CN114611127A - Database data security management system - Google Patents
Database data security management system Download PDFInfo
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- CN114611127A CN114611127A CN202210252910.3A CN202210252910A CN114611127A CN 114611127 A CN114611127 A CN 114611127A CN 202210252910 A CN202210252910 A CN 202210252910A CN 114611127 A CN114611127 A CN 114611127A
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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
- G06F21/606—Protecting data by securing the transmission between two devices or processes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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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
- G06F21/6227—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 where protection concerns the structure of data, e.g. records, types, queries
Abstract
The invention provides a database data security management system, which comprises a public library, a protection library, a security transmission channel and a cluster mapping service module, wherein the public library is used for storing data which can be accessed by all users, the protection library is used for storing data which can be accessed by a specific user, the security transmission channel is used for transporting the data in the protection library, and the cluster mapping service module is used for carrying out security processing on the data in the protection library; the system obtains the clustering data after processing the data in the protection library through the clustering model in a mapping mode and then outputs the clustering data to the outside instead of presenting the original data, so that the system has safety, and the clustering model is continuously optimized through the data in the public library, so that the output clustering data can more accurately embody the characteristics of the original data.
Description
Technical Field
The invention relates to the field of data processing, in particular to a database data security management system.
Background
The core and key of database security is data security, which means that the integrity, confidentiality, availability, controllability and auditability of data are ensured by protective measures, because the database stores a large amount of important information and confidential data, and a large amount of data is stored in the database system in a centralized manner for sharing by multiple users, some of the data is not directly open to all users, but when general users are allowed to obtain the characteristics of the data, a special security management system is needed to realize the function.
A number of data management systems have been developed, and through extensive search and reference, it has been found that existing authorization systems, such as those disclosed in KR1020170034963A, KR1020010003094A, CN104662870B and KR1020090024336A, include: a security server configured to store an encryption key for encrypting a file or any data and a decryption key for decrypting the file or data; a first computing device configured to send an access authorization list carrying authorization restrictions to the security server, to request an encryption key from the security server, and to encrypt the file or data with the encryption key received from the security server; a second computing device configured to request a decryption key from the secure server and decrypt an encrypted file using the decryption key received from the secure server; and cloud storage configured to share the file between a first user using the first computing device and a second user using the second computing device. However, data in the system can only be completely acquired or not acquired according to the identity of the user, and the effect that an ordinary user can obtain reliable characteristics of corresponding data on the premise of not directly acquiring original data cannot be achieved.
Disclosure of Invention
The invention aims to provide a database data security management system aiming at the defects.
The invention adopts the following technical scheme:
a database data security management system comprises a public library, a protection library, a security transmission channel and a cluster mapping service module, wherein the public library is used for storing data which can be accessed by all users, the protection library is used for storing data which can be accessed by a specific user, the security transmission channel is used for transporting the data in the protection library, and the cluster mapping service module is used for performing security processing on the data in the protection library;
the cluster mapping service module comprises a cluster training manager and a cluster model, the cluster training manager optimizes the cluster model through data in the public library, the cluster model is transmitted to the protection library through the safe transmission channel, when a common user submits a request for accessing data in the protection library, the cluster model processes the corresponding data to obtain cluster data, and the cluster data are transmitted to the corresponding common user through the safe transmission channel;
the cluster model has k centroids, each centroid having n elements, with bj_iThe ith dimension element, i ∈ {1, 2, 3, …, n }, j ∈ {1, 2, 3, …, k }, β ∈ representing the jth centroid in the cluster modelj_iI-th weight coefficient representing j-th centroid, bj_iAnd betaj_iModel parameters for constructing a model;
the cluster model processes the accessed raw data into n-dimensional cluster particles using { (a)1，a2，…，an)mDenotes where m is the number of particles, aiFor the ith dimension element of each particle, then calculate the distance Δ s between the mass point and the mass centerj：
If the distance between a certain mass point and the jth mass center is the closest, dividing the mass point into jth mass points;
the cluster model obtains a clustering vector Y (N) according to the classification of particles1，N2，…，Nk) Wherein N isiRepresenting the number of the ith particles, and normalizing Y to obtain clustering data Y';
further, the cluster training manager comprises a data preprocessor and a model optimizer, wherein the data preprocessor is used for defining various preprocessing rules for specific types of data items, the specific types of data items comprise area identifiers, time stamps and partition identifiers, the preprocessing rules replace the specific types of data items by placeholder values, the preprocessing rules can be transmitted to the protection library, and when the data in the protection library is preprocessed by using the same preprocessing rules before being processed by the cluster model;
further, the model optimizer comprises a cluster analyzer and a model evaluator, wherein the cluster analyzer is used for modifying the model, the model evaluator is used for evaluating the model, the cluster analyzer can implement a clustering technique to obtain new data and output the new data to the updated cluster model for training, the updated cluster model is tested by using the test data set, the updated cluster model outputs regeneration data corresponding to the test data set, the model evaluator compares the regenerated regeneration data with the test data set and determines whether the model is improved, if the model is improved, the regeneration data is exported to a protection library for data analysis, and if the model is not improved, the model parameters are modified and are reused for model training;
furthermore, a safety query unit is arranged in the protection library, and the safety query unit can identify the identity of the user sending the request and search corresponding data in the protection library according to the request;
further, the security query unit includes a user registry, a data search component, and a security transmission component, where the user registry records user information of a specific user, the data search component can search corresponding data in the protection library according to a cluster identifier in a request, and the security transmission component determines to send a data index to the cluster model or the security transmission channel according to a comparison result between the request user and the user registry.
The beneficial effects obtained by the invention are as follows:
the system adopts the cluster model to obtain the data characteristics in the protection library, and the cluster model has similar processing effect when processing the same type of data in the public library and the protection library, so that the cluster model is optimized and improved through the data in the public library, the data in the protection library is more reliable after being processed by the cluster model, and meanwhile, the data in the protection library is not exposed to common users.
For a better understanding of the features and technical content of the present invention, reference should be made to the following detailed description of the invention and accompanying drawings, which are provided for purposes of illustration and description only and are not intended to limit the invention.
Drawings
FIG. 1 is a schematic view of the overall structural framework of the present invention;
FIG. 2 is a schematic diagram illustrating the working principle of the cluster mapping service module according to the present invention;
FIG. 3 is a schematic diagram of a model optimizer optimization framework of the present invention;
FIG. 4 is a schematic diagram of a placeholder rule handling mechanism according to the present invention;
FIG. 5 is a schematic flow chart of the data in the security access protection library according to the present invention.
Detailed Description
The following is a description of embodiments of the present invention with reference to specific embodiments, and those skilled in the art will understand the advantages and effects of the present invention from the disclosure of the present specification. The invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the spirit and scope of the present invention. The drawings of the present invention are for illustrative purposes only and are not drawn to scale. The following embodiments will further explain the related art of the present invention in detail, but the disclosure is not intended to limit the scope of the present invention.
The first embodiment.
The embodiment provides a database data security management system, which, with reference to fig. 1, includes a public library, a protection library, a secure transmission channel, and a cluster mapping service module, where the public library is used to store data that can be accessed by all users, the protection library is used to store data that can be accessed by a specific user, the secure transmission channel is used to transmit data in the protection library, and the cluster mapping service module is used to perform security processing on data in the protection library;
the cluster mapping service module comprises a cluster training manager and a cluster model, the cluster training manager optimizes the cluster model through data in the public library, the cluster model is transmitted to the protection library through the safe transmission channel, when a common user submits a request for accessing data in the protection library, the cluster model processes the corresponding data to obtain cluster data, and the cluster data are transmitted to the corresponding common user through the safe transmission channel;
the cluster model has k centroids, each centroid having n elements, with bj_iThe ith dimension element, i ∈ {1, 2, 3, …, n }, j ∈ {1, 2, 3, …, k }, β ∈ representing the jth centroid in the cluster modelj_iI-th weight coefficient representing j-th centroid, bj_iAnd betaj_iModel parameters for constructing a model;
the cluster model processes the accessed raw data into n-dimensional cluster particles using { (a)1，a2，…，an)mDenotes where m is the number of particles, aiFor the ith dimension element of each particle, then calculate the distance Δ s between the mass point and the mass centerj：
If the distance between a certain mass point and the jth mass center is the closest, dividing the mass point into jth mass points;
the cluster model obtains a clustering vector Y (N) according to the classification of particles1，N2，…，Nk) Wherein N isiRepresenting the number of the ith particles, and normalizing Y to obtain clustering data Y';
the cluster training manager comprises a data preprocessor and a model optimizer, wherein the data preprocessor is used for defining various preprocessing rules for specific types of data items, the specific types of data items comprise area identifiers, time stamps and partition identifiers, the preprocessing rules replace the specific types of data items by using placeholder values, the preprocessing rules can be transferred into a protection library, and when data in the protection library is preprocessed by using the same preprocessing rules before being processed by a cluster model;
the model optimizer comprises a cluster analyzer and a model evaluator, wherein the cluster analyzer is used for modifying the model, the model evaluator is used for evaluating the model, the cluster analyzer can implement a clustering technology to obtain new data and output the new data to an updated cluster model for training, the updated cluster model is tested by using a test data set, the updated cluster model outputs regeneration data corresponding to the test data set, the model evaluator compares the regenerated regeneration data with the test data set and determines whether the model is improved, if the model is improved, the regeneration data is exported to a protection library for data analysis, and if the model is not improved, model parameters are modified and reused for model training;
a safety query unit is arranged in the protection library, and can identify the identity of the user sending the request and search corresponding data in the protection library according to the request;
the security query unit comprises a user registry, a data search component and a security transmission component, wherein user information of a specific user is recorded in the user registry, the data search component can search corresponding data in the protection library according to a cluster identifier in a request, and the security transmission component determines to send a data index to the cluster model or the security transmission channel according to a comparison result of the request user and the user registry.
Example two.
The embodiment includes the whole content of the first embodiment, and provides a database data security management system, which includes a public library, a protection library, a security transmission channel and a cluster mapping service module, wherein the public library is used for storing data that can be accessed by all users, the protection library is used for storing data that can be accessed by a specific user, the security transmission channel is used for transporting the data in the protection library, and the cluster mapping service module is used for performing security processing on the data in the protection library;
with reference to fig. 2, the cluster mapping service module includes a cluster training manager and a cluster model, where the cluster mapping manager can receive training data, where the training data includes log files and data in a public library, the cluster mapping manager can train and optimize a cluster mass point of the training data on the cluster model, the cluster model is output to a protection library through the secure transmission channel after being optimized, data in the protection library is processed by the cluster model to obtain cluster data, and the cluster data is output to the outside through the secure transmission channel;
the cluster training manager comprises a data preprocessor and a model optimizer, wherein the data preprocessor is used for defining various preprocessing rules for specific types of data items, the specific types of data items comprise area identifiers, time stamps and partition identifiers, the preprocessing rules replace the specific types of data items by using placeholder values, the preprocessing rules can be transferred into a protection library, and when data in the protection library is preprocessed by using the same preprocessing rules before being processed by a cluster model;
the placeholder values are used for identifying configuration errors of data items in the public library, when partitions in the public library regenerate data items, the placeholder identifiers are replaced by the placeholder values, different partitions are associated with different configuration data, the configuration data of the partition A is applied to resources of the partition B and can cause errors, when one of the errors generates a log file or other data items, the area identifiers are checked to determine whether correct area identifiers are included in the log file, as a check before data training is performed, and if the wrong area identifiers are detected, the data preprocessor generates a notification indicating that the configuration file needs to be updated;
the model optimizer comprises a cluster analyzer and a model evaluator, wherein the cluster analyzer is used for changing the model, and the model evaluator is used for evaluating the model, and the specific optimization mode is as follows by combining with fig. 3:
the cluster analyzer can implement a clustering technique to obtain new data and output the new data to an updated cluster model for training, the updated cluster model is tested by using a test data set, the updated cluster model outputs regeneration data corresponding to the test data set, a model evaluator compares the regenerated regeneration data with the test data set and determines whether the model is improved, if the model is improved, the regeneration data is exported to a protection library for data analysis, and if the model is not improved, model parameters are modified and reused for model training;
the cluster model has k centroids, each centroid having n elements, with bj_iThe ith dimension element, i ∈ {1, 2, 3, …, n }, j ∈ {1, 2, 3, …, k }, β ∈ representing the jth centroid in the cluster modelj_iI-th weight coefficient representing j-th centroid, bj_iAnd betaj_iModel parameters to be optimized in the model optimizer;
in connection with FIG. 4, partial data items that vary or differ between protected and unprotected data items may be replaced with placeholder values to make the clustering model more accurate, pre-processing is performed using placeholder rules defined by a user manually viewing the unprotected data items to identify fields, columns, or other portions of the data items that include information that may vary, replacing them with placeholder values as secondary information may vary, the clustering model identifying clusters based on information in the data items that is more useful to the task being performed, the placeholder rules including manual rules generated by the user and a machine learning model trained to identify and replace portions of the data items, processed unprotected data being generated once the unprocessed unprotected data is pre-processed using the placeholder rules, the processed data is used for training a cluster model;
passing placeholder rules to the protected area through a secure transport service, preprocessing unprocessed protected data into processed protected data using the placeholder rules, analyzing the processed data using a cluster model, the placeholder rules comprising a list of data parts to be replaced and placeholder IDs for mapping identifiers to placeholder values;
with reference to fig. 5, the process of securing access to data in the protected library includes the following steps:
s1, the system receives a request of a user for acquiring one or more data items in the protection library;
s2, the system sends a request into a protection library, a cluster model in the protection library identifies a cluster identifier associated with each of the one or more data items;
s3, the cluster model reads corresponding data in a protection library according to the cluster identifier;
s4, preprocessing the read corresponding data by the cluster model according to a preprocessing rule;
s5, the cluster model processes the preprocessed data to obtain cluster data;
s6, transmitting the clustering data to a corresponding user through the secure transmission channel;
the public repository and the protection repository are each divided into a plurality of partitions, each partition being logically isolated from the other partitions, each partition being a distinct logical data center supported by one or more physical data centers, and each partition being supported by its own power supply and network infrastructure, thereby preventing a condition affecting one partition in the event of a failure of another partition;
the protection library is internally provided with a safety query unit, the safety query unit can identify the identity of a user sending a request and search corresponding data in the protection library according to the request, for a specific user, the safety query unit directly sends the searched data to the corresponding specific user through the safety transmission channel, for a common user, the safety query unit sends an index address of the searched data to the cluster model, and the cluster model reads and processes the data and then sends the data to the corresponding common user through the safety transmission channel;
the security query unit comprises a user registry, a data search component and a security transmission component, wherein the user registry records user information of a specific user and can add, delete and modify the user information according to a received instruction, the data search component can search corresponding data in the protection library according to a cluster identifier in a request, and the security transmission component determines to trigger a data index to the cluster model or the security transmission channel according to a comparison result of the request user and the user registry;
the process of processing the original data in the protection library by the cluster model to obtain the clustering data comprises the following steps:
s21, converting the preprocessed original data into n-dimensional particles to obtain cluster particles (a)1，a2，…，an)mDenotes where m is the number of particles, aiThe ith dimension element of each particle;
s22, the cluster model calculates the distance deltas between the cluster particles and the centroid in the cluster model according to the following formulaj：
If the distance between a certain mass point and the jth mass center is the closest, dividing the mass point into jth mass points;
s23, counting the classified number of particles of all clusters to obtain data NiIndicating the number of particles belonging to the i-th class;
s24, obtaining the clustering vector Y (N) according to the number of classifications1，N2，…，Nk) And carrying out normalization treatment to obtain Y';
y' obtained in step S24 is output as cluster data to the outside of the protection library.
The disclosure is only a preferred embodiment of the invention, and is not intended to limit the scope of the invention, so that all equivalent technical changes made by using the contents of the specification and the drawings are included in the scope of the invention, and further, the elements thereof can be updated as the technology develops.
Claims (5)
1. The database data security management system is characterized by comprising a public library, a protection library, a security transmission channel and a cluster mapping service module, wherein the public library is used for storing data which can be accessed by all users, the protection library is used for storing data which can be accessed by a specific user, the security transmission channel is used for transporting the data in the protection library, and the cluster mapping service module is used for performing security processing on the data in the protection library;
the cluster mapping service module comprises a cluster training manager and a cluster model, the cluster training manager optimizes the cluster model through data in the public library, the cluster model is transmitted to the protection library through the safe transmission channel, when a common user submits a request for accessing data in the protection library, the cluster model processes the corresponding data to obtain cluster data, and the cluster data are transmitted to the corresponding common user through the safe transmission channel;
the cluster model has k centroids, each centroid having n elements, with bj_iThe ith dimension element, i ∈ {1, 2, 3, …, n }, j ∈ {1, 2, 3, …, k }, β ∈ representing the jth centroid in the cluster modelj_iI-th weight coefficient representing j-th centroid, bj_iAnd betaj_iModel parameters for constructing a model;
the cluster model processes the accessed raw data into n-dimensional cluster particles using { (a)1，a2，…，an)mDenotes where m is the number of particles, aiFor the ith dimension element of each particle, then calculate the distance Δ s between the mass point and the mass centerj：
If the distance between a certain mass point and the jth mass center is the closest, dividing the mass point into jth mass points;
the cluster model obtains a clustering vector Y (N) according to the classification of particles1，N2，…，Nk) Wherein N isiAnd expressing the number of the ith particles, and normalizing Y to obtain clustering data Y'.
2. The database data security management system of claim 1, wherein the cluster training manager comprises a data pre-processor and a model optimizer, wherein the data pre-processor is configured to define various pre-processing rules for certain types of data items, the certain types of data items including region identifiers, timestamps, and partition identifiers, the pre-processing rules replace the certain types of data items with placeholder values, and the pre-processing rules can be passed to the protection repository and data in the protection repository is pre-processed using the same pre-processing rules before being processed with the cluster model.
3. The database data security management system of claim 2, wherein the model optimizer comprises a cluster analyzer and a model evaluator, the cluster analyzer is configured to modify the model, the model evaluator is configured to evaluate the model, the cluster analyzer is configured to perform clustering to obtain new data and output the new data to the updated cluster model for training, the updated cluster model is tested using the test dataset, the updated cluster model outputs regeneration data corresponding to the test dataset, the model evaluator compares the regenerated regeneration data with the test dataset and determines whether the model is improved, if the model is improved, the regenerated regeneration data is exported to a protection library for data analysis, and if the model is not improved, the model parameters are modified and reused for model training.
4. A database data security management system according to claim 3, characterized in that a security query unit is provided in the protection repository, said security query unit being capable of identifying the identity of the user sending the request and searching the protection repository for corresponding data upon request.
5. The database data security management system of claim 4, wherein the security query unit includes a user registry, a data search component and a security transmission component, the user registry records user information of a specific user, the data search component can search the corresponding data in the protection library according to the cluster identifier in the request, and the security transmission component determines to send a data index to the cluster model or the security transmission channel according to the comparison result between the requesting user and the user registry.
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