Generation method and device of logic real data, equipment and medium

Abstract

The invention relates to the technical field of data privacy protection, and discloses a method, a device and equipment for generating logic real data and a medium, the method comprises the following steps: extracting a sensitive data set from an original data set, and performing grouped hash storage on the original data set according to the sensitive data set to obtain a hash storage bucket set; performing tuple class decomposition on tuples in the hash storage bucket set to obtain a decomposed tuple class set and a residual tuple set; distributing each tuple in the remaining tuple set to each decomposed tuple class in the decomposed tuple class set by using a non-identical random matching method to obtain a standard tuple class set; and carrying out sensitive information disassembly on the standard tuple class set to obtain logic real data. Through iterative multi-component decomposition, different random allocation and sensitive data acquisition implemented by the method, privacy protection of the original data set can be effectively realized, data logic of the original data is reserved, and the efficiency of mining data privacy protection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data privacy protection, and in particular to a method, device, equipment and medium for generating logical real data. Background Art

[0002] Data mining is the process of extracting valuable information and patterns from large amounts of data. Data mining can help all walks of life obtain valuable information and insights from data, thereby promoting intelligent and refined management in various fields, improving efficiency and decision-making quality. However, in the process of data mining, in order to prevent the leakage of personal identity information or sensitive information and protect personal privacy, the original data needs to be protected.

[0003] Traditional privacy protection methods for data mining include data desensitization, privacy computing, data sandbox, and federated learning. In actual use, technical solutions such as privacy computing and federated learning require multiple devices or computing nodes to be performed without sharing the original data. In order to ensure data security, complex encryption and decryption operations and data communication between multiple parties are required, which adds additional computing and communication overheads, consumes a lot of computing resources, and makes the overall computing cost high and the computing efficiency low. In order to pursue the security of desensitized data, the desensitized data completely loses data attributes and the correlation between data, thereby reducing the mining value of the data, which may lead to low efficiency in privacy protection of mined data. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for generating logical real data, the main purpose of which is to solve the problem of low efficiency in privacy protection of mining data in related technologies.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for generating logical real data, comprising: extracting a sensitive data set from an original data set, grouping and hashing the original data set according to the sensitive data set to obtain a hash storage bucket set; performing tuple class decomposition on tuples in the hash storage bucket set to obtain a decomposed tuple class set and a remaining tuple set; using a non-random matching method to assign each tuple in the remaining tuple set to each decomposed tuple class in the decomposed tuple class set to obtain a standard tuple class set; performing sensitive information decomposition on the standard tuple class set to obtain logical real data.

[0006] In order to solve the above problems, the present invention also provides a device for generating logical real data, which includes: a hash grouping module, which is used to extract a sensitive data set from an original data set, and group the original data set according to the sensitive data set for hash storage to obtain a hash storage bucket set; an iterative grouping module, which is used to perform tuple class decomposition on the tuples in the hash storage bucket set to obtain a decomposed tuple class set and a remaining tuple set; a remaining grouping module, which is used to assign each tuple in the remaining tuple set to each decomposed tuple class in the decomposed tuple class set using a non-random matching method to obtain a standard tuple class set; an information disassembly module, which is used to perform sensitive information disassembly on the standard tuple class set to obtain logical real data.

[0007] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0008] at least one processor; and,

[0009] a memory communicatively connected to at least one processor; wherein,

[0010] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned method for generating logical real data.

[0011] In order to solve the above problem, the present invention also provides a computer-readable storage medium storing a computer program, which implements the above method for generating logical real data when executed by a processor.

[0012] The embodiment of the present invention performs grouped hash storage on the original data set according to the sensitive data set to obtain a hash storage bucket set, and can realize grouped bucket storage of the sensitive data set according to the data value of the sensitive data, thereby ensuring efficient data storage and search, and realizing primary tuple classification according to the sensitive data. By performing iterative tuple decomposition and generating a standard tuple class set using a non-identical random matching method, it can ensure that each tuple in the remaining tuple set can also be assigned to a tuple class with different sensitive data, thereby disrupting the association between sensitive data and class names, and ensuring the privacy security of the data. By performing sensitive information disassembly on the standard tuple class set, logical real data is obtained, and the one-to-one correspondence between the original tuple and the sensitive data is broken, so that each equivalence class corresponds to multiple sensitive data values. The attacker can only reconstruct the data by naturally connecting the quasi-identifier data set and the sensitive information set, but according to the nature of the natural connection, the reconstruction will generate many records that do not exist in the original data set, resulting in a greatly reduced probability of obtaining sensitive information, thereby effectively ensuring user privacy. Therefore, the method, device, equipment and medium for generating logical real data proposed by the present invention can solve the problem of low efficiency in privacy protection of mining data. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of a flow chart of a method for generating logical real data provided by an embodiment of the present invention;

[0014] Figure 2 A schematic diagram of an example of an original data set provided by an embodiment of the present invention;

[0015] Figure 3 A schematic diagram of an example of a bucket tuple number set provided by an embodiment of the present invention;

[0016] Figure 4 A schematic diagram of an example of decomposing a tuple class set provided by an embodiment of the present invention;

[0017] Figure 5 A schematic diagram of an example of a standard tuple class set provided by an embodiment of the present invention;

[0018] Figure 6 A schematic diagram of an example of a quasi-identifier data set provided by an embodiment of the present invention;

[0019] Figure 7 A schematic diagram of an example of a sensitive information set provided by an embodiment of the present invention;

[0020] Figure 8 A functional module diagram of a device for generating logical real data provided by an embodiment of the present invention;

[0021] Fig. 9 A schematic diagram of the structure of an electronic device for implementing a method for generating logical real data provided by an embodiment of the present invention.

[0022] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0024] The embodiment of the present application provides a method for generating logical real data. The execution subject of the method for generating logical real data includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for generating logical real data can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0025] Reference Figure 1 FIG. 1 is a flow chart of a method for generating logical real data according to an embodiment of the present invention. In this embodiment, the method for generating logical real data includes:

[0026] S1. Extract sensitive data sets from the original data sets, group the original data sets according to the sensitive data sets and perform hash storage to obtain a hash storage bucket set.

[0027] Specifically, the original data set refers to the data set that needs to be mined and privacy protected. The original data set may be a patient data form of a hospital, a student data form of a school, or a personnel data form of a company.

[0028] Specifically, the original dataset can be Figure 2 The patient data form shown contains headers such as serial number (ID), patient name (Name), patient age (Age), patient gender (Sex), compression code (Zipcode), and patient disease (Disease). The sensitive data set refers to the collection of some data in the original data set that needs to be privacy protected. In the patient data form, the sensitive data set refers to the data set composed of all elements in the patient disease (Disease) column.

[0029] In detail, a sensitive data set is extracted from an original data set, including extracting a header of the original data set to obtain an original header set; matching sensitive keywords on the original header set to obtain sensitive header elements; generating a sensitive element query statement based on the sensitive header elements; and traversing and extracting the original data set based on the sensitive element query statement to obtain a sensitive data set.

[0030] Specifically, the headers can be extracted using the SHOW COLUMNS query statement of MySQL or the INFORMATION_SCHEMA item of PostgreSQL. The original header set is a data set consisting of all the header elements in the original data set. For example, Figure 2 The corresponding original header set is [ID, Name, Age, Sex, Zipcode, Disease].

[0031] Specifically, sensitive keyword matching refers to using keyword matching methods to extract headers related to sensitive or privacy elements from the original header set as sensitive header elements. Sensitive element query statements refer to query statements used to traverse and extract all elements of the item where the sensitive header element is located. The executeQuery method of the Statement object can be used to generate a sensitive element query statement, and the sensitive element query statement can be used to call the getString, getInt and other methods of the ResultSet object for traversal and extraction.

[0032] In detail, the original data set is grouped and hashed according to the sensitive data set to obtain a hash bucket set, including: selecting sensitive data in the sensitive data set one by one as target sensitive data, and taking the tuple where the target sensitive data is located in the original data set as the target data tuple; performing hash operation on the target sensitive data to obtain a target hash index; mapping a mapping bucket from a preset bucket list according to the target hash index; storing the target data tuple in the mapping bucket to obtain a hash bucket, and aggregating the hash buckets corresponding to all target sensitive data in the sensitive data set into a hash bucket set.

[0033] Specifically, a tuple refers to an element row in the original data set, and a target data tuple refers to an element row in the original data set where the target sensitive data is located. For example, Figure 2 As shown in Figure 2, when the target sensitive data is gastritis, the corresponding target data tuple is [6, Jim, 65, F, 25120, gastritis].

[0034] Specifically, a hash operation may be performed using a hash algorithm such as MD5, SHA-256, CRC32, or FNV-1a, and the target hash index is a hash value obtained after the target sensitive data is subjected to the hash operation.

[0035] In detail, a mapping bucket is mapped from a preset bucket list according to a target hash index, including: performing a modulo operation on the target hash index to obtain a bucket index; matching a mapping bucket from a preset bucket list according to the bucket index, wherein a bucket is a storage space for storing certain types of data, and a bucket list is part of a hash table for storing data items with similar hash values. When a new data item needs to be added to the hash table, a hash function is used to calculate the key of the data item, and then the data item is stored in a corresponding bucket.

[0036] In an embodiment of the present invention, by grouping and hashing the original data set according to the sensitive data set to obtain a hash storage bucket set, grouped bucket storage of the sensitive data set can be implemented according to the data value of the sensitive data, ensuring efficient data storage and search, and realizing primary tuple classification based on the sensitive data.

[0037] S2. Perform tuple class decomposition on the tuples in the hash bucket set to obtain a decomposed tuple class set and a remaining tuple set.

[0038] In detail, each decomposition tuple class in the decomposition tuple class set is a combination class composed of multiple tuples, and the tuples in the decomposition tuple class satisfy diverse classification characteristics, that is, the decomposition tuple class is a class composed of tuples corresponding to multiple different types of sensitive data, and the remaining tuple set is a set of tuples in the hash bucket set except for the tuples corresponding to the decomposition tuple class set.

[0039] In an embodiment of the present invention, tuples in a hash bucket set are decomposed into tuple classes to obtain a decomposed tuple class set and a residual tuple set, including: counting the number of bucket tuples in the hash bucket set to obtain a bucket tuple number set; extracting a diversity coefficient from the bucket tuple number set using a maximum matching method; classifying the tuples in the hash bucket set according to the diversity coefficient to obtain a decomposed tuple class set; and aggregating the remaining tuples in the hash bucket set except the decomposed tuple class set into a residual tuple set.

[0040] Specifically, bucket tuple count refers to counting the number of tuples in each hash bucket in the hash bucket set, and collecting the number as the bucket tuple number into a bucket tuple number set. Figure 3 As shown, this is the corresponding bucket tuple number set when the original data set is a patient data form, where bucket-ID refers to the serial number ID of each hash bucket in the hash bucket set, Disease is a sensitive header element, and Count is the header of the same tuple number set. For example, the hash bucket with serial number 1 is used to store tuples whose sensitive header element is pneumonia, and the number of bucket tuples whose sensitive header element is pneumonia in the hash bucket with serial number 1 is 2.

[0041] Specifically, maximum value matching refers to matching the number of bucket tuples with the largest value in the bucket tuple set as the diversity coefficient. The diversity coefficient is the maximum probability for inferring sensitive values. Figure 3 It can be seen that when the original data set is a patient data form, the diversity coefficient is 3.

[0042] In detail, tuples in the hash bucket set are classified according to the diversity coefficient to obtain a decomposition tuple class set, including: judging whether the number of non-empty buckets in the hash bucket set is less than the diversity coefficient; if not, filtering out a tuple bucket group from the hash bucket set according to the diversity coefficient; randomly filtering out a tuple from each bucket in the tuple bucket group, and generating a decomposition tuple class based on all the filtered tuples; updating the hash bucket set with the tuple bucket group that has filtered out the decomposition tuple class, and returning to the step of judging whether the number of non-empty buckets in the hash bucket set is less than the diversity coefficient; if so, generating a decomposition tuple class set based on all the decomposition tuple classes.

[0043] In detail, a non-empty bucket refers to a non-empty bucket in the hash bucket set, that is, a bucket storing a tuple.

[0044] Specifically, a multi-tuple bucket group is selected from the hash bucket set according to the diversity coefficient, including: counting the number of bucket tuples in the hash bucket set to obtain a bucket tuple number set; arranging the bucket tuple number set in descending order to obtain a bucket tuple number sequence; aggregating the first diversity coefficient bucket tuple numbers in the bucket tuple number sequence into a multi-tuple bucket tuple array; and aggregating the hash buckets corresponding to the multi-tuple bucket tuple array in the hash bucket set into a multi-tuple bucket group.

[0045] For details, refer to Figure 3 As shown, when the original data set is a patient data form, the sequence of homograph numbers is [2,2,2,1,1], the multi-tuple bucket tuple array is [2, 2, 2], and the multi-tuple bucket group is the buckets corresponding to bucket-IDs 1, 2, and 3.

[0046] Specifically, refer to Figure 4 As shown in the figure, when the original data set is a patient data form, the tuple table corresponding to the decomposed tuple class set is shown, where QI gcnt It is the serial number of each decomposition tuple class in the decomposition tuple class set. The number of decomposition tuple classes is 2, and each decomposition tuple class contains 3 tuples.

[0047] In detail, the remaining tuple set is a set of tuples in the hash bucket set except the decomposed tuple set, refer to Figure 3 and Figure 4 As shown, the remaining tuple set is the tuples corresponding to bucket-IDs 4 and 5.

[0048] In an embodiment of the present invention, by performing iterative tuple decomposition to obtain a decomposed tuple class set and a remaining tuple set, each tuple in a hash storage bucket with the same sensitive data can be split into different tuple classes, thereby disrupting the association between sensitive data and class names and ensuring the privacy and security of the data.

[0049] S3. Use a non-random matching method to assign each tuple in the remaining tuple set to each decomposition tuple class in the decomposition tuple class set to obtain a standard tuple class set.

[0050] In an embodiment of the present invention, a non-random matching method is used to assign each tuple in the remaining tuple set to each decomposition tuple class in the decomposition tuple class set to obtain a standard tuple class set, including: selecting tuples in the remaining tuple set one by one as target remaining tuples, and using sensitive data in the target remaining tuple as target remaining sensitive data; randomly selecting a decomposition tuple class in the decomposition tuple class set as the target decomposition tuple class, and using the tuple storage bucket group corresponding to the target decomposition tuple class as the target tuple storage bucket group; judging whether the target remaining sensitive data exists in the target tuple storage bucket group; if so, returning to the step of randomly selecting a decomposition tuple class in the decomposition tuple class set as the target decomposition tuple class; if not, assigning the target remaining tuple to the target decomposition tuple class to obtain a secondary tuple class, and using the secondary tuple class to update the decomposition tuple class set into a secondary tuple class set, until the target remaining tuple is the last tuple in the remaining tuple set, and using the secondary tuple class set as the standard tuple class set.

[0051] For details, refer to Figure 5 As shown in the figure, when the original data set is a patient data form, the tuple table corresponding to the standard tuple class set is Figure 4 Compared with the standard tuple class, there are two more tuples corresponding to the sensitive data gastritis and sensitive data bronchitis in the standard tuple class, that is, the two tuples in the remaining tuple set. gcnt The target tuple bucket group corresponding to 1 does not contain sensitive data gastritis, so the tuple of sensitive data gastritis can be added to the decomposition tuple class with serial number QI gcnt 1 in the decomposition tuple class.

[0052] In an embodiment of the present invention, by using a non-random matching method to generate a standard tuple class set, it is possible to ensure that each tuple in the remaining tuple set can also be assigned to a tuple class with different sensitive data, thereby disrupting the association between sensitive data and class names and ensuring data privacy security.

[0053] S4. Decompose sensitive information of standard tuple sets to obtain logically true data.

[0054] In detail, logical real data refers to real data logic with original data to meet the needs of data mining. Logical real data includes identification information and sensitive information, wherein the identification information refers to quasi-identifiers (QI) that can uniquely identify individuals, and the sensitive information (SI) is referred to as sensitive information.

[0055] In an embodiment of the present invention, sensitive information in a standard tuple set is decomposed to obtain logical real data, including: decomposing identification information in the standard tuple set to obtain a quasi-identifier data set; decomposing sensitive information in the standard tuple set to obtain a sensitive information set; and generating logical real data based on the quasi-identifier data set and the sensitive information set.

[0056] In detail, decomposing the identification information in the standard tuple class set means using the class serial number of each tuple in the standard tuple class set to replace the sensitive data items in the tuple. The identification information in the standard tuple class set can be decomposed using keyword matching methods and character replacement methods.

[0057] Specifically, refer to Figure 6 As shown in FIG. 1 , when the original data set is a patient data form, the table corresponding to the quasi-identifier data set is a table corresponding to the quasi-identifier data set. The quasi-identifier data in the quasi-identifier data set is composed of quasi-identifiers (QI) that can uniquely identify an individual. Figure 5 Compared with the standard tuple class set, the quasi-identifier dataset replaces the sensitive data Disease item with the corresponding decomposition tuple class number QI gcnt .

[0058] In detail, the sensitive information in the standard tuple class set is decomposed to obtain a sensitive information set, including: extracting a sensitive data set from the standard tuple class set; selecting sensitive data in the sensitive data set one by one as target sensitive data, and using the standard tuple class corresponding to the target sensitive data in the standard tuple class set as the target standard tuple class; counting the number of sensitive data of the target sensitive data in the target standard tuple class; using the serial number of the target standard tuple class as the target class serial number; generating sensitive information according to the target class serial number, the target sensitive data and the number of sensitive data, and aggregating the sensitive information of all target sensitive data in the sensitive data set into a sensitive information set.

[0059] For details, refer to Figure 7 As shown in FIG. 1 , when the original data set is a patient data form, the table corresponding to the sensitive information set is shown in FIG. 1 . The sensitive information (SI) in the sensitive information set is sensitive data that is stored separately. gcntThe item corresponds to the target class serial number, and the Count item corresponds to the number of sensitive data.

[0060] In the embodiment of the present invention, by disassembling the sensitive information of the standard tuple class set, logical real data is obtained, and the one-to-one correspondence between the original tuple and the sensitive data is broken, so that each equivalence class corresponds to multiple sensitive data values. The attacker can only reconstruct the data by naturally connecting the quasi-identifier data set and the sensitive information set. However, according to the nature of the natural connection, the reconstruction will produce many records that do not exist in the original data set, resulting in a greatly reduced probability of obtaining sensitive information, thereby effectively ensuring user privacy.

[0061] The embodiment of the present invention performs grouped hash storage on the original data set according to the sensitive data set to obtain a hash storage bucket set, and can realize grouped bucket storage of the sensitive data set according to the data value of the sensitive data, thereby ensuring efficient data storage and search, and realizing primary tuple classification according to the sensitive data. By performing iterative tuple decomposition and generating a standard tuple class set using a non-identical random matching method, it can ensure that each tuple in the remaining tuple set can also be assigned to a tuple class with different sensitive data, thereby disrupting the association between sensitive data and class names, and ensuring data privacy security. By performing sensitive information disassembly on the standard tuple class set, logical real data is obtained, and the one-to-one correspondence between the original tuple and sensitive data is broken, so that each equivalence class corresponds to multiple sensitive data values. The attacker can only reconstruct the data by naturally connecting the quasi-identifier data set and the sensitive information set. However, according to the nature of the natural connection, the reconstruction will generate many records that do not exist in the original data set, resulting in a greatly reduced probability of obtaining sensitive information, thereby effectively ensuring user privacy. Therefore, the method for generating logical real data proposed by the present invention can solve the problem of low efficiency in privacy protection of mining data.

[0062] like Figure 8 , which is a functional module diagram of a device for generating logical real data provided by an embodiment of the present invention.

[0063] The device 800 for generating logical real data of the present invention can be installed in an electronic device. According to the functions to be implemented, the device 800 for generating logical real data can include a hash grouping module 801, an iterative grouping module 802, a residual grouping module 803 and an information disassembly module 804. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0064] In this embodiment, the functions of each module / unit are as follows:

[0065] A hash grouping module 801 is used to extract a sensitive data set from an original data set, group the original data set according to the sensitive data set, and perform hash storage to obtain a hash storage bucket set;

[0066] Iterative grouping module 802, used for performing tuple class decomposition on the tuples in the hash bucket set to obtain a decomposed tuple class set and a remaining tuple set;

[0067] The remaining grouping module 803 is used to allocate each tuple in the remaining tuple set to each decomposition tuple class in the decomposition tuple class set by using a non-random matching method to obtain a standard tuple class set;

[0068] The information disassembly module 804 is used to disassemble the sensitive information of the standard tuple set to obtain logical real data.

[0069] In detail, each module in the device 800 for generating logical real data in the embodiment of the present invention adopts the same method as above when used. Figure 1 The technical means used are the same as the method for generating logical real data in the method and can produce the same technical effects, so I will not go into details here.

[0070] like Fig. 9 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a method for generating logical real data provided by an embodiment of the present invention.

[0071] The electronic device 901 may include a processor 910 , a memory 911 , a communication bus 912 , and a communication interface 913 , and may also include a computer program stored in the memory 911 and executable on the processor 910 , such as a program for generating logical real data.

[0072] In some embodiments, the processor 910 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 910 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory 911 (for example, executing a program for generating logical real data, etc.), and calling data stored in the memory 911.

[0073] The memory 911 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 911 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 911 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 911 may also include both an internal storage unit of the electronic device and an external storage device. The memory 911 can be used not only to store application software and various types of data installed in the electronic device, such as the code of the program for generating logical real data, but also to temporarily store data that has been output or is to be output.

[0074] The communication bus 912 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 911 and at least one processor 910, etc.

[0075] The communication interface 913 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0076] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0077] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to at least one processor 910 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0078] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited by this structure.

[0079] Specifically, the specific implementation method of the processor 910 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0080] Furthermore, if the module / unit integrated in the electronic device 901 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying a computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0081] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0082] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for generating logical real data, characterized in that: The method comprises: Extracting a sensitive data set from the original data set, grouping and hashing the original data set according to the sensitive data set to obtain a hash storage bucket set; Performing tuple class decomposition on the tuples in the hash bucket set to obtain a decomposed tuple class set and a remaining tuple set; Allocating each tuple in the remaining tuple set to each decomposition tuple class in the decomposition tuple class set by using a non-random matching method to obtain a standard tuple class set; The sensitive information of the standard tuple class set is disassembled to obtain logically real data.

2. The method for generating logical real data according to claim 1, characterized in that: The step of extracting a sensitive data set from an original data set includes: Extracting headers from the original data set to obtain an original header set; Perform sensitive keyword matching on the original header set to obtain sensitive header elements; Generate a sensitive element query statement according to the sensitive header element; The original data set is traversed and extracted according to the sensitive element query statement to obtain a sensitive data set.

3. The method for generating logical real data according to claim 1, characterized in that: The grouping and hashing the original data set according to the sensitive data set to obtain a hash storage bucket set includes: Selecting sensitive data in the sensitive data set one by one as target sensitive data, and taking the tuple containing the target sensitive data in the original data set as the target data tuple; Performing a hash operation on the target sensitive data to obtain a target hash index; Mapping a mapping bucket from a preset bucket list according to the target hash index; The target data tuple is stored in the mapping bucket to obtain a hash bucket, and the hash buckets corresponding to all target sensitive data in the sensitive data set are aggregated into a hash bucket set.

4. The method for generating logical real data according to claim 1, characterized in that: The step of performing tuple class decomposition on the tuples in the hash bucket set to obtain a decomposed tuple class set and a remaining tuple set includes: Counting the number of bucket tuples on the hash storage bucket set to obtain a bucket tuple number set; Extracting a diversity coefficient from the bucket tuple set using a maximum value matching method; Classifying the tuples in the hash bucket set according to the diversity coefficient to obtain a decomposed tuple class set; The remaining tuples in the hash bucket set except the decomposed tuple set are assembled into a remaining tuple set.

5. The method for generating logical real data according to claim 4, characterized in that: The step of classifying the tuples in the hash bucket set according to the diversity coefficient to obtain a decomposed tuple class set includes: Determining whether the number of non-empty buckets in the hash bucket set is less than the diversity coefficient; If not, filtering out a multi-tuple bucket group from the hash bucket set according to the diversity coefficient; Randomly select a tuple from each storage bucket in the tuple storage bucket group, and generate a decomposed tuple class according to all the selected tuples; The hash bucket set is updated by using the tuple bucket group that has screened out the decomposed tuple class, and returning to the step of determining whether the number of non-empty buckets in the hash bucket set is less than the diversity coefficient; If so, generate a decomposition tuple class set based on all decomposition tuple classes.

6. The method for generating logical real data according to claim 1, characterized in that: The method of using non-random matching to distribute each tuple in the remaining tuple set to each decomposition tuple class in the decomposition tuple class set to obtain a standard tuple class set includes: Selecting tuples in the remaining tuple set one by one as target remaining tuples, and using sensitive data in the target remaining tuples as target remaining sensitive data; Randomly select a decomposition tuple class in the decomposition tuple class set as a target decomposition tuple class, and use a tuple storage bucket group corresponding to the target decomposition tuple class as a target tuple storage bucket group; Determine whether there is target remaining sensitive data in the target multi-tuple storage bucket group; If yes, returning to the step of randomly selecting a decomposition tuple class from the decomposition tuple class set as a target decomposition tuple class; If not, the target remaining tuple is assigned to the target decomposition tuple class to obtain a secondary tuple class, and the secondary tuple class is used to update the decomposition tuple class set into a secondary tuple class set, until the target remaining tuple is the last tuple in the remaining tuple set, and the secondary tuple class set is used as the standard tuple class set.

7. The method for generating logical real data according to claim 1, characterized in that: The step of decomposing the sensitive information in the standard tuple class set to obtain a sensitive information set includes: Extracting a sensitive data set from the standard tuple set; Selecting sensitive data in the sensitive data set one by one as target sensitive data, and taking the standard tuple class corresponding to the target sensitive data in the standard tuple class set as the target standard tuple class; Counting the number of sensitive data of the target sensitive data in the target standard tuple class; Using the serial number of the target standard tuple class as the target class serial number; Sensitive information is generated according to the target class serial number, the target sensitive data and the amount of sensitive data, and the sensitive information of all target sensitive data in the sensitive data set is aggregated into a sensitive information set.

8. A device for generating logical real data, characterized in that: The device comprises: A hash grouping module is used to extract a sensitive data set from an original data set, and group and hash the original data set according to the sensitive data set to obtain a hash storage bucket set; An iterative grouping module, used for performing tuple class decomposition on the tuples in the hash bucket set to obtain a decomposed tuple class set and a remaining tuple set; A remaining grouping module is used to allocate each tuple in the remaining tuple set to each decomposition tuple class in the decomposition tuple class set by using a non-random matching method to obtain a standard tuple class set; The information disassembly module is used to disassemble the sensitive information of the standard tuple class set to obtain logical real data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for generating logical truth data according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for generating logical truth data according to any one of claims 1 to 7 is implemented.

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