KR20150012226A - Anonymity management system for privacy information in bigdata having fixed form and free style data - Google Patents

Abstract

The present invention relates to an anonymity management system for privacy information in big data having structured data and unstructured data. The anonymity management system includes: a storage unit to store big data having privacy information; a conversion unit to convert the privacy information included in the big data to anonymized data (anonymized privacy data) in order to distinguish between anonymized data and non-anonymized data; and a detection unit to detect the anonymized data and the non-anonymized data from the big data.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to personal information anonymization management system for large data including fixed and unstructured data,

The present invention relates to a personal information anonymization management system in big data including fixed and unstructured data.

Recently, big data (bigdata) has emerged as a trend of IT technology, and protection issues are becoming a stumbling block to the industry. To solve this problem, it is necessary to detect and anonymize the personal information included in the stereotyped unstructured data in order to make it possible to distribute the data between related industries and companies. In addition, as the Personal Information Protection Act came into effect, we investigated the status of the columns in which personal information was stored among the numerous information defined and stored in the existing DBMS system, and constantly identify and manage personal information as the IT system continuously changes. The necessity has arisen and it is difficult to manage personal information in a lot of catalogs of DBMS because there is a limit to manage the personal information visually and manually, and even if a solution for protecting personal information is introduced, the management cost is remarkably increased and it is difficult to manage the system well

According to an embodiment of the present invention, it is possible to automatically detect whether personal information included in big data composed of fixed and unstructured data is anonymized and to report it in writing, thereby minimizing the cost of investigating and managing personal information anonymization A personal information anonymization management system and a personal information anonymization management program in a big data which can be provided.

According to another embodiment of the present invention, personal information included in big data composed of fixed and unstructured data is anonymized in a way that can not be guessed from the original, but anonymized so as to distinguish data that is not anonymized from data that is not anonymized, A personal information anonymization management system and a personal information anonymization management program in Big Data which can selectively anonymize data that is not available can be provided.

According to an embodiment of the present invention, there is provided an information processing apparatus comprising: a storage unit for storing big data including personal information;

A conversion unit for converting the personal information included in the big data into anonymized data (referred to as 'anonymized personal information') as a method for distinguishing between anonymized data and non-anonymized data; And a detector for detecting personal information anonymized in the big data and non-anonymous personal information. The personal information anonymization management system in the big data including the fixed and unstructured data can be provided.

When the detecting unit detects the personal information that is not anonymized in the big data, the converting unit transforms the non-anonymized personal information detected by the detecting unit into a method capable of distinguishing the anonymous data from the non-anonymous data .

The system may further include a report preparation unit for preparing a result of the detection by the detection unit as a report.

The personal information may include at least one of a name, a telephone number, a passport number, an address, and a resident number.

The conversion unit may convert only a part of the personal information to be anonymized ('anonymization part'), but convert at least a part of the remaining part of the personal information to be anonymized ('non-anonymized part').

A random map generating unit for randomly generating a map used by the converting unit; And the converting unit may perform a converting operation using a map (MAP) generated by the random map generating unit.

If the personal information is data consisting of n (where n is a natural number)

The random map generator generates n maps,

And the converting unit may use the n maps for each place of the personal information and convert the maps.

An encryption / decryption unit for encrypting or decrypting a map (MAP) generated by the random map generation unit; (MAP) generated by the random map generation unit is encrypted by the encryption / decryption unit and stored in the storage unit, and only when used by the conversion unit, And may be used after being decoded by < RTI ID = 0.0 >

The conversion unit may replace part of the resident number with invalid data when converting the resident number into anonymized data.

The conversion unit may add a check digit when converting the personal information into anonymized data.

The converting unit may replace part of the personal information with a character-character, a number, and / or a symbol when converting the personal information into anonymized data.

The conversion unit may add a check digit to the anonymized data converted by the n maps.

The conversion unit may replace a part of the anonymized data converted by the n maps with a character-character, a number, and / or a symbol.

According to another embodiment of the present invention, the personal information included in the big data is classified into anonymized data (referred to as 'anonymized personal information') in a manner that allows the computer to distinguish between the anonymized data and the non- Converting; And detecting personal information anonymized in the big data and personal information that is not anonymized. A computer-readable recording medium having recorded thereon a personal information anonymization management program for executing the method may be provided.

Wherein, in the detecting step, when personal information that is not anonymized in the big data is detected, the converting step distinguishes the anonymized data and the anonymized data from the anonymized personal information detected in the detecting step And converting it into a method that can be performed.

The personal information may include at least one of a name, a telephone number, a passport number, an address, and a resident number.

The converting step may be a step of converting only a part of the personal information to be anonymized ('anonymizing part'), using at least part of the remaining part of the personal information to be anonymized ('non-anonymizing part') .

A random map generating step of randomly generating a map used in the converting step; And the converting step may perform a converting operation using a map (MAP) generated by the random map generating unit.

If the personal information is data consisting of n (where n is a natural number)

Wherein the random map generating step generates n maps,

The converting may be performed using the n maps for each place of the personal information.

Encrypting or decrypting a map (MAP) generated in the random map generating step; (MAP) generated in the random MAP generation step is encrypted and stored in the encryption / decryption step, and only when the MAP is decrypted in the encryption / decryption step, It may be used later.

The converting may replace part of the resident number with invalid data when converting the resident number into anonymized data.

The converting step may be to add a check digit when converting the personal information into anonymized data.

The converting step may replace part of the personal information with a character-character, a number, and / or a symbol when converting the personal information into anonymized data.

The converting step may be to add a check digit to the anonymized data converted by the n maps.

The converting step may replace part of the anonymized data converted by the n maps with a character-character, a number, and / or a symbol.

According to one or more embodiments of the present invention, it is possible to automatically detect whether personal information included in big data composed of fixed and unstructured data is anonymized and to report it in writing, thereby minimizing the cost of investigating and managing personal information anonymization can do.

In addition, personal information contained in big data made up of stereotyped and unstructured data can be anonymized in a way that can not be deduced from the original, but anonymized so as to distinguish between anonymized data and anonymized data, Anonymization can be done. Therefore, the time required for the anonymization work can be drastically reduced even if the personal information included in the large data is large.

FIG. 1 is a diagram for explaining a personal information anonymization management system in big data according to an embodiment of the present invention.
FIG. 2 is a diagram for explaining a personal information anonymization management method in big data according to an embodiment of the present invention.
FIG. 3 is a diagram for explaining a method of converting personal information in big data used in an embodiment of the present invention.
4 is a diagram for explaining a random map used in an embodiment of the present invention.
5 is a diagram for explaining an exemplary configuration of a personal information anonymization management program according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects, features, and advantages of the present invention will become more readily apparent from the following description of preferred embodiments with reference to the accompanying drawings. However, the present invention is not limited to the embodiments described herein but may be embodied in other forms. Rather, the embodiments disclosed herein are provided so that the disclosure can be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

In this specification, when an element is referred to as being on another element, it may be directly formed on another element, or a third element may be interposed therebetween. Further, in the drawings, the thickness of the components is exaggerated for an effective description of the technical content.

Where the terms first, second, etc. are used herein to describe components, these components should not be limited by such terms. These terms have only been used to distinguish one component from another. The embodiments described and exemplified herein also include their complementary embodiments.

Also, when it is mentioned that the first element (or component) is operated or executed on the second element (or component) ON, the first element (or component) It should be understood that it is operated or executed in an operating or running environment or is operated or executed through direct or indirect interaction with a second element (or component).

It is to be understood that when an element, component, apparatus, or system is referred to as comprising a program or a component made up of software, it is not explicitly stated that the element, component, (E.g., memory, CPU, etc.) or other programs or software (e.g., drivers necessary to run an operating system or hardware, etc.)

It is also to be understood that the elements (or components) may be implemented in software, hardware, or any form of software and hardware, unless the context clearly dictates otherwise.

Also, terms used herein are for the purpose of illustrating embodiments and are not intended to limit the invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. The terms "comprises" and / or "comprising" used in the specification do not exclude the presence or addition of one or more other elements.

Hereinafter, the present invention will be described in detail with reference to the drawings. In describing the specific embodiments below, various specific details have been set forth in order to explain the invention in greater detail and to assist in understanding it. However, it will be appreciated by those skilled in the art that the present invention may be understood by those skilled in the art without departing from such specific details. In some instances, it should be noted that portions of the invention that are not commonly known in the description of the invention and are not significantly related to the invention do not describe confusing reasons for explaining the present invention.

FIG. 1 is a diagram for explaining a personal information anonymization management system in big data according to an embodiment of the present invention.

Referring to FIG. 1, a personal information anonymization management system (hereinafter referred to as a 'personal information anonymization management system') in Big Data according to an embodiment of the present invention includes a computer processor 1, And a personal information anonymization management program 7 loaded on the memory 5 under the control of the computer processor 1 to protect the personal information contained in the big data .

Big data stored in the storage unit 3 includes various types of fixed or unstructured data such as data defined in a DBMS column and stored in a file (ex) text, XML, HWP, DOC, XLS, Personal information such as a telephone number, a passport number, an address, and a resident registration number. Big data stored in the storage unit 3 can be updated from time to time.

In the memory 5, a personal information anonymization management program 7 is loaded under the control of the computer processor 1 and operates.

At least one random map (MAP) is loaded into the memory 5 and the personal information anonymization management program 7 uses the at least one random map (MAP) loaded in the memory 5 to anonymize the personal information . When the random map (MAP) loaded in the memory 5 is encrypted, the personal information anonymization management program 7 can use it after decryption.

In the present specification, data for anonymizing personal information will be referred to as 'anonymized personal information' for convenience of explanation.

The personal information anonymization management program 7 can perform the anonymization management method according to an embodiment of the present invention. For example, the personal information anonymization management program 7 can perform the anonymization management method exemplarily shown in Fig.

Referring to FIG. 2, the operation of the personal information anonymization management program 7 will be described. The personal information included in the big data is converted into 'anonymized personal information' (S201), and the periodic or non- The personal information anonymization management program 7 monitors whether any personal information included in the big data is not anonymized (hereinafter referred to as "unprofessional personal information") (S203).

When the personal information anonymization management program 7 detects the personal information unprofitable from the big data stored in the storage unit 3 as a result of the monitoring in step S203 (S205: Y), the personal information anonymization management program 7 stores the detected unprovoked personal information as an anonymous Information (step S201).

On the other hand, if the personal information unanimated management program 7 does not exist in the big data stored in the storage unit 3 as a result of the monitoring in step S203, the personal information anonymization management program 7 performs step S203.

Now, the conversion operation of step S201 will be described in detail with reference to FIG.

FIG. 3 is a diagram for explaining a method of converting personal information in big data used in an embodiment of the present invention.

Referring to FIG. 3, the personal information anonymization management program 7 can perform an operation (S301) of converting personal information using a random map. Referring to the random map exemplarily shown in FIG. 4, the personal information anonymization management program 7 can perform an operation of converting using a plurality of random maps as exemplarily shown in FIG. 4 have. It should be noted that the random maps shown in FIG. 4 are exemplary and that the present invention is not limited to such.

For example, if it is assumed that the personal information is composed of n (where n is a natural number) digit, the personal information anonymization management program 7, Maps can be used.

1, the personal information anonymization management system 10 may include a random map generator for generating a plurality of random maps, and the random map generator may be embedded in the personal information anonymization management program 7, And may be included in the personal information anonymization management system 10 in the form of software and / or hardware.

Referring to FIG. 4, a total of n maps are illustrated by way of example, and these maps are used to convert the value of each digit constituting private information to another value. That is, each of the maps is used to correspond to each digit constituting the personal information on a one-to-one basis and to be converted.

With continued reference to FIG. 3, the personal information anonymization management program 7 may further perform step S303 after performing step S301.

For example, assuming that the map illustrated in FIG. 4 is used to transform 781123-2253647 and 651106-1253647 in step S301, they are as follows.

1) 781123-2 253647 -> 781123-2 297820

2) 651206-1 253647 -> 651206-1 297820

In step S303, the non-converted part of the personal information to be converted is applied to the part to be converted. For example, in 781123-2 297820, the following values are calculated for untransformed data.

(7 + 8 + 1 + 1 + 2 + 3 + 2)% 10 = 4

 The value calculated from the 'unconverted part of personal information' is applied to the 'part of personal information to be converted'.

(2 + 4) 10% = 6

(9 + 4) 10% = 3

(7 + 4) 10% = 1

(8 + 4) 10% = 2

(2 + 4) 10% = 6

(0 + 4) 10% = 4

Finally, after step S302, 781123-2 297820 is converted to 781123-2 631264 .

651206-1 253647 are also transformed in the same or similar way as follows.

(6 + 5 + 1 + 2 + 0 + 6 + 1) 10% = 1

(2 + 1) 10% = 3

(5 + 1) 10% = 6

(3 + 1) 10% = 4

(6 + 1) 10% = 7

(4 + 1) 10% = 5

(7 + 1) 10% = 8

Finally, after the step S303, 651206-1 253647 is converted to 651206-1 354758 .

As described above, after step S303, different data are always converted to different data.

3, the personal information anonymization management program 7 may further perform step S305 after performing step S303. Step S305 is performed in order to be able to judge whether or not the personal information has been anonymized.

In step S305, the check digit may be generated by adding 10% of the sum of (number of digits * weight) and added to the anonymized personal information.

For example, if the converted data through step S303 is 651206-1354758

Each spot weight can be fetched from a random map. If the value is 1 7 3 8 5 9 2 6 4 0

6 * 1 = 7

5 * 7 = 35

1 * 3 = 3

2 * 8 = 16

0 * 5 = 0

6 * 9 = 54

1 * 2 = 2

3 * 6 = 18

5 * 4 = 20

7 * 1 = 7

5 * 7 = 35

8 * 3 = 24

7 + 35 + 3 + 16 + 0 + 54 + 2 + 18 + 20 + 7 + 35 + 24 = 221 10% = 1

Substituting the last digit of the generated check digit results in 651206-1354751.

When checking the anonymized data, it is possible to check whether the converted data is the same by performing the above calculation. If it is not necessary to convert the same value to the same value, the converted value is represented by 651206-1354758 [221] It is also possible to create data by adding a check digit of a certain format for this purpose. If the data after the anonymization does not necessarily need to be valid, a part of the data may be replaced with a certain character such as 651206-1 ****** for masking.

The method of adding the check digit may be useful for examining whether it has been anonymized on a file or table basis.

Although the step S305 is implemented by adding the check digit in FIG. 3, the step S305 may be modified and implemented according to the type of the personal information to be anonymized. For example, if the personal information is a name, step S305 may be modified to replace the part with a character, number, and / or symbol, as shown in the following example.

Ex) Barnett Information Technology -> Sewer or Basso *

By this operation, it is judged whether the personal information includes a character, a number, and / or a symbol if it is a name, so that it can be known whether or not it is anonymized.

In the embodiment of FIG. 3, step S303 is performed after step S301. However, step S301 may be performed after step S303.

The personal information anonymization management program according to the embodiment of the present invention can also automatically create anonymization status of the big data in the form of a report. Referring back to FIG. 2, if the personal information anonymization management program detects information that is not anonymized in step S205, the personal information anonymization management program may configure the personal information anonymization status in the form of a report before performing step S201 again. The status of personal information anonymization includes the status of anonymized personal information and non-anonymous personal information.

The personal information anonymization management program can also organize the status of personal information anonymization in the form of a report even when the user requests it. It is inefficient for a person to individually confirm whether personal information included in big data is anonymized. In the present invention, when anonymizing personal information, it is converted into a manner of identifying whether or not anonymization is to be performed, .

5 is a diagram for explaining an exemplary configuration of a personal information anonymization management program according to an embodiment of the present invention.

5, a personal information anonymization management program according to an embodiment of the present invention may include a conversion unit 9, a detection unit 11, a report creation unit 13, and a random map generation unit 15 have.

The conversion unit 9 performs an operation of anonymizing the personal information included in the big data.

For example, the converting unit 9 can perform the converting operation described with reference to Figs. 2 and 3. Fig. The personal information that has been anonymized by the conversion unit 9 is stored in the storage unit 3. [

The detection unit 11 can detect unnamed personal information and / or anonymized personal information from the personal information included in the big data stored in the storage unit 3. [

For example, the detection unit 11 can perform the detection operation (S205) described with reference to Fig.

The report creating unit 13 can create a report based on the result detected by the detecting unit 11. [ For example, the report creating unit 13 may generate a report using a result of a request from the user or periodically or occasionally detected by the detecting unit 11. This report includes anonymized personal information and / or non-anonymous personal information.

 The random map generation unit 14 generates a random map used when the conversion unit 9 anonymizes the personal information. The random map generation unit 14 can generate a random map equal to or more than the number of digits of the personal information to be anonymized, for example.

On the other hand, although the encryption / decryption unit is not shown in Fig. 5, the personal information anonymization management program may further include an encryption / decryption unit for encrypting the random map and decrypting the encrypted random map.

While the present invention has been described with reference to the particular embodiments and drawings, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. This is possible. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the equivalents of the claims, as well as the claims.

1: Computer processor
3:
5: Memory
7: Personal information anonymization management program
9:
11:
13: Report writing section
15: random map generator

Claims (23)

A storage unit for storing big data including personal information;
A conversion unit for converting the personal information included in the big data into anonymized data (referred to as 'anonymized personal information') as a method for distinguishing between anonymized data and non-anonymized data; And
And a detector for detecting personal information anonymized in the big data and non-anonymous personal information in the big data. The method according to claim 1,
When the detecting unit detects the personal information that is not anonymized in the big data, the converting unit transforms the non-anonymized personal information detected by the detecting unit into a method capable of distinguishing the anonymous data from the non-anonymous data Wherein the personal information anonymization management system comprises: 3. The method of claim 2,
And a report creating unit that creates a report as a result of the detection by the detecting unit. The personal information anonymization management system of claim 1, wherein the personal information anonymization management information includes formatted and unstructured data. The method according to claim 1,
A random map generating unit for randomly generating a map used by the converting unit; Further comprising:
Wherein the conversion unit performs a conversion operation using a map (MAP) generated by the random map generation unit. 5. The method of claim 4,
If the personal information is data consisting of n (where n is a natural number)
The random map generator generates n maps,
Wherein the conversion unit uses the n maps for each place of the personal information and converts the map. 6. The method of claim 5,
An encryption / decryption unit for encrypting or decrypting a map (MAP) generated by the random map generation unit; Further comprising:
(MAP) generated by the random map generation unit is encrypted by the encryption / decryption unit and stored in the storage unit, and is decrypted by the tampering unit only when used by the conversion unit Wherein said personal information anonymization management system comprises: The method according to claim 6,
Wherein the converting unit transforms only a part of the personal information to be anonymized ('anonymizing part'), using at least a part of the remaining part of the personal information to be anonymized ('unneeded part'), Personal information anonymization management system in. 5. The method of claim 4,
The personal information includes at least one of a name, a telephone number, a passport number, an address, and a resident number,
Wherein the conversion unit replaces part of the resident number with invalid data when converting the resident number into anonymized data. The method according to claim 1,
Wherein the conversion unit adds a check digit when converting the personal information into anonymized data. The method according to claim 1,
Wherein the conversion unit replaces a part of the personal information with a character-character, a number, and / or a symbol when converting the personal information into anonymized data. The method according to claim 6,
Wherein the conversion unit adds the check digit to the anonymization data converted by the n maps. The method according to claim 6,
Wherein the conversion unit replaces a part of the anonymized data converted by the n maps with a character-character, a number, and / or a symbol -. On the computer,
Converting the personal information included in the big data into anonymized data (referred to as 'anonymized personal information') in a way that can distinguish between anonymized data and non-anonymous data;
Detecting personal information that is anonymized in the big data and personal information that is not anonymized. The computer-readable recording medium according to claim 1, wherein the personal information is anonymized. 14. The method of claim 13,
Wherein, in the detecting step, when personal information that is not anonymized in the big data is detected, the converting step distinguishes the anonymized data and the anonymized data from the anonymized personal information detected in the detecting step Wherein the step of converting the personal information anonymization management program is a step of converting a personal information anonymization management program into a method capable of performing personal information anonymization management. 13. The method of claim 12,
A random map generating step of randomly generating a map used in the converting step; Further comprising:
Wherein the converting step performs a converting operation using a map (MAP) generated by the random map generating unit. ≪ Desc / Clms Page number 19 > 16. The method of claim 15,
If the personal information is data consisting of n (where n is a natural number)
Wherein the random map generating step generates n maps,
Wherein the conversion is performed using each of the n maps for each place of the personal information. 17. The method of claim 16,
Encrypting or decrypting a map (MAP) generated in the random map generating step; Further comprising:
The map (MAP) generated in the random MAP generation step is encrypted and stored in the encryption / decryption step, and is used after being decrypted in the encryption / decryption step only when it is used in the conversion step A personal information anonymization management program recorded on a computer-readable recording medium. 18. The method of claim 17,
The converting step is a step of converting only a part of the personal information to be anonymized ('anonymizing part') using at least a part of the remaining part of the personal information to be anonymized ('non-anonymizing part') A personal information anonymization management program recorded on a computer-readable recording medium. 15. The method of claim 14,
The personal information includes at least one of a name, a telephone number, a passport number, an address, and a resident number,
Wherein said converting step replaces part of said resident number with invalid data when said resident number is converted into anonymized data. ≪ Desc / Clms Page number 20 > 13. The method of claim 12,
Wherein said conversion step adds a check digit when converting said personal information into anonymized data. ≪ Desc / Clms Page number 19 > 13. The method of claim 12,
Wherein the converting step replaces a part of the personal information with a character, a character, a number, and / or a symbol when converting the personal information into anonymized data. Readable recording medium. 20. The method of claim 19,
Wherein the converting step adds a check digit to the anonymized data converted by the n maps. ≪ Desc / Clms Page number 19 > 21. The method of claim 20,
Wherein the converting step replaces a part of the anonymized data converted by the n maps with a character-character, a number, and / or a symbol -. The computer-readable recording medium storing the personal information anonymization management program media.

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