KR102105368B1 - Data storage and analysis methods and programs in the cloud to prevent the leakage of important information - Google Patents

Abstract

Methods and programs for safely storing and analyzing data in the cloud are disclosed. The client computer de-identifying the original data using the mapping table; The client computer transmitting the non-identifying data to a cloud server; Storing and analyzing the non-identifying data by the cloud server; Receiving, by the client computer, non-identification analysis data from the cloud server; And identifying, by the client computer, the non-identification analysis data using the mapping table.

Description

Data storage and analysis methods and programs in the cloud to prevent the leakage of important information}

The present invention relates to a method and program for safely storing and analyzing data in the cloud, and more particularly, to a method and program for safely storing and analyzing data in the cloud so that important information of the client is not disclosed to the outside. .

In the past, companies have built and operated separate data centers, but recently, they have borrowed and operated computing infrastructure resources from cloud services, and gradually reduced their own data centers and introduced and utilized cloud services in many areas.

However, when such a cloud server is hacked, or when a person other than an administrator accesses it, the entire data of the cloud server is disclosed to an unspecified number of people, and there is a problem that important information of customers can spread over the Internet.

The technology that is the background of the present invention is disclosed in Korean Patent Publication No. 10-2012-0128827, etc., but a fundamental solution to the above-mentioned problem has not been proposed.

The present invention for solving the above-described problems can provide a method and a program for safely storing and analyzing data uploaded to a cloud server by de-identifying the original data through a mapping table.

In addition, the present invention analyzes non-identifying data from a cloud server and transmits it to a client computer, and identifies and analyzes the non-identifying data analyzed by the client computer to safely store and analyze data in the cloud And programs.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

A method for safely storing and analyzing data in the cloud according to an embodiment of the present invention for solving the above-described problems includes: a client computer de-identifying original data using a mapping table; The client computer transmitting the non-identifying data to a cloud server; Storing and analyzing the non-identifying data by the cloud server; Receiving, by the client computer, non-identification analysis data from the cloud server; And identifying, by the client computer, the non-identification analysis data using the mapping table.

In addition, the step of de-identifying may include: de-identifying numeric information in the original data using a specific linear function of the mapping table; And replacing character type information among the original data with an arbitrary character string in the mapping table to de-identify.

In addition, the step of de-identifying further includes the step of generating a mapping table according to a preset mapping table generation rule by the mapping table generator.

In addition, the step of de-identifying further includes the step of receiving data to be de-identified from the original data.

In addition, prior to the de-identifying step, the client computer requesting a hash key to proceed with the de-identification to the cloud server; Generating and storing the hash key by the cloud server, and transmitting the hash key to the client computer; And verifying the hash key received by the client computer in communication with the cloud server. Before the identifying step, the client computer requests the hash key for identification to the cloud server. step; Transmitting the hash key stored in the hash key storage unit to the client computer by the cloud server; And verifying the hash key received by the client computer communicating with the cloud server.

In addition, after the identifying step, the client computer displaying the identification analysis data as a graph image; further includes.

In addition, when it is necessary to restore the original data, the client computer requesting the transfer of non-identifying data to the cloud server; And identifying, by the client computer, non-identification data transmitted from the cloud server using the mapping table.

A method for safely storing and analyzing data in the cloud according to another embodiment of the present invention for solving the above-described problems includes: a cloud server generating and storing a hash key and transmitting it to a client computer; Verifying the hash key received by the client computer and de-identifying the original data using a mapping table; The cloud server receiving non-identifying data from a client computer; Analyzing, by the cloud server, the non-identifying data; Transmitting, by the cloud server, the non-identification analysis data to a client computer; And verifying the hash key by the client computer and identifying the non-identifying analysis data using the mapping table.

The program for safely storing and analyzing data in the cloud according to another embodiment of the present invention is combined with a hardware computer to execute a method for safely storing and analyzing the above-mentioned data in the cloud, and stored in a medium.

According to the present invention as described above, since the original data is de-identified through the mapping table in the client computer and uploaded to the cloud server, the data cannot be identified even if the cloud server is exposed or hacked by others.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram of a system for safely storing and analyzing data in the cloud according to an embodiment of the present invention.
2 is a flowchart of a method for safely storing and analyzing data in the cloud according to an embodiment of the present invention.
3 is an exemplary diagram illustrating de-identification of numeric information according to an embodiment of the present invention.
4 is an exemplary diagram illustrating the de-identification of character information according to an embodiment of the present invention.
5 is an exemplary view illustrating that the original data is converted into non-identified data by de-identifying the original data through FIGS. 3 and 4.

Advantages and features of the present invention, and methods for achieving them will be clarified with reference to embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only the present embodiments allow the disclosure of the present invention to be complete, and are common in the art to which the invention pertains. It is provided to fully inform the skilled person of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, the singular form also includes the plural form unless otherwise specified in the phrase. As used herein, “comprises” and / or “comprising” does not exclude the presence or addition of one or more other components other than the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and “and / or” includes each and every combination of one or more of the mentioned components. Although "first", "second", etc. are used to describe various components, it goes without saying that these components are not limited by these terms. These terms are only used to distinguish one component from another component. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used as meanings commonly understood by those skilled in the art to which the present invention pertains. In addition, terms defined in the commonly used dictionary are not ideally or excessively interpreted unless explicitly defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Prior to the description, the meaning of terms used in the present specification will be briefly described. However, it should be noted that the description of terms is intended to aid understanding of the present specification, and is not used to limit the technical spirit of the present invention unless explicitly described as a limitation of the present invention.

-De-identification: Refers to a series of processes or methods that convert some or all of the information so that it cannot be identified (eg, personal information, company information, confidentiality, etc.). It means to encrypt the information so that it is not identifiable even by a person other than the administrator or the person in charge who has access (management) of the information.

-Identification: De-identified data means decryption so that the de-identified data can be recognized because it cannot be identified even when the administrator or the person in charge who is authorized to manage the data accesses the data.

-Original data: Data uploaded to the computer by the client for backup and analysis in the cloud, and may be files created by a program such as Excel.

-Non-identified data: Original data refers to un-identified data.

-Non-identification analysis data: The non-identification analysis data is analyzed in the cloud server, and refers to analysis data in an unidentifiable state.

-Identification analysis data: Non-identification analysis data is identified on the client computer, and refers to analysis data in a identifiable state.

1 is a block diagram of a system for safely storing and analyzing data in the cloud according to an embodiment of the present invention.

Referring to FIG. 1, a system for safely storing and analyzing data in the cloud according to an embodiment of the present invention includes a client computer 100 and a cloud server 200.

The client computer 100 includes a mapping table generation unit 110, a non-identification module 130, a hash key request unit 120, a hash key matching unit 123, an identification module 140, and an analysis data display unit ( 150) and a data restoration module 160, and the cloud server 200 includes a hash key generation unit 210, a data storage unit 220, and a data analysis unit 230.

The mapping table generation unit 110 generates a mapping table 115 according to a preset mapping table 115 generation rule to proceed with de-identification when original data is input. More details on the creation of the mapping table 115 will be described in detail later.

When the client computer 100 receives the original data, the de-identification module 130 de-identifies the received original data using the mapping table 115 to generate non-identification data.

At this time, the de-identification module 130 of the client computer 100 performs de-identification by using the mapping table 115 generated by the mapping table generation unit 110.

In addition, it is preferable that the client computer 100 stores the mapping table 115 that has undergone de-identification for identification of the non-identification data.

At this time, the de-identification module 130 replaces the character-type information among the original data with an arbitrary character string of the mapping table 115, and de-identifies the numeric information using the specific linear function of the mapping table 115. Identify it. More details on de-identification will be explained later.

The hash key request unit 120 of the client computer 100 requests the hash key before de-identification through the mapping table 115. At this time, since the hash key is generated by the hash key generator 210 of the cloud server 200, the hash key is requested to the cloud server 200.

The hash key generation unit 210 of the cloud server 200 generates and stores the hash key and transmits it to the client computer 100.

When the hash key is received from the cloud server 200, the hash key matching unit 123 of the client computer 100 communicates with the cloud server 200 to verify whether it is the same as the hash key stored in the hash key generation unit 210. . If it is determined that the hash keys match according to the verification result of the hash key matching unit 123, the de-identification module 130 proceeds to de-identify the original data.

For example, when the client computer 100 uses a web browser program such as Explorer, Google Chrome, and Firefox, when it is connected to the cloud server 200 through a web browser and receives a hash key from the cloud server 200 , It means that the hash key is applied in the web browser to proceed with de-identification or identification.

Here, the hash key means a password for de-identification and identification of data. Therefore, the client computer 100 cannot proceed with de-identification or identification of data without the hash key received from the cloud server 200.

When the de-identification module 130 of the client computer 100 completes the de-identification, the generated non-identification data is transmitted to the cloud server 200.

The data storage unit 220 of the cloud server 200 stores non-identification data received from the client computer 100.

The data analysis unit 230 of the cloud server 200 analyzes non-identification data received from the client computer 100.

Preferably, the data analysis unit 230 analyzes the non-identification data received from the client computer 100 to generate non-identification analysis data, and stores the non-identification analysis data in the data storage unit 220.

At this time, the data analysis unit 230 maintains information that is not subject to analysis, such as character-type information (name, department, job title, etc.), and information such as numeric information (age, years of service, annual salary, performance score, etc.) It is preferable to conduct an analysis because they are subject to analysis.

In addition, since the de-identification module 130 of the client computer 100 de-identifies the numeric information using a linear function, each numeric-type information maintains a ratio between each other, so the de-identification Analysis of data is possible.

The identification module 140 of the client computer 100 identifies the non-identification analysis data received from the cloud server 200 by the client computer 100.

At this time, the hash key request unit 120 requests the hash key before the identification module 140 proceeds with the identification.

At this time, the hash key uses the hash key generated by the hash key generation unit 210 of the cloud server 200 to de-identify the original data, and the client computer 100 hash the hash key generation unit 210 You can request the key and receive it.

The hash key matching unit 123, when the client computer 100 receives the hash key from the cloud server 200, communicates with the cloud server 200, the received hash key hash key stored in the hash key generator 210 Is verified, and if the hash key is the same, the identification module 140 proceeds with identification.

The identification module 140 identifies the non-identification analysis data using the mapping table 115 that was used when the original data was de-identification, and converts the non-identification analysis data into identification analysis data.

The analysis data display unit 150 displays the identification analysis data through the client computer 100.

At this time, the analysis data display unit 150 allows the identification analysis data to be displayed as a graph image, and more specifically, includes a function for allowing each data to be displayed as a variety of graph images, so that the user can easily analyze the data. So that you can check

In addition, the analysis data display unit 150 may directly check the analysis results on a web browser that can directly access the cloud server 200, such as Explorer, Google Chrome, Firefox, and the like, such as programs such as Excel and Word. You can also check the analysis results in conjunction.

When the data restoration module 160 needs to restore the original data, the cloud server 200 requests non-identification data transmission, and the cloud server 200 transmits the non-identification data stored in the data storage unit 220 to the client computer 100 ). Then, the client computer 100 requests the identification of the non-identification data transmitted to the identification module 140.

For example, when the original data is needed in the client computer 100, the data restoration module 160 is configured to receive the non-identifying data stored in the data storage unit 220 and perform identification to restore the original data.

At this time, the hash key request unit 120 requests the hash key input in the same way as when identifying the non-identification analysis data, and if it is determined that the received hash key is the same, the identification module 140 proceeds with identification Then, non-identifying data is identified and converted into original data.

Due to the above-described configurations of the present invention, even when the cloud server 200 exposes information to the outside due to external intrusion such as hacking, the mapping table 115 that has proceeded with the de-identification of data is the client computer 100. Because it is stored in the non-identifiable data cannot be identified, and hash key must be entered, so there is an effect of protecting information with double security.

2 is a flowchart of a method for safely storing and analyzing data in the cloud according to an embodiment of the present invention.

A flow for safely storing and analyzing data in the cloud will be described with reference to FIG. 2.

The client computer 100 requests a hash key to the cloud server 200. (S5010)

Since the de-identification module 130 needs a hash key to de-identify the original data using the mapping table 115, it requests the hash key to the cloud server 200.

Thereafter, the hash key generation unit 210 of the cloud server 200 generates and stores the hash key. (S5030)

Since the same hash key is required when the client computer 100 proceeds with identification later, it is desirable to store the hash key to retransmit the hash key.

Thereafter, the cloud server 200 transmits a hash key to the client computer 100. (S505)

Then, the client computer 100 verifies the received hash key. (S507) In more detail, the hash key matching unit 123 verifies the hash key received in the client computer 100 and the hash key stored in the hash key generator 210 of the cloud server 200. To do.

Then, when the hash key verification is completed, the de-identification module 130 de-identifies the original data using the mapping table 115. (S509) More specifically, the de-identification module 130 of the client computer 100 de-identifies the original data using the mapping table 115, but uses a specific linear function for numeric information among the original data. To de-identify, replace character type information in the original data with an arbitrary string to de-identify.

At this time, it is preferable that the mapping table generation unit 110 further includes the step of generating the mapping table 115 according to a preset mapping table 115 generation rule.

In addition, it is preferable that the de-identification module 130 further includes receiving data to be de-identified from the original data.

In addition, since the identification module 140 has to proceed with identification using the same mapping table 115 to identify the unidentified data, the client computer 100 used to de-identify the original data. The mapping table 115 is stored.

Thereafter, the client computer 100 transmits the non-identifying data to the cloud server 200. (S511)

Thereafter, the data storage unit 220 stores non-identification data transmitted from the client computer 100. (S513)

Thereafter, the data analysis unit 230 analyzes the non-identification data received from the client computer 100. (S515) More specifically, the data analysis unit 230 analyzes de-identified numeric information using the linear function of the mapping table 115.

Thereafter, the cloud server 200 transmits the non-identification analysis data to the client computer 100. (S517)

Then, the identification module 140 of the client computer 100 identifies the non-identification analysis data. (S519)

At this time, the hash key request unit 120 requests the hash key input to the client computer 100 before proceeding with identification.

If a hash key exists in the client computer 100, the hash key is selected to cause the hash key matching unit 123 to perform the hash key verification, and if the client computer 100 does not have a hash key, the cloud server 200 Request the hash key to receive the hash key stored in the hash key generation unit 210.

Then, the hash key matching unit 123 verifies the transmitted hash key by communicating with the cloud server 200, and proceeds with identification.

Thereafter, the analysis data display unit 150 displays the identification analysis data through the client computer 100. (S521)

3 is an exemplary diagram illustrating de-identification of numeric information according to an embodiment of the present invention, and FIG. 4 is an exemplary diagram illustrating de-identification of character-type information according to an embodiment of the present invention. 5 is an exemplary view illustrating that the original data is de-identified and converted into non-identified data through FIGS. 3 and 4.

The de-identification module 130 according to an embodiment of the present invention de-identifies using the mapping table 115, but de-identifies by calculating numerical information through a linear function and converts character information into an arbitrary string. Instead, it is de-identified.

At this time, the linear function uses the first order function of Y = aX or Y = aX + b, and the use of b allows the practitioner of the present invention to easily select. Here, X means original data as an independent variable, and Y means non-identifying data as a dependent variable whose value varies according to a, b, and X.

Here, it is preferable to set the value of a to an exponent of 2 so that the remaining error (Round-off Error) does not occur. When performing de-identification for data analysis, b is not used, or the value of b is the value of a. Using a significantly smaller value is effective in reducing errors in the analysis results.

Accordingly, the mapping table generation unit 110 generates the mapping table 115 according to the preset mapping table 115 generation rule, and the mapping table 115 generation rule is a first order function of Y = aX + b, a Let the value of be an exponent of 2.

With reference to FIG. 3, it will be described to de-identify numeric information among data of A.

1. As a mapping table 115 for the annual salary, a linear function of Y = 0.000625X-0.8125 (a = 0.000625, b = -0.8125) is used.

2. As a mapping table 115 for performance, a linear function of Y = 0.322580645X-2.064516129 (a = 0.322580645, b = -2.064516129) is used.

In the above embodiment, the mapping table generation unit generates a mapping table 115 of "Y = 0.000625X-0.8125" for an annual salary according to a preset mapping table generation rule, and a mapping of "Y = 0.322580645X-2.064516129" for performance. The table 115 is illustrated.

When the de-identification module 130 performs de-identification using each mapping table 115 as described above, it is converted to the non-identification data as shown in FIG. 3. (Example: Honggil-dong's annual salary is 0.000625 × 1,500 -0.8125 = 0.125, and pigeon's annual salary is 0.000625 × 1,300-0.8125 = 0)

As such, when the de-identification module 130 proceeds with de-identification, and when the identification module 140 proceeds with identification, the linear function of the mapping table 115 used when de-identification is performed is maintained. Using the a, b, and Y values to calculate the X value, data identification is completed.

Referring to FIG. 4, description will be made of de-identifying character type information among data of A.

Character type information often means the title and table of contents of numeric information such as name, job title, department, salary, performance, etc., and is not subject to direct numerical analysis. To proceed.

For example, the de-identification module 130, as shown in FIG. 4, annual salary: AASS $ # TG # 231, performance: SKEJL # $ SD343s, rank: 34JLDRT *) ($ # R, department: FALJ $ # ($% US3, Employee: ACD, Agency: $ SJ3, IT:? SE #, Marketing: Convert to JS3R to de-identify.

In addition, when the de-identification module 130 performs de-identification of names, de-identification is performed in the same manner.

As such, when the de-identification module 130 proceeds with de-identification, and when the identification module 140 proceeds with identification, the mapping table 115 used when proceeding with de-identification is loaded and the variable name Replace the original data to complete the data identification.

As described above, when de-identification of numeric information and character-type information for data of A is performed, non-identification data as shown in FIG. 5 is generated.

The method for safely storing and analyzing data according to an embodiment of the present invention described above in the cloud may be implemented as a program (or application) to be executed in combination with a hardware server, and stored in a medium.

The above-described program is C, C ++, JAVA, machine language, etc., in which a processor (CPU) of the computer can be read through a device interface of the computer in order for the computer to read the program and execute the methods implemented as a program. It may include a code (Code) coded in the computer language of. Such code may include functional code related to a function defining functions required to execute the above methods, and control code related to an execution procedure necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, the code may further include a memory reference related code to which location (address address) of the internal or external memory of the computer should be referred to additional information or media necessary for the computer's processor to perform the functions. have. Also, when the processor of the computer needs to communicate with any other computer or server in the remote to execute the functions, the code can be used to communicate with any other computer or server in the remote using the communication module of the computer. It may further include a communication-related code for whether to communicate, what information or media to transmit and receive during communication, and the like.

The storage medium refers to a medium that stores data semi-permanently and that can be read by a device, rather than a medium that stores data for a short moment, such as registers, caches, and memory. Specifically, examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the program may be stored in various recording media on various servers that the computer can access or various recording media on the user's computer. In addition, the medium may be distributed over a computer system connected through a network to store computer-readable codes in a distributed manner.

The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, a software module executed by hardware, or a combination thereof. The software modules may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer readable recording medium well known in the art.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but a person skilled in the art to which the present invention pertains may implement the present invention in other specific forms without changing its technical spirit or essential features. You will understand. Therefore, it should be understood that the above-described embodiments are illustrative in all respects and not restrictive.

100: client computer
110: mapping table generation unit
115: mapping table
120: hash key request
123: hash key matching unit
130: de-identification module
140: identification module
150: analysis data display unit
160: data restoration module
200: cloud server
210: hash key generation unit
220: data storage
230: data analysis unit

Claims (9)

Generating, by the client computer, unidentified data by de-identifying the original data using the mapping table;
The client computer transmitting the non-identifying data to a cloud server;
The cloud server storing and analyzing the non-identification data to generate non-identification analysis data;
The client computer receiving the non-identifying analysis data from the cloud server; And
And the client computer identifying the non-identification analysis data using the mapping table and converting the non-identification analysis data into identification analysis data.
The non-identifying data generation step,
De-identifying the numeric information among the original data using a specific linear function of the mapping table; And
A method of storing and analyzing data in the cloud for preventing external leakage of important information, including the step of de-identifying by replacing character-type information among the original data with an arbitrary string in the mapping table.
A client computer requesting a hash key for de-identification to the cloud server;
Generating and storing a hash key by the cloud server, and transmitting the hash key to the client computer;
Verifying the hash key received by the client computer communicating with the cloud server;
If it is determined that the hash keys match according to the verification result, the client computer de-identifying the original data using the mapping table to generate non-identifying data;
The client computer transmitting the non-identifying data to the cloud server;
The cloud server storing and analyzing the non-identification data to generate non-identification analysis data;
The client computer receiving the non-identifying analysis data from the cloud server;
Requesting, by the client computer, a hash key to proceed with identification to a cloud server;
The cloud server transmitting the hash key stored in the hash key storage unit to the client computer;
Verifying the hash key received by the client computer communicating with the cloud server; And
If it is determined that the hash key is matched according to the verification result, the client computer using the mapping table to identify the non-identifying analysis data and converting it into identification analysis data, for preventing the leakage of important information to the outside How to store and analyze data in the cloud.
Generating, by the client computer, unidentified data by de-identifying the original data using the mapping table;
The client computer transmitting the non-identifying data to a cloud server;
The cloud server storing and analyzing the non-identification data to generate non-identification analysis data;
Receiving, by the client computer, non-identification analysis data from the cloud server; And
And the client computer identifying the non-identification analysis data using the mapping table and converting the non-identification analysis data into identification analysis data.
When original data restoration is necessary,
Requesting the client computer to transmit non-identifying data to the cloud server; And
The client computer receives the non-identification data from the cloud server, and further comprising the step of identifying the non-identification data received from the cloud server using the mapping table, in the cloud for preventing external leakage of important information Methods of data storage and analysis.
The method according to any one of claims 1 to 3,
The non-identifying data generation step,
The mapping table generation unit further comprises the step of generating a mapping table according to a preset mapping table generation rule, data storage and analysis method in the cloud to prevent leakage of important information.
The method according to any one of claims 1 to 3,
The non-identifying data generation step,
Further comprising the step of selecting the data to be de-identified from the original data, data storage and analysis method in the cloud to prevent leakage of important information.
The method according to any one of claims 1 to 3,
After the step of converting to the identification analysis data,
The client computer further comprising the step of displaying the identification analysis data as a graph image, data storage and analysis method in the cloud to prevent leakage of important information.
A data storage and analysis program in the cloud for preventing external leakage of important information stored in a medium in order to execute the method of any one of claims 1 to 3 in combination with a computer that is hardware. delete delete

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