CN111625845A - Security management method, device and equipment for big data - Google Patents
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- 238000007726 management method Methods 0.000 title claims description 33
- 238000000586 desensitisation Methods 0.000 claims abstract description 70
- 238000000034 method Methods 0.000 claims abstract description 26
- 238000013507 mapping Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000003860 storage Methods 0.000 claims description 10
- 230000000873 masking effect Effects 0.000 claims description 5
- 238000004088 simulation Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 abstract description 8
- 238000012360 testing method Methods 0.000 abstract description 5
- 238000004458 analytical method Methods 0.000 abstract description 4
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- 230000004931 aggregating effect Effects 0.000 description 1
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
- G06F21/6254—Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
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Abstract
The embodiment of the invention discloses a method, a device and equipment for safety management of big data, wherein the method comprises the following steps: acquiring original data; judging whether the attribute of the original data accords with a preset desensitization data attribute and whether the attribute accords with a preset mixed-arranging data attribute; if the attribute of the desensitization data is met, desensitization data are obtained from the original data through a desensitization algorithm, and the original data and mapping relation information between the original data and the desensitization data are stored in a security island; and if the attribute of the mixed data is met, obtaining the mixed data by the original data through a mixed algorithm, and storing the original data and the mapping relation information between the original data and the mixed data in the safety island. The method and the system can ensure that the sensitive information cannot appear in daily development, test, data processing and analysis of a large data platform, ensure the same algorithm and program in the processes as those of the process with the sensitive information, and can acquire the corresponding sensitive information when the sensitive information is required.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data processing, in particular to a method, a device and equipment for safety management of big data.
Background
At present, the big data security technology mainly aims at the fields of big data storage security, authority/access security and big data sharing traceability. The related technical means is lacked aiming at the field of big data content security.
In the process of developing, testing, processing and analyzing big data, data processing personnel must use real data to process and use real data to analyze. The large number of people using real data for processing may cause data leakage and violate the requirements of laws and regulations. Such as: the cleaning of the identity card must take the complete set of the identity card, including all possible dirty data and clean data, to clean the identity card to ensure that the DT process is reliable, complete and effective, but the problem of data leakage exists. In addition, during data analysis, such as accurate marketing, real customer information is also required to be processed, so as to complete effective customer portrayal. How to use real data is an urgent problem to be solved on the premise of ensuring the security of big data.
Disclosure of Invention
The embodiment of the invention aims to provide a security management method, a security management device and security management equipment for big data, which are used for solving the problem that key data are easy to leak when the existing big data platform uses real data.
In order to achieve the above object, the embodiments of the present invention mainly provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides a method for security management of big data, including: acquiring original data; judging whether the attribute of the original data accords with a preset desensitization data attribute or not, and judging whether the attribute of the original data accords with a preset mixed-arranging data attribute or not; if the attribute of the original data accords with the preset desensitization data attribute, desensitization data are obtained through a desensitization algorithm on the original data, and the original data and mapping relation information between the original data and the desensitization data are stored in a security island; and if the attribute of the original data accords with the preset mixed arranging data attribute, obtaining mixed arranging data from the original data through a mixed arranging algorithm, and storing the original data and the mapping relation information between the original data and the mixed arranging data in the safety island.
According to an embodiment of the present invention, the shuffling algorithm is configured to randomly sort the original data by columns to generate the shuffled data.
According to one embodiment of the invention, the desensitization algorithm is used to perform at least one of masking desensitization, data simulation desensitization, generalization desensitization, and data consistency desensitization on the raw data.
According to an embodiment of the present invention, the shuffling algorithm is used for shuffling a data corpus of the original data or shuffling partial data of the original data.
In a second aspect, an embodiment of the present invention further provides a device for security management of big data, including: the acquisition module is used for acquiring original data; a security island; the processing module is used for judging whether the attribute of the original data meets a preset desensitization data attribute or not and judging whether the attribute of the original data meets a preset mixed-arranging data attribute or not; if the attribute of the original data accords with the preset desensitization data attribute, desensitization data are obtained through a desensitization algorithm on the original data, and the original data and mapping relation information between the original data and the desensitization data are stored in the security island; and if the attribute of the original data accords with the preset mixed arranging data attribute, obtaining mixed arranging data from the original data through a mixed arranging algorithm, and storing the original data and the mapping relation information between the original data and the mixed arranging data in the safety island.
According to an embodiment of the present invention, the shuffling algorithm is configured to randomly sort the original data by columns to generate the shuffled data.
According to one embodiment of the invention, the desensitization algorithm is used to perform at least one of masking desensitization, data simulation desensitization, generalization desensitization, and data consistency desensitization on the raw data.
According to an embodiment of the present invention, the shuffling algorithm is used for shuffling a data corpus of the original data or shuffling partial data of the original data.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the method for security management of big data according to the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium containing one or more program instructions, where the one or more program instructions are used to execute the security management method for big data according to the first aspect.
The technical scheme provided by the embodiment of the invention at least has the following advantages:
the method, the device and the equipment for safety management of the big data provided by the embodiment of the invention ensure that the sensitive information cannot appear in the development, test, data processing and analysis related to daily big data, simultaneously ensure the same algorithm and program in the processes as those of the process with the sensitive information, and can acquire the corresponding sensitive information when the sensitive information is required.
Drawings
Fig. 1 is a flowchart of a security management method for big data according to an embodiment of the present invention.
FIG. 2 is a body architecture diagram of a large data platform in accordance with one example of the present invention.
Fig. 3 is a schematic diagram of data in a security island in one example of the invention.
Fig. 4 is a diagram illustrating desensitization zone data in an example of the present invention.
Fig. 5(a) -5 (c) are schematic diagrams of mapping region data in an example of the present invention.
Fig. 6 is a block diagram of a security management apparatus for big data according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the description of the present invention, it is to be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as meaning directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Fig. 1 is a flowchart of a security management method for big data according to an embodiment of the present invention. As shown in fig. 1, the method for managing security of big data according to the embodiment of the present invention includes:
s1: raw data is acquired.
In particular, the raw data is obtained from a data source, which may be a batch or real-time. The original data is the real data.
S2: and judging whether the attribute of the original data accords with the preset desensitization data attribute or not, and judging whether the attribute of the original data accords with the preset mixed-arranging data attribute or not.
Specifically, after the original data are collected, it is judged according to a preset desensitization rule and a shuffling rule which attribute data need to be desensitized and which attribute data need to be shuffled.
S3: and if the attribute of the original data accords with the preset desensitization data attribute, desensitization data are obtained from the original data through a desensitization algorithm, and the original data and mapping relation information between the original data and the desensitization data are stored in the security island. The safety island is arranged in the large data platform as a special safety control area or in a storage device independent of the large data platform. The patch source region may be generated simultaneously with the security island data or asynchronously.
S4: if the attribute of the original data accords with the preset attribute of the mixed data, obtaining the mixed data by the mixed data algorithm for the original data, and storing the original data and the mapping relation information between the original data and the mixed data in the safety island;
it should be noted that, the present invention does not limit the sequential execution relationship between step S3 and step S4, that is, step S3 may be executed first, and then step S4 may be executed; step S4 may be executed first, and then step S3 may be executed; it is also possible to simultaneously perform step S3 and step S4.
FIG. 2 is a body architecture diagram of a large data platform in accordance with one example of the present invention. As shown in fig. 2, for batch or real-time data, when data is read from a data source and enters a pasting layer, all data entering a security island through a static desensitization/shuffling/mapping/desensitization API is desensitized data, and is mainly implemented by a desensitization or shuffling algorithm; and the data of the entering safety island is a mapping table of the data before desensitization and the data before and after desensitization.
The shuffling algorithm is a method for processing sensitive and single-column data of a database, and is mainly characterized in that a data set is not changed, but data are rearranged randomly, for example: the names in the database are: zhang three, Li four, Zhao five, only the ordering of the data has changed after processing, has changed into: li four, Zhao five and Zhang three.
The mixed arrangement method comprises two methods: 1. and carrying out mixed arrangement on the data complete set. 2. And mixing and arranging partial data. Such as: every 1000 pieces of data are mixed and arranged in the 1000 pieces of data.
The advantages of mixed row are: the algorithm integrity of the cleaning of the data processing is not influenced: examples are, for example: the membership card number washes 1112222, 111222. The cleaning rules include two types: and rotating the full angle to the half angle to remove unnecessary blank spaces. The distribution of the data is not affected: if the transaction amount is mixed, the distribution of the transaction amount is not influenced.
Data cleaning and communication: and (3) carrying out cleaning, standardization and communication operations on the data by developers, completing development, testing, processing and analysis in a blue area, and generating an ODS layer.
Cleaning a safety island: and processing the data of the safety island by using a program generated in the data cleaning and communicating step, and generating an ODS layer of the safety island.
And (3) data aggregation: and aggregating the data output to the ODS layer by the developer, and outputting to a data aggregation layer.
Summary analytical results: the aggregate layer data is used to generate the desired results.
For individual-specific customer portraits (scenes where sensitive data is certainly needed): associating data of the safety area, the reverse mapping area and the desensitization area to obtain real sensitive information, such as: customer data, customer track, customer biometric information, etc.
Fig. 3 is a schematic diagram of data in a security island in one example of the invention. As shown in fig. 3, the original data includes four sets of data, four sets of data having the names of zhang san, li si, zhao wu, and wang xi from top to bottom.
Fig. 4 is a diagram illustrating desensitization zone data in an example of the present invention. As shown in fig. 4, the desensitization region data includes four sets of data, four sets of data named person four, person six, person three, and person five from top to bottom.
Fig. 5(a) -5 (c) are schematic diagrams of mapping region data in an example of the present invention. As shown in fig. 5(c) to (a), the mapping areas store the mapping relationship between names, the mapping relationship between id cards, and the mapping relationship between addresses before and after shuffling, respectively.
The safety management method for the big data provided by the embodiment of the invention ensures that the sensitive information cannot appear in daily development, test, data processing and analysis of the big data, simultaneously ensures the same algorithm and program in the processes as those with the sensitive information, and can acquire the corresponding sensitive information when the sensitive information is required.
Fig. 6 is a block diagram of a security management apparatus for big data according to an embodiment of the present invention. As shown in fig. 6, the security management apparatus for big data according to the embodiment of the present invention includes: acquisition module 100, security island 200, and processing module 300.
The obtaining module 100 is configured to obtain raw data.
The processing module 300 is configured to determine whether the attribute of the original data meets a preset desensitization data attribute, and determine whether the attribute of the original data meets a preset shuffling data attribute. If the attribute of the original data accords with the preset desensitization data attribute, desensitization data are obtained through a desensitization algorithm on the original data, the original data are stored in the security island, and mapping relation information between the original data and the desensitization data is stored; and if the attribute of the original data accords with the preset mixed arranging data attribute, obtaining mixed arranging data from the original data through a mixed arranging algorithm, and storing the original data and the mapping relation information between the original data and the mixed arranging data in the safety island.
According to one embodiment of the invention, the shuffling algorithm is used for randomly sorting the original data by columns to generate shuffled data.
According to one embodiment of the invention, a desensitization algorithm is used to perform at least one of masking desensitization, data simulation desensitization, generalization desensitization, and data consistency desensitization on the raw data.
According to an embodiment of the present invention, the shuffling algorithm is used for shuffling a data corpus of original data or shuffling partial data of the original data.
It should be noted that, a specific implementation of the big data security management apparatus in the embodiment of the present invention is similar to a specific implementation of the big data security management method in the embodiment of the present invention, and specific reference is specifically made to the description of the big data security management method, and details are not repeated for reducing redundancy.
In addition, other configurations and functions of the big data security management apparatus according to the embodiment of the present invention are known to those skilled in the art, and are not described in detail for reducing redundancy.
An embodiment of the present invention further provides an electronic device, including: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the method for security management of big data according to the first aspect.
The disclosed embodiments of the present invention provide a computer-readable storage medium, in which computer program instructions are stored, and when the computer program instructions are run on a computer, the computer is caused to execute the above-mentioned security management method for big data.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (ddr Data Rate SDRAM), enhanced SDRAM (enhanced SDRAM, ESDRAM), synclink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in the embodiments of the invention are intended to comprise, without being limited to, memory of these and any other suitable attributes.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. A security management method for big data is characterized by comprising the following steps:
acquiring original data;
judging whether the attribute of the original data accords with a preset desensitization data attribute or not, and judging whether the attribute of the original data accords with a preset mixed-arranging data attribute or not;
if the attribute of the original data accords with the preset desensitization data attribute, desensitization data are obtained through a desensitization algorithm on the original data, and the original data and mapping relation information between the original data and the desensitization data are stored in a security island;
and if the attribute of the original data accords with the preset mixed arranging data attribute, obtaining mixed arranging data from the original data through a mixed arranging algorithm, and storing the original data and the mapping relation information between the original data and the mixed arranging data in the safety island.
2. The big data security management method according to claim 1, wherein the shuffling algorithm is configured to randomly sort the original data by columns to generate the shuffling data.
3. The big data security management method according to claim 1, wherein the desensitization algorithm is used for performing at least one of masking desensitization, data simulation desensitization, generalization desensitization, and data consistency desensitization on the original data.
4. The big data security management method according to claim 1 or 2, wherein the shuffling algorithm is used for shuffling a data corpus of the original data or shuffling partial data of the original data.
5. A big data security management device is characterized by comprising:
the acquisition module is used for acquiring original data;
a security island;
the processing module is used for judging whether the attribute of the original data meets a preset desensitization data attribute or not and judging whether the attribute of the original data meets a preset mixed-arranging data attribute or not; if the attribute of the original data accords with the preset desensitization data attribute, desensitization data are obtained through a desensitization algorithm on the original data, and the original data and mapping relation information between the original data and the desensitization data are stored in the security island; and if the attribute of the original data accords with the preset mixed arranging data attribute, obtaining mixed arranging data from the original data through a mixed arranging algorithm, and storing the original data and the mapping relation information between the original data and the mixed arranging data in the safety island.
6. The big data security management device according to claim 5, wherein the shuffling algorithm is configured to randomly sort the original data by columns to generate the shuffled data.
7. The big data security management device according to claim 5, wherein the desensitization algorithm is configured to perform at least one of masking desensitization, data simulation desensitization, generalization desensitization, and data consistency desensitization on the original data.
8. The big data security management device according to claim 5 or 6, wherein the shuffling algorithm is configured to shuffle a data corpus of the original data or shuffle partial data of the original data.
9. An electronic device, characterized in that the electronic device comprises: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor is used for executing one or more program instructions to execute the security management method of the big data according to any one of claims 1 to 4.
10. A computer-readable storage medium containing one or more program instructions for performing the method for security management of big data according to any one of claims 1 to 4.
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| CN115081544A (en) * | 2022-07-22 | 2022-09-20 | 国网浙江省电力有限公司 | Power grid equipment panoramic model data processing method based on multi-source data fusion |
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