Data desensitization method, device and equipment of distributed embedded system and medium

Abstract

The embodiment of the invention discloses a data desensitization method and device for a distributed embedded system, equipment and a medium, and relates to the technical field of data desensitization. The method comprises the following steps: if a data query request is received, determining a correlation coefficient according to the correlation degree of queried data in the data query request; the queried data are located in at least two nodes; determining the data precision of the queried data; and determining a target desensitization mode according to the association coefficient of the queried data and the data precision, and desensitizing the queried data based on the target desensitization mode. According to the technical scheme, different target desensitization modes are set for different queried data, so that data desensitization of the distributed embedded system is more reasonable.

Description

Technical Field

[0001] The present invention relates to the technical field of data desensitization, and in particular to a data desensitization method, device, equipment and medium for a distributed embedded system. Background Art

[0002] An embedded system is a special-purpose computer system that is completely embedded in a controlled device and designed for a specific application. In an embedded system, nodes can communicate with each other through a network to form a distributed embedded system. How to perform data desensitization based on a distributed embedded system is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present invention provides a data desensitization method, device, equipment and medium for a distributed embedded system, which can reasonably desensitize the queried data during data query.

[0004] According to one aspect of the present invention, a data desensitization method for a distributed embedded system is provided, the method comprising:

[0005] If a data query request is received, determining a correlation coefficient according to the correlation degree of the queried data in the data query request; the queried data is located in at least two nodes;

[0006] Determining the data accuracy of the queried data;

[0007] The target desensitization method is determined according to the correlation coefficient and data accuracy of the queried data, and the queried data is desensitized based on the target desensitization method.

[0008] According to another aspect of the present invention, a data desensitization device for a distributed embedded system is provided, comprising:

[0009] A correlation coefficient determination module, configured to determine a correlation coefficient according to a correlation degree of the queried data in the data query request if a data query request is received; the queried data is located in at least two nodes;

[0010] A data accuracy determination module, used to determine the data accuracy of the queried data;

[0011] The desensitization processing module is used to determine a target desensitization method according to the correlation coefficient and data accuracy of the queried data, and perform desensitization processing on the queried data based on the target desensitization method.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed 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 execute the data desensitization method for a distributed embedded system described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the data desensitization method for a distributed embedded system described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present application includes: if a data query request is received, a correlation coefficient is determined according to the correlation degree of the queried data in the data query request; the queried data is located in at least two nodes; the data accuracy of the queried data is determined; the target desensitization method is determined according to the correlation coefficient and data accuracy of the queried data, and the queried data is desensitized based on the target desensitization method. The technical solution sets different target desensitization methods for different queried data to make the data desensitization of the distributed embedded system more reasonable.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a flow chart of a data desensitization method for a distributed embedded system provided according to the first embodiment of the present application;

[0021] Figure 2 It is a flow chart of a data desensitization method for a distributed embedded system provided according to the second embodiment of the present application;

[0022] Figure 3 It is a structural schematic diagram of a data desensitization device for a distributed embedded system provided according to Embodiment 3 of the present application;

[0023] Figure 4It is a structural schematic diagram of an electronic device for implementing a data desensitization method for a distributed embedded system according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] Figure 1 A flowchart of a data desensitization method for a distributed embedded system is provided for the first embodiment of the present application. The embodiment of the present application can be applied to the case of desensitizing data. The method can be executed by a data desensitization device for a distributed embedded system. The data desensitization device for a distributed embedded system can be implemented in the form of hardware and / or software. The data desensitization device for a distributed embedded system can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0028] S110, if a data query request is received, determining a correlation coefficient according to a correlation degree of the queried data in the data query request; the queried data is located in at least two nodes.

[0029] In the embodiment of the present application, the nodes in the distributed embedded system are all embedded devices, and each node may receive a data query request. The data desensitization method of the distributed embedded system in the embodiment of the present application can be executed by the nodes of the distributed embedded system.

[0030] Among them, the data query request is a query request for the data in the node. If the queried data targeted by the request is located on at least two nodes, when desensitizing the queried data, it is necessary to consider the data correlation while protecting privacy, so that the query party can extract the required information from the desensitized data. The correlation coefficient reflects the strength of the correlation. The larger the correlation coefficient, the stronger the correlation. For example, the power output of the power grid and the power consumption of the user are closely related, and the correlation coefficient between the power output of the power grid and the power consumption of the user is large; there may be no correlation between the equipment number and the power consumption, and the correlation coefficient between the equipment number and the power consumption is small.

[0031] Specifically, the degree of association between the data in different nodes can be pre-calibrated. If a data query request is received, the queried data is determined in the data query request, and it is determined whether there is an association between the queried data and the degree of association, and then the corresponding association coefficient is determined according to the degree of association. The greater the degree of association, the greater the association coefficient; the smaller the degree of association, the smaller the association coefficient.

[0032] S120: Determine the data accuracy of the queried data.

[0033] Specifically, in one achievable manner, the data accuracy of the queried data is read in the data query request. In another achievable manner, after the queried data is determined, the data accuracy corresponding to the queried data is determined based on a pre-calibrated data accuracy table.

[0034] S130, determining a target desensitization method according to the correlation coefficient and data accuracy of the queried data, and performing desensitization processing on the queried data based on the target desensitization method.

[0035] Specifically, if the correlation coefficient is 0 and the data accuracy is low, it means that there is no correlation between the queried data and the accuracy requirement for the queried data is also low. In this case, the inquirer does not need to use the correlation between the queried data, and the queried data can be arbitrarily desensitized, that is, the target desensitization methods for different queried data can be different.

[0036] If the correlation coefficient is greater than 0, it indicates that there is a correlation between the queried data. In this case, all the queried data may adopt the same desensitization method, that is, the target desensitization methods of the queried data in different nodes are the same.

[0037] The technical solution of the embodiment of the present application includes: if a data query request is received, a correlation coefficient is determined according to the correlation degree of the queried data in the data query request; the queried data is located in at least two nodes; the data accuracy of the queried data is determined; the target desensitization method is determined according to the correlation coefficient and data accuracy of the queried data, and the queried data is desensitized based on the target desensitization method. The technical solution sets different target desensitization methods for different queried data to make the data desensitization of the distributed embedded system more reasonable.

[0038] Embodiment 2

[0039] Figure 2 This is a flow chart of a data desensitization method for a distributed embedded system provided in Example 2 of the present application. The embodiment of the present application is optimized based on the above embodiment.

[0040] like Figure 2 As shown, the method of the embodiment of the present application specifically includes the following steps:

[0041] S210, if a data query request is received, then in a pre-established data association graph, a target node where the queried data is located is determined; the nodes in the association graph correspond to nodes in the distributed embedded system.

[0042] Specifically, the data in the distributed embedded system can be regarded as nodes, and the associations between the data can be regarded as edges to construct an association graph. For example, in the power system, the power generation equipment nodes, transmission line nodes, substation nodes, user meter nodes, etc. are connected through edges representing the association relationship. For each node, the data it contains may have multiple attributes, such as the power and temperature of the power generation equipment.

[0043] After the association graph is pre-built, if a data query request is received, the queried data is determined according to the data query request, and in the association graph, the node where the queried data is located is identified, and the identified node is determined as the target node where the queried data is located.

[0044] S220, in the association graph, determining an association coefficient according to the connection relationship between the target nodes.

[0045] In an embodiment of the present application, optionally, in the association graph, the association coefficient is determined according to the connection relationship between the target nodes, including: if there is a target node on the sensitive association path of the association graph, the association coefficient is determined to be a ratio of a first number to a second number; the first number is the number of nodes in the target node that are located on the sensitive association path; the second number is the number of nodes in the target node that are not located on the sensitive association path; otherwise, the association coefficient is determined to be 0.

[0046] Specifically, in the association graph, for associated nodes, a sensitive association path can be pre-marked between the associated nodes. If there is a target node on the sensitive association path, the number of nodes in the target node that are on the sensitive association path is determined as a first number, the number of nodes in the target node that are not on the sensitive association path is determined as a second number, and the association coefficient is determined as the ratio of the first number to the second number; otherwise, it means that there is no association with the queried data, and the association coefficient is determined to be 0.

[0047] This scheme is set up so that the correlation coefficient of the queried data can be accurately calculated, and the higher the correlation degree of the queried data, the greater the correlation coefficient.

[0048] S230: Determine the data accuracy of the queried data.

[0049] S240, determining a target desensitization method according to the correlation coefficient and data accuracy of the queried data, and performing desensitization processing on the queried data based on the target desensitization method.

[0050] In an embodiment of the present application, optionally, a target desensitization method is determined based on a correlation coefficient of the queried data and data accuracy, including: if the correlation coefficient is greater than 0 and the data accuracy is greater than an accuracy threshold, then it is determined that the target desensitization processing method of the queried data is differential privacy; otherwise, based on the association information between the pre-calibrated data type and the desensitization method, a target desensitization method corresponding to each queried data is determined.

[0051] Among them, the accuracy threshold can be set according to actual conditions, and the embodiments of the present application do not limit this.

[0052] Exemplarily, if the correlation coefficient is greater than 0 and the data accuracy is greater than the accuracy threshold, there is a correlation between the queried data, and it can be determined that the target desensitization processing methods of the queried data are the same, both of which are differential privacy; otherwise, based on the association information between the pre-calibrated data type and the desensitization method, the desensitization method corresponding to the queried data is determined in turn according to the data type of each queried data.

[0053] In an embodiment of the present application, optionally, the queried data is desensitized based on a target desensitization method, including: determining a target noise distribution type in the noise distribution type in differential privacy according to a size relationship between the correlation coefficient and a correlation coefficient threshold; generating a target noise distribution that conforms to the target noise type according to a privacy budget and sensitivity; determining a noise value based on the target noise distribution, and desensitizing the queried data based on the noise value.

[0054] Among them, the correlation coefficient threshold can be set according to actual conditions, and the embodiments of the present application do not limit this.

[0055] Specifically, when the target desensitization method is differential privacy, since differential privacy includes two distributions, Laplace distribution and Gaussian distribution, the target noise distribution type can be determined in the noise distribution type in differential privacy according to the size relationship between the correlation coefficient and the correlation coefficient threshold. In an embodiment of the present application, optionally, according to the size relationship between the correlation coefficient and the correlation coefficient threshold, the target noise distribution type is determined in the noise distribution type in differential privacy, including: if the correlation coefficient is greater than the correlation coefficient threshold, the target noise distribution type is determined to be Laplace distribution; otherwise, the target noise distribution type is determined to be Gaussian distribution.

[0056] After determining the target noise distribution type, determine the privacy budget according to the data accuracy; the data accuracy is inversely proportional to the privacy budget, for example, if the data accuracy is 0.01, the privacy budget can be 10; if the data accuracy is 0.1, the privacy budget can be 5. Then determine the sensitivity, which can be set according to the actual situation, and the embodiment of the present application does not limit this. Generate a target noise distribution that meets the target noise type according to the privacy budget and sensitivity; determine the noise value based on the target noise distribution, and desensitize the queried data based on the noise value.

[0057] For example, if the privacy budget is set to 0.1, the privacy protection of the queried data will be very strict, and more noise may be added to confuse the data, resulting in a certain degree of decrease in data accuracy; if the privacy budget is set to 10, data availability will increase, but the degree of privacy protection will decrease.

[0058] For example, if the target noise distribution type is a Laplace distribution, the scale parameter can be calculated according to the privacy budget and the sensitivity to obtain the Laplace distribution. The noise value is obtained by random sampling from the Laplace distribution.

[0059] For example, for the case of Gaussian distribution, the standard deviation is calculated according to the privacy budget and sensitivity to obtain Gaussian distribution. Noise is obtained by random sampling from the Gaussian distribution. Noise is added to the data.

[0060] The embodiment of the present application is configured such that when there is a correlation in the queried data, the way to add noise is to add noise through differential privacy, and when the degree of correlation is high, the Laplace distribution is adopted, so that when the degree of correlation of the queried data is high, its correlation is less affected after adding noise, which facilitates the data query party to further analyze and process the desensitized data.

[0061] Embodiment 3

[0062] Figure 3This is a schematic diagram of the structure of a data desensitization device for a distributed embedded system provided in the third embodiment of the present application. The device can execute the data desensitization method for a distributed embedded system provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Figure 3 As shown, the device comprises:

[0063] The correlation coefficient determination module 310 is used to determine the correlation coefficient according to the correlation degree of the queried data in the data query request if a data query request is received; the queried data is located in at least two nodes;

[0064] A data accuracy determination module 320, used to determine the data accuracy of the queried data;

[0065] The desensitization processing module 330 is used to determine a target desensitization method according to the correlation coefficient and data accuracy of the queried data, and perform desensitization processing on the queried data based on the target desensitization method.

[0066] The technical solution of the embodiment of the present application includes: a correlation coefficient determination module 310, which is used to determine the correlation coefficient according to the correlation degree of the queried data in the data query request if a data query request is received; the queried data is located in at least two nodes; a data accuracy determination module 320, which is used to determine the data accuracy of the queried data; a desensitization processing module 330, which is used to determine the target desensitization method according to the correlation coefficient and data accuracy of the queried data, and desensitize the queried data based on the target desensitization method. This technical solution sets different target desensitization methods for different queried data to make the data desensitization of the distributed embedded system more reasonable.

[0067] In the embodiment of the present application, optionally, the correlation coefficient determination module 310 includes:

[0068] A target node determination unit, used to determine the target node where the queried data is located in a pre-established data association graph; the nodes in the association graph correspond to the nodes in the distributed embedded system;

[0069] The association coefficient determination unit is used to determine the association coefficient in the association graph according to the connection relationship between the target nodes.

[0070] In the embodiment of the present application, optionally, the correlation coefficient determination unit is specifically used to:

[0071] If there is a target node on the sensitive association path of the association graph, the association coefficient is determined as a ratio of a first number to a second number; the first number is the number of nodes in the target node that are on the sensitive association path; the second number is the number of nodes in the target node that are not on the sensitive association path;

[0072] Otherwise, the correlation coefficient is determined to be 0.

[0073] In the embodiment of the present application, optionally, the desensitization processing module 330 includes:

[0074] A target desensitization processing method determining unit, configured to determine that the target desensitization processing methods of the queried data are all differential privacy if the correlation coefficient is greater than 0 and the data accuracy is greater than an accuracy threshold;

[0075] Otherwise, based on the association information between the pre-calibrated data type and the desensitization method, determine the target desensitization method corresponding to each queried data.

[0076] In the embodiment of the present application, optionally, the desensitization processing module 330 includes:

[0077] A target noise distribution type determination unit, configured to determine a target noise distribution type from among the noise distribution types in differential privacy according to a magnitude relationship between the correlation coefficient and the correlation coefficient threshold;

[0078] A target noise distribution determination unit, used to generate a target noise distribution that meets the target noise type according to the privacy budget and the sensitivity;

[0079] The desensitization processing unit is used to determine the noise value based on the target noise distribution, and perform desensitization processing on the queried data based on the noise value.

[0080] In the embodiment of the present application, optionally, the target noise distribution type determination unit is specifically configured to:

[0081] If the correlation coefficient is greater than the correlation coefficient threshold, determining that the target noise distribution type is a Laplace distribution;

[0082] Otherwise, the target noise distribution type is determined to be Gaussian distribution.

[0083] In the embodiment of the present application, optionally, the device further includes: a privacy budget determination module, specifically configured to:

[0084] A privacy budget is determined according to the data accuracy; the data accuracy is inversely proportional to the privacy budget.

[0085] A data desensitization device for a distributed embedded system provided in an embodiment of the present application can execute a data desensitization method for a distributed embedded system provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0086] Embodiment 4

[0087] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0088] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0089] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0090] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 performs the various methods and processes described above, such as a data desensitization method for a distributed embedded system.

[0091] In some embodiments, the data desensitization method for a distributed embedded system may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data desensitization method for a distributed embedded system described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the data desensitization method for a distributed embedded system in any other appropriate manner (e.g., by means of firmware).

[0092] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0094] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0096] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0097] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0098] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0099] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data desensitization method for a distributed embedded system, characterized in that: include: If a data query request is received, determining a correlation coefficient according to the correlation degree of the queried data in the data query request; The queried data is located in at least two nodes; Determining the data accuracy of the queried data; The target desensitization method is determined according to the correlation coefficient and data accuracy of the queried data, and the queried data is desensitized based on the target desensitization method.

2. The method according to claim 1, characterized in that Determining a correlation coefficient according to the correlation degree of the queried data in the data query request includes: In a pre-established data association graph, determining a target node where the queried data is located; the nodes in the association graph correspond to nodes in the distributed embedded system; In the association graph, an association coefficient is determined according to the connection relationship between the target nodes.

3. The method according to claim 2, characterized in that In the association graph, determining the association coefficient according to the connection relationship between the target nodes includes: If there is a target node on the sensitive association path of the association graph, the association coefficient is determined as a ratio of a first number to a second number; the first number is the number of nodes in the target node that are on the sensitive association path; the second number is the number of nodes in the target node that are not on the sensitive association path; Otherwise, the correlation coefficient is determined to be 0.

4. The method according to claim 1, characterized in that: Determine the target desensitization method according to the correlation coefficient and data accuracy of the queried data, including: If the correlation coefficient is greater than 0 and the data accuracy is greater than the accuracy threshold, it is determined that the target desensitization processing method of the queried data is differential privacy; Otherwise, based on the association information between the pre-calibrated data type and the desensitization method, determine the target desensitization method corresponding to each queried data.

5. The method according to claim 4, characterized in that Desensitizing the queried data based on the target desensitization method, including: Determining a target noise distribution type in the noise distribution types in differential privacy according to a magnitude relationship between the correlation coefficient and the correlation coefficient threshold; Generate a target noise distribution that matches the target noise type according to the privacy budget and sensitivity; A noise value is determined based on the target noise distribution, and desensitization processing is performed on the queried data based on the noise value.

6. The method according to claim 5, characterized in that According to the magnitude relationship between the correlation coefficient and the correlation coefficient threshold, determining a target noise distribution type in the noise distribution type in differential privacy includes: If the correlation coefficient is greater than the correlation coefficient threshold, determining that the target noise distribution type is a Laplace distribution; Otherwise, the target noise distribution type is determined to be Gaussian distribution.

7. The method according to claim 5, characterized in that The process of determining the privacy budget includes: A privacy budget is determined according to the data accuracy; the data accuracy is inversely proportional to the privacy budget.

8. A data desensitization device for a distributed embedded system, characterized in that: include: A correlation coefficient determination module, configured to determine a correlation coefficient according to a correlation degree of the queried data in the data query request if a data query request is received; the queried data is located in at least two nodes; A data accuracy determination module, used to determine the data accuracy of the queried data; The desensitization processing module is used to determine a target desensitization method according to the correlation coefficient and data accuracy of the queried data, and perform desensitization processing on the queried data based on the target desensitization method.

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 that can be executed 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 execute the data desensitization method for a distributed embedded system described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the data desensitization method for a distributed embedded system according to any one of claims 1 to 7 when executed.

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