CN117113402A - Data mining method, device, equipment and readable storage medium - Google Patents
Data mining method, device, equipment and readable storage medium Download PDFInfo
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- CN117113402A CN117113402A CN202311074599.9A CN202311074599A CN117113402A CN 117113402 A CN117113402 A CN 117113402A CN 202311074599 A CN202311074599 A CN 202311074599A CN 117113402 A CN117113402 A CN 117113402A
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- 238000007418 data mining Methods 0.000 title claims abstract description 120
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000004458 analytical method Methods 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 34
- 238000012549 training Methods 0.000 claims description 11
- 238000010606 normalization Methods 0.000 claims description 5
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 238000003066 decision tree Methods 0.000 claims description 4
- 238000000611 regression analysis Methods 0.000 claims description 4
- 238000012706 support-vector machine Methods 0.000 claims description 4
- 238000004138 cluster model Methods 0.000 claims 1
- 230000008569 process Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013480 data collection Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012360 testing method Methods 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
- 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
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Abstract
The application discloses a data mining method, a device, equipment and a readable storage medium, which can be applied to the field of big data or the field of finance, wherein the data mining method comprises the following steps: receiving a data mining request, wherein the data mining request comprises identification information of target privacy distortion data; determining target privacy distortion data from a privacy distortion database according to the identification information, wherein the privacy distortion database comprises a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client; and inputting the target privacy distortion data into a target data mining model to obtain an analysis result of the target privacy distortion data. Therefore, when the data is mined, the application does not need to acquire complete privacy data from the client, but only performs data mining according to the privacy distortion data, so that even if the data is leaked, the data processing platform also distorts the data instead of the complete privacy data, thereby improving the security of the privacy data.
Description
Technical Field
The present application relates to the field of big data, and more particularly, to a data mining method, apparatus, device, and readable storage medium.
Background
With the popularization of the internet and the application of large data technology, large enterprises and service organizations have acquired a large amount of data, such as patient diagnosis data sets established by medical institutions, customer online transaction data sets collected by e-commerce enterprises, customer information and transaction data stored by financial companies, and the like. Enterprises or institutions analyze the data sets, can obtain more valuable knowledge, then provide better services for clients, and improve living standards. However, such data sets store a large amount of data related to personal privacy, such as identity information, address information, communication information, etc., and privacy revealing events are very likely to occur.
The existing method for improving the security of the private data is generally that a data processing platform which is expected to collect the private data for data mining can comply with laws and regulations, and does not actively reveal the private data. However, once the data processing platform leaks the private data, a serious threat is posed to the security of the private data.
Disclosure of Invention
The embodiment of the application provides a data mining method, a device, equipment and a readable storage medium, which can improve the security of private data.
In view of this, an embodiment of the present application provides a data mining method, including:
receiving a data mining request, wherein the data mining request comprises identification information of target privacy distortion data;
determining target privacy distortion data from a privacy distortion database according to the identification information, wherein the privacy distortion database comprises a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client;
and inputting the target privacy distortion data into a target data mining model to obtain an analysis result of the target privacy distortion data.
Optionally, the method further comprises:
receiving initial privacy distortion data sent by a client;
normalizing the initial privacy data to obtain privacy distortion data;
the privacy distortion data is stored in a privacy distortion database.
Optionally, the method further comprises:
determining an original data mining model;
acquiring privacy distortion data to be trained and an analysis result of the privacy distortion data to be trained;
and training the original data mining model according to the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained to obtain the target data mining model.
Optionally, the method further comprises:
acquiring privacy distortion data to be tested and an analysis result of the privacy distortion data to be tested;
inputting the privacy distortion data to be tested into the target data mining model to obtain an analysis result to be verified;
determining the accuracy of the target data mining model according to the analysis result to be verified and the analysis result of the privacy distortion data to be tested;
and if the accuracy is lower than a preset threshold, re-executing the step of determining the original data mining model.
The embodiment of the application also provides a data mining device, which comprises:
a receiving unit configured to receive a data mining request, where the data mining request includes identification information of target privacy distortion data;
the first determining unit is used for determining target privacy distortion data from a privacy distortion database according to the identification information, wherein the privacy distortion database comprises a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client;
and the input unit is used for inputting the target privacy distortion data into a target data mining model to obtain an analysis result of the target privacy distortion data.
Optionally, the apparatus further comprises:
the receiving unit is used for receiving the initial privacy distortion data sent by the client;
the processing unit is used for carrying out normalization processing on the initial privacy data to obtain privacy distortion data;
and the storage unit is used for storing the privacy distortion data in a privacy distortion database.
Optionally, the apparatus further comprises:
the second determining unit is used for determining an original data mining model;
the acquisition unit is used for acquiring the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained;
the training unit is used for training the original data mining model according to the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained to obtain the target data mining model.
The embodiment of the application also provides computer equipment, which comprises: a memory, a processor, and a bus system;
wherein the memory is used for storing programs;
the processor is used for executing the program in the memory so as to realize any one of the data mining methods;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
The embodiments of the present application also provide a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any of the data mining methods described above.
The embodiment of the application provides a data mining method, which comprises the following steps: receiving a data mining request, wherein the data mining request comprises identification information of target privacy distortion data; determining target privacy distortion data from a privacy distortion database according to the identification information, wherein the privacy distortion database comprises a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client; and inputting the target privacy distortion data into a target data mining model to obtain an analysis result of the target privacy distortion data. Therefore, when the data is mined, the application does not need to acquire complete privacy data from the client, but only performs data mining according to the privacy distortion data, so that even if the data is leaked, the data processing platform also distorts the data instead of the complete privacy data, thereby improving the security of the privacy data.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a data mining method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data mining apparatus according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
With the popularization of the internet and the application of large data technology, large enterprises and service organizations have acquired a large amount of data, such as patient diagnosis data sets established by medical institutions, customer online transaction data sets collected by e-commerce enterprises, customer information and transaction data stored by financial companies, and the like. Enterprises or institutions analyze the data sets, can obtain more valuable knowledge, then provide better services for clients, and improve living standards. However, such data sets store a large amount of data related to personal privacy, such as identity information, address information, communication information, etc., and privacy revealing events are very likely to occur.
The existing method for improving the security of the private data is generally that a data processing platform which is expected to collect the private data for data mining can comply with laws and regulations, and does not actively reveal the private data. However, once the data processing platform leaks the private data, a serious threat is posed to the security of the private data.
Therefore, in view of the above problems, embodiments of the present application provide a data mining method, apparatus, device, and readable storage medium, which can improve security of private data.
Referring to fig. 1, a data mining method provided by an embodiment of the present application is applicable to a data processing platform, and includes the following steps.
S101, receiving a data mining request.
In this embodiment, the data processing platform may first receive a data mining request sent by a user, so as to mine the data to be mined. It is understood that the data mining request includes identification information of the target privacy-distorting data. The user can send a data mining request containing identification information to the data processing platform through the terminal equipment to realize data mining on the target privacy distortion data.
S102, determining target privacy distortion data from a privacy distortion database according to the identification information, wherein the privacy distortion database comprises a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client.
In this embodiment, after receiving a data mining request including identification information, the data processing platform may determine target privacy distortion data from a privacy distortion database according to the identification information, where the privacy distortion database includes a plurality of privacy distortion data, where the privacy distortion data is obtained by performing distortion processing on the privacy data by a client. It is understood that the privacy distortion database for storing privacy distortion data may be constructed in advance. The client can randomly divide the complete privacy data of the user, and performs light and personalized distortion processing on each part to obtain corresponding distortion data, and then sends the distortion data to the data collection platform for storage and data mining processing. The data processing platform can only collect the privacy distortion data, but not collect the complete privacy data, so that the problem of privacy data safety caused by revealing the privacy data is avoided.
In one possible implementation, initial privacy-distorted data sent by a client may be received; normalizing the initial privacy data to obtain privacy distortion data; the privacy distortion data is stored in a privacy distortion database. It can be understood that, in order to facilitate better data mining and improve the data mining efficiency, preprocessing such as normalization processing can be performed on the initial privacy distortion data sent by the client, and used parameters are recorded to unify the format of the privacy distortion data, and finally, the privacy distortion data after normalization processing is stored in the privacy distortion database so as to be convenient for calling during data mining in the follow-up process.
S103, inputting the target privacy distortion data into a target data mining model to obtain an analysis result of the target privacy distortion data.
In this embodiment, after determining the target privacy distortion data, the target privacy distortion data may be input into the target data mining model, to obtain an analysis result of the target privacy distortion data. It can be appreciated that a target data mining model capable of performing data mining on the privacy distortion data may be pre-trained, so as to perform data mining on the target privacy distortion data, and obtain an analysis result of the target privacy distortion data, where the analysis result may include data association information and the like.
In one possible implementation, the original data mining model may be determined first; acquiring privacy distortion data to be trained and analysis results of the privacy distortion data to be trained; training the original data mining model according to the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained to obtain a target data mining model. It can be understood that an original data mining model can be determined from various data mining models, then a training data set comprising the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained is obtained to train the original data mining model, so that a target data mining model is obtained, data mining can be performed only according to the privacy distortion data, complete privacy data of a user is not required to be obtained, and the problem of privacy data safety caused by leakage of the privacy data by a data processing platform is avoided.
Specifically, the raw data mining model may include one or more of a regression analysis model, a decision tree model, an artificial neural network model, a bayesian network model, a support vector machine model, a clustering model, an association model, and the like. It can be understood that, in order to improve diversity and comprehensiveness of data mining, one or more models selected from a regression analysis model, a decision tree model, an artificial neural network model, a bayesian network model, a support vector machine model, a clustering model, an association model and the like can be selected as an original data mining model to train according to actual requirements, so that analysis results of data mining can contain various relevant information of data, and diversity and comprehensiveness of data mining are improved.
In one possible implementation manner, in order to further improve the accuracy of data mining, the privacy distortion data to be tested and the analysis result of the privacy distortion data to be tested may be obtained; inputting the privacy distortion data to be tested into a target data mining model to obtain an analysis result to be verified; determining the accuracy of the target data mining model according to the analysis result to be verified and the analysis result of the privacy distortion data to be tested; and if the accuracy is lower than the preset threshold value, the step of determining the original data mining model is re-executed. It can be understood that the trained target data mining model can be tested by acquiring test set data comprising privacy distortion data to be tested and privacy distortion data analysis results to be tested, and the accuracy of the target data mining model is determined by comparing the analysis results to be verified output by the target data mining model with the marked privacy distortion data analysis results to be tested; if the accuracy is higher than the preset threshold, the accuracy of the target data mining model is qualified, and the trained target data mining model can be directly used; and if the accuracy of the target data mining model is lower than the preset threshold, the accuracy of the target data mining model is unqualified, and at the moment, the model can be retrained by redefining the original data mining model or redefining the training data set until the accuracy of the trained target data mining model is higher than the preset threshold, so that the accuracy of the data mining is improved.
Therefore, in the embodiment of the application, the data mining method is provided, and the complete privacy data is not required to be obtained from the client side when the data mining is performed, but the data mining is performed only according to the privacy distortion data, so that even if the data is leaked, the data processing platform also distorts the data instead of the complete privacy data, and the security of the privacy data is improved.
Referring to fig. 2, the embodiment of the application further provides a data mining apparatus, which includes:
a receiving unit 201, configured to receive a data mining request, where the data mining request includes identification information of target privacy distortion data;
a first determining unit 202, configured to determine target privacy distortion data from a privacy distortion database according to the identification information, where the privacy distortion database includes a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client;
and an input unit 203, configured to input the target privacy distortion data into a target data mining model, and obtain an analysis result of the target privacy distortion data.
Optionally, the apparatus further comprises:
the receiving unit 201 is configured to receive initial privacy distortion data sent by a client;
the processing unit is used for carrying out normalization processing on the initial privacy data to obtain privacy distortion data;
and the storage unit is used for storing the privacy distortion data in a privacy distortion database.
Optionally, the apparatus further comprises:
the second determining unit is used for determining an original data mining model;
the acquisition unit is used for acquiring the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained;
the training unit is used for training the original data mining model according to the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained to obtain the target data mining model.
Optionally, the apparatus further comprises:
the acquisition unit is further used for acquiring privacy distortion data to be tested and an analysis result of the privacy distortion data to be tested;
the input unit 203 is further configured to input the privacy distortion data to be tested into the target data mining model, to obtain an analysis result to be verified;
the third determining unit is used for determining the accuracy of the target data mining model according to the analysis result to be verified and the analysis result of the privacy distortion data to be tested;
and the execution unit is used for re-executing the step of determining the original data mining model if the accuracy is lower than a preset threshold.
Optionally, the raw data mining model includes one or more of a regression analysis model, a decision tree model, an artificial neural network model, a bayesian network model, a support vector machine model, a clustering model, and an association model.
Therefore, in the embodiment of the application, the data mining device is provided, and the complete privacy data is not required to be obtained from the client side when the data is mined, but the data mining is only carried out according to the privacy distortion data, so that even if the data is leaked, the data processing platform also distorts the data instead of the complete privacy data, and the security of the privacy data is improved.
The embodiment of the application also provides computer equipment, which comprises: a memory, a processor, and a bus system;
wherein the memory is used for storing programs;
the processor is used for executing the program in the memory so as to realize any one of the data mining methods;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
The embodiments of the present application also provide a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any of the data mining methods described above.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be specifically noted that the data mining method, device, equipment and readable storage medium provided by the application can be used in the big data field or the financial field. The foregoing is merely exemplary, and the application fields of the data mining method, apparatus, device and readable storage medium provided by the present application are not limited.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of data mining, the method comprising:
receiving a data mining request, wherein the data mining request comprises identification information of target privacy distortion data;
determining target privacy distortion data from a privacy distortion database according to the identification information, wherein the privacy distortion database comprises a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client;
and inputting the target privacy distortion data into a target data mining model to obtain an analysis result of the target privacy distortion data.
2. The method according to claim 1, wherein the method further comprises:
receiving initial privacy distortion data sent by a client;
normalizing the initial privacy data to obtain privacy distortion data;
the privacy distortion data is stored in a privacy distortion database.
3. The method according to claim 1, wherein the method further comprises:
determining an original data mining model;
acquiring privacy distortion data to be trained and an analysis result of the privacy distortion data to be trained;
and training the original data mining model according to the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained to obtain the target data mining model.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring privacy distortion data to be tested and an analysis result of the privacy distortion data to be tested;
inputting the privacy distortion data to be tested into the target data mining model to obtain an analysis result to be verified;
determining the accuracy of the target data mining model according to the analysis result to be verified and the analysis result of the privacy distortion data to be tested;
and if the accuracy is lower than a preset threshold, re-executing the step of determining the original data mining model.
5. The method of claim 3 or 4, wherein the raw data mining model comprises one or more of a regression analysis model, a decision tree model, an artificial neural network model, a bayesian network model, a support vector machine model, a cluster model, and an association model.
6. A data mining apparatus, the apparatus comprising:
a receiving unit configured to receive a data mining request, where the data mining request includes identification information of target privacy distortion data;
the first determining unit is used for determining target privacy distortion data from a privacy distortion database according to the identification information, wherein the privacy distortion database comprises a plurality of privacy distortion data, and the privacy distortion data is obtained by performing distortion processing on the privacy data by a client;
and the input unit is used for inputting the target privacy distortion data into a target data mining model to obtain an analysis result of the target privacy distortion data.
7. The apparatus of claim 6, wherein the apparatus further comprises:
the receiving unit is used for receiving the initial privacy distortion data sent by the client;
the processing unit is used for carrying out normalization processing on the initial privacy data to obtain privacy distortion data;
and the storage unit is used for storing the privacy distortion data in a privacy distortion database.
8. The apparatus of claim 6, wherein the apparatus further comprises:
the second determining unit is used for determining an original data mining model;
the acquisition unit is used for acquiring the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained;
the training unit is used for training the original data mining model according to the privacy distortion data to be trained and the analysis result of the privacy distortion data to be trained to obtain the target data mining model.
9. A computer device, comprising: a memory, a processor, and a bus system;
wherein the memory is used for storing programs;
the processor being adapted to execute a program in the memory to implement the method of any one of claims 1 to 5;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
10. A computer readable storage medium storing instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202311074599.9A CN117113402A (en) | 2023-08-24 | 2023-08-24 | Data mining method, device, equipment and readable storage medium |
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| CN202311074599.9A CN117113402A (en) | 2023-08-24 | 2023-08-24 | Data mining method, device, equipment and readable storage medium |
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