination system may further comprise a personal information re-identification unit. Wherein the personal information re-identification unit comprises: a tagging information searching unit for receiving the second data from the big data store and searching for the tagging personal information of the second data; a token extracting unit for extracting the random token from the tagging private information; And a re-identification unit for calling the ciphertext corresponding to the random token from the personal information store, decrypting the cipher text, and replacing the tagging private information of the second data with pre-tagged private information. have.

In one embodiment of the present invention, the re-identification unit copies the extracted random token into the third memory area, retrieves the ciphertext corresponding to the random token from the personal information storage, decrypts the ciphertext Decrypted data of the first data, and outputting the third data generated by replacing the tagging personal information of the second data with the decrypted data to the big data store.

 According to another aspect of the present invention, there is provided a tagging-based personal information non-discrimination method comprising: receiving first data from a big data store; searching personal information among the first data; Identifying non-identified personal information as random tokens and ciphertext among the first data, outputting the non-identified personal information to the personal information storage, replacing the personal information with the tagging private information Generating second data and outputting the second data to the big data store.

In one embodiment of the present invention, the tagging-based personal information non-discrimination method comprises receiving the second data from the big data store, retrieving the tagging personal information of the second data, Calling the cipher text corresponding to the random token from the information repository, and decrypting the cipher text to replace the tagging private information of the second data with pre-tagging private information.

A tagging-based personal information non-discrimination system and a personal information non-discrimination method using the same according to the present invention distinguish personal information of a first data item of a big data store with a random token and a cipher text, Replace the information with your tagging personal information and overwrite the Big Data Store. Accordingly, personal information in the big data storage can be protected.

In addition, the non-identified personal information is re-identified using the random token and the cipher text, and the corresponding data can be utilized.

1 is a block diagram illustrating a tagging-based personal information non-discrimination system in accordance with an embodiment of the present invention.
Fig. 2 is a block diagram showing the personal information non-discrimination unit of Fig. 1;
FIG. 3 is a block diagram showing the personal information re-identification unit of FIG. 1;
4 is a table showing the operation of the personal information searching unit of FIG.
5 is a table illustrating the operation of the personal information store of FIG.
6 is a table showing the operation of the tagging information searching unit of FIG.

For the embodiments of the invention disclosed herein, specific structural and functional descriptions are set forth for the purpose of describing an embodiment of the invention only, and it is to be understood that the embodiments of the invention may be practiced in various forms, The present invention should not be construed as limited to the embodiments described in Figs.

The present invention is capable of various modifications and various forms, and specific embodiments are illustrated in the drawings and described in detail in the text. It is to be understood, however, that the invention is not intended to be limited to the particular forms disclosed, but on the contrary, is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms may be used for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises ", or" having ", and the like, are used to specify the presence of stated features, integers, But do not preclude the presence or addition of steps, operations, elements, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be construed as meaning consistent with meaning in the context of the relevant art and are not to be construed as ideal or overly formal in meaning unless expressly defined in the present application .

On the other hand, if an embodiment is otherwise feasible, the functions or operations specified in a particular block may occur differently from the order specified in the flowchart. For example, two consecutive blocks may actually be performed at substantially the same time, and depending on the associated function or operation, the blocks may be performed backwards.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The same reference numerals are used for the same constituent elements in the drawings and redundant explanations for the same constituent elements are omitted.

1 is a block diagram illustrating a tagging-based personal information non-discrimination system in accordance with an embodiment of the present invention.

Referring to FIG. 1, the personal information non-discrimination system includes a big data repository 100, a personal information repository 200, and a personal information non-discrimination unit 300. The personal information non-discrimination system may further include a personal information re-identification unit (400).

The big data store 100 may be a database storing a plurality of data. The Big Data Store 100 is a structured or unstructured data store collected by various demands.

For example, various types of data may be stored in the big data store 100, and personal information may be stored in the big data store 100.

For example, the personal information may include a resident registration number, a foreigner registration number, a passport number, a bank account number, a card number, and the like.

The personal information non-discrimination unit 300 receives the first data from the big data repository 100, and outputs the non-identified personal information to the personal information storage 200. The personal information non-discrimination unit 300 generates second data in which the personal information in the first data is replaced with the tagging personal information, and outputs the generated second data to the big data store 100. At this time, the first data of the big data repository 100 may be overwritten with the second data generated by the personal information non-discrimination unit 300. [ As a result, the personal information among the information of the big data repository 100 is converted into the tagging private information by the personal information non-identification unit 300, and is stored in the big data repository 100. Accordingly, sensitive personal information is deleted from the big data storage 100, and personal information can be protected.

The personal information storage 200 receives and stores the non-identified personal information from the personal information non-identification unit 300. For example, the non-identified personal information may include a random token and a cipher text.

The personal information re-identification unit 300 receives the second data from the big data repository 100 and replaces the tagging personal information of the second data with pre-tagging personal information. Therefore, the personal information re-identifying unit 300 restores the pre-tagging personal information, and can effectively use the personal information after the personal information non-discrimination.

Fig. 2 is a block diagram showing the personal information non-discrimination unit of Fig. 1; FIG. 3 is a block diagram showing the personal information re-identification unit of FIG. 1; 4 is a table showing the operation of the personal information searching unit of FIG. 5 is a table illustrating the operation of the personal information store of FIG. 6 is a table showing the operation of the tagging information searching unit of FIG.

Operations of the personal information non-discrimination unit 300 will be described in detail with reference to FIGS. 2, 4 and 5 first.

The personal information non-discrimination unit 300 may include a personal information searching unit 320, a non-identifying unit 340, and a tagging generating unit 380.

The personal information searching unit 320 receives the first data from the big data repository 100 and searches for the personal information among the first data. The personal information searching unit 320 sets a search pattern of the type of the personal information and the regular expression type.

FIG. 4 shows an example of a search pattern of the personal information searching unit 320. FIG. For example, the type of the personal information may be an e-mail address, a telephone number, a resident registration number, and the like. The search pattern may include a regular expression for locating the email address in a string of the first data. The search pattern may include a regular expression for locating the telephone number in a string of the first data. The search pattern may include a regular expression for locating the resident registration number in the string of the first data.

The personal information searching unit 320 can sequentially read data from the big data store 100 and search for data that matches the personal information search pattern.

The non-identifying unit 340 non-identifies the personal information among the first data by using a random token and a cipher text. The non-identifying unit 340 outputs the non-identified personal information to the personal information storage 200.

The non-identifying unit 340 may copy the type of the personal information among the first data and the contents of the personal information among the first data to the first memory area. Examples of the type and contents stored in the first memory area are as follows.

Example> Type (resident registration number), contents (800101-1 ******)

The non-identifying unit 340 may generate the random token having the same length as the content of the personal information. For example, the random token is a character string having the same format and length as the content, and is generated in a random manner. For example, when the generated random token is overlapped with the random token previously stored in the personal information storage 200, the non-identifying unit 340 may regenerate another random token. An example where the random token is further generated is as follows.

Example> Type (resident registration number), contents (800101-1 ******), random token (abcdef-hijkadg)

The non-identifying unit 340 may generate the cipher text corresponding to the content of the personal information. An example in which the above-described cipher text is further generated is as follows.

Example> Type (resident registration number), contents (800101-1 ******), random token (abcdef-hijkadg), ciphertext (nGAB5L2 + Et + wRtkQiTPfKA ==)

The non-identifying unit 340 outputs the random token and the ciphertext to the personal information store 200. FIG. 5 shows an example of the table structure of the personal information storage.

For example, when the ciphertext received by the non-identifying unit 340 does not overlap with ciphertexts previously stored in the personal information storage, the personal information storage 200 stores the ciphertexts received by the non- You can store the ciphertext.

The personal information non-discrimination unit 300 may further include an anonymization unit 360. The anonymizing unit 360 may copy the content of the personal information among the first data and the type of the personal information among the first data to the second memory area. Examples of the type and contents stored in the second memory area are as follows.

Example> Type (resident registration number), contents (800101-1 ******)

The anonymization unit 360 may generate anonymization data corresponding to the personal information among the first data. The anonymization data may be data representing the property of an individual irreversibly extracted from the personal information of the first data.

For example, from the above-mentioned resident registration number 800101-1 ******, the individual can extract the fact that he is a man born on January 1, 1980. At this time, the anonymization data may be valid data for indicating the nature of the individual in a line that does not over-expose the personal information. For example, the number of anonymized data extracted from the resident registration number 800101-1 ****** in the above example may be 30. For example, the anonymization data extracted from the resident registration number 800101-1 ****** in the above example may be male. For example, the anonymization data extracted from the resident registration number 800101-1 ****** in the above example may be January birth date. An example of the data to which the anonymized data is added is as follows.

Example> Type (resident registration number), contents (800101-1 ******), anonymization data (30 units)

The tagging generation unit 380 generates second data in which the personal information is replaced with tagging personal information among the first data. The tagging generation unit 380 outputs the second data to the big data store 100. [

The tagging personal information may include the type of the personal information among the first data, the random token of the personal information among the first data, and the anonymized data corresponding to the personal information among the first data. An example of the tagging personal information is as follows.

Example> Tagging private information = {[type] [random token] [anonymization data]}

For example, the type may be a resident registration number, passport number, driver's license number, alien registration number, card number, bank account number, address, telephone number, and the like. For example, the random token may be random number and numerical data having the same number of digits as the content of the personal information. For example, the anonymization data may be generated by various anonymization processing techniques such as alias processing, aggregation processing, data value deletion, categorization, and the like.

For example, the start and end tags of the tagging private information may be denoted by {}. For example, the start and end tags of the data in the tagging personal information may be denoted by [].

The tagging generation unit 380 generates the second data by replacing the personal information of the first data with the tagging personal information. An example of the first data and an example of the second data are as follows.

Example>

(800202-2 ******) from the account of Kookmin Bank (102-12345-123456) to withdraw the 100,000 won from the account of Yoo Gwan-soon (800202-2 ******) I transferred to the bank account (11-222-22222).

(Account number [cdf-huoeh-hijoeh]), who is a member of Kookmin Bank ({[name] [갉 닳 닳]] lokefg] [""]}) You can withdraw 100,000 won from your account and get a copy of your [{name] [contact] [u]} ({[resident registration number] [bcdefg-ijrkrgb] [30s, Woori Bank ({[account number] [aa-ije-ppode] [""]}) transferred to account.

The operation of the personal information re-identification unit 400 will be described in detail with reference to FIGS. 3 and 6. FIG.

The personal information re-identification unit 400 includes a tagging information search unit 420, a token extraction unit 440, and a re-identification unit 460.

The tagging information searching unit 420 receives the second data from the big data store 100. The tagging information searching unit 420 searches the second data for the tagging personal information. And is the same as the example of the second data generated by the tagging generating unit 380.

The tagging information searching unit 420 can set a tagging private information search pattern. The tagging information searching unit 420 can set the items of the tagging personal information and the search pattern of the regular expression type.

6 shows an example of a search pattern of the tagging information searching unit 420. As shown in FIG. For example, the item of the tagging personal information may be tagging information, personal information type, random token, anonymized data, and the like. The search pattern may include a regular expression for finding the tagging information in a string of the second data. The search pattern may include a regular expression for locating the personal information type in a string of the second data. The search pattern may include a regular expression for locating the random token in a string of the second data. The search pattern may include a regular expression for locating the anonymized data in a string of the second data.

The tagging information searching unit 420 sequentially reads the data including the tagging personal information from the big data store 200. It is possible to search for data that matches the tagging information search pattern.

The token extractor 440 extracts the random token from the tagging private information. For example, the token extractor 440 may copy the tagging personal information to the third memory area. An example of the tagging personal information stored in the third memory area is as follows.

Example> Tagging personal information ({[Resident Registration Number] [abcdef-hijkadg] [30s, M]}}

The token extracting unit 440 may extract a random token from the tagging private information. An example of extracting the random token is as follows.

Example> Tagging personal information ({[Resident Registration Number] [abcdef-hijkadg] [30s, M]}), random token (abcdef-hijkadg)

The re-identification unit 460 calls the cipher text corresponding to the random token from the personal information storage 200, decrypts the cipher text, and stores the tagging private information of the second data as pre-tagging private information Replace.

For example, the re-identification unit 460 may copy the extracted random token to the fourth memory area. The random tokens stored in the fourth memory area are as follows.

Example) The random token (abcdef-hijkadg)

The re-identification unit 460 retrieves the ciphertext corresponding to the random token from the personal information storage 200. An example in which the cipher text is retrieved is as follows.

Example) random token (abcdef-hijkadg), ciphertext (nGAB5L2 + Et + wRtkQiTPfKA ==)

The re-identification unit 460 generates decrypted data by decrypting the cipher text. An example in which the decoded data is further generated is as follows.

The random token (abcdef-hijkadg), the cipher text (nGAB5L2 + Et + wRtkQiTPfKA ==), the decrypted data (800101-1 ******)

The re-identification unit 460 may generate the third data by replacing the tagging personal information of the second data with the decoded data. The re-identification unit 460 may output the third data to the big data store 300 again. At this time, the big data storage 300 may overwrite the third data with the second data. Alternatively, the big data store 300 may not delete the non-identified second data even if the third data is input.

An example of the second data and an example of the third data are as follows.

Example>

(Account number [cdf-huoeh-hijoeh]), who is a member of Kookmin Bank ({[name] [갉 닳 닳]] lokefg] [""]}) You can withdraw 100,000 won from your account and get a copy of your [{name] [contact] [u]} ({[resident registration number] [bcdefg-ijrkrgb] [30s, Woori Bank ({[account number] [aa-ije-ppode] [""]}) transferred to account.

(Third data) Hong Gil-dong (800101-1 ******) withdraws 100,000 won from the account of Kookmin Bank (102-12345-123456) I transferred to the bank account (11-222-22222).

The third data may be the same as the first data which is the personal information before the non-identification.

The tagging-based personal information non-discrimination method includes receiving first data from a big data store (100), searching personal information among the first data, storing the personal information among the first data into a random token And outputting the non-identified personal information to the personal information storage (200), generating second data in which the personal information is replaced with the tagging personal information among the first data, And outputting the second data to the big data store 100.

The tagging-based personal information non-discrimination method may further include receiving the second data from the big data store (100), searching the tagging personal information of the second data, And calling the ciphertext corresponding to the random token from the personal information storage 200, decrypting the cipher text, and replacing the tagging private information of the second data with pre-tagged private information As shown in FIG.

According to this embodiment, the personal information non-discrimination unit 300 identifies the personal information of the first data of the big data store 100 as a random token and a cipher text, and the personal information of the first data is tagged It is overwritten on the big data storage 100 by replacing it with personal information. Accordingly, personal information in the big data repository 100 can be protected.

In addition, the personal information re-identification unit 400 may re-identify the non-identified personal information to a state before the non-identification using the random token and the cipher text. Therefore, it is possible to use the data even after it is unidentified.

The present invention can be applied to devices, servers and systems for personal information non-discrimination and re-identification. The present invention can be widely applied in areas where security or protection of information is required.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims. It will be understood.

100: Big Data Store 200: Personal Information Store
300: personal information non-discrimination unit 320: personal information search unit
340: non-identifying unit 360: anonymizer
380: tagging generation unit 400: personal information re-identification unit
420: tagging information searching unit 440: token extracting unit
460:

Claims (9)

Big Data Store;
A personal information searching unit for receiving first data from the big data repository and searching for personal information among the first data, a non-identifying unit for non-identifying the personal information among the first data by using a random token and a cipher text, And a tagging unit for outputting second data obtained by replacing the personal information of the first data with tagging private information to the big data store. And
And a personal information storage for storing the non-identified personal information,
The non-identifying unit copies the type of the personal information of the first data and the content of the personal information of the first data into the first memory area, and generates the random token having the same length as the content of the personal information Generates the encrypted text corresponding to the content of the personal information,
The private information store storing the random token and the ciphertext,
Wherein the personal information non-discrimination unit further comprises an anonymizing unit,
Wherein the anonymizing unit copies the content of the personal information of the type of the personal information and the first data of the first data into the second memory area and generates anonymization data corresponding to the personal information of the first data In addition,
Wherein the anonymization data is data representing a property of an individual irreversibly extracted from the personal information of the first data. delete The personal information storage system according to claim 1, wherein the personal information storage stores the ciphertext received by the non-cognizant unit when the ciphertext received by the non-cognizant unit does not overlap with ciphertexts previously stored in the personal information store Tagging - based personal information non - discrimination system. delete 2. The method of claim 1, wherein the tagging personal information includes at least one of the type of the personal information of the first data, the random token of the personal information of the first data, Tag information based on the tag information. 6. The apparatus of claim 5, further comprising a personal information re-
The personal information re-identification unit
A tagging information retrieval unit receiving the second data from the big data store and retrieving the tagging private information of the second data;
A token extracting unit for extracting the random token from the tagging private information; And
And a re-identification unit for calling the cipher text corresponding to the random token from the personal information storage and decrypting the cipher text to replace the tagging private information of the second data with pre-tagging private information. Tagging - based personal information non - discrimination system. The method according to claim 6, wherein the re-identification unit copies the extracted random token into the third memory area, retrieves the ciphertext corresponding to the random token from the private information storage, decrypts the decrypted data And outputs the third data generated by replacing the tagging personal information of the second data with the decoded data to the big data store. Receiving first data from a big data store;
Searching personal information among the first data;
Identifying the personal information among the first data as a random token and a cipher text;
Outputting the non-identified personal information to the personal information storage;
Generating second data in which the personal information is replaced with tagging personal information among the first data; And
And outputting the second data to the big data store,
Receiving the second data from the big data store;
Retrieving the tagging personal information from the second data;
Extracting the random token from the tagging private information; And
Further comprising the step of: calling the ciphertext corresponding to the random token from the personal information store and decrypting the cipher text to replace the tagging private information of the second data with pre-tagged private information. Information non - discrimination method.
delete

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