MPC-based government affair data privacy query method and system

Abstract

The invention relates to the technical field of government affair data query, in particular to an MPC-based government affair data privacy query method and system. Comprising the steps of setting a plurality of department nodes according to a government affair architecture; establishing a security sub-domain of each department node, and establishing an MPC network architecture based on all the security sub-domains; selecting a verification path according to the data query requirement, and constructing a calculation task package according to the verification path; and outputting a query result according to the MPC network architecture and the calculation task package. The MPC network architecture is established by constructing the security sub-domains of all government affair departments, the mixed processing requirement of data of different security levels in the government affair system is met by adjusting the differential trust strategies of all the security sub-domains, and meanwhile the expansibility of the MPC network architecture is improved. Original data of each department node is secretly segmented and stored in a distributed manner, the whole query calculation process is performed on encrypted fragments of the data, and any single node cannot peep complete information, so that the query efficiency and security of government affair private data are improved.

Description

Technical Field

[0001] This application relates to the field of government data query technology, and in particular to a government data privacy query method and system based on MPC. Background Technology

[0002] With the deepening of e-government digital transformation, the need for cross-departmental and cross-level data sharing and collaboration is becoming increasingly urgent. E-government data often contains a large amount of personal privacy or sensitive information. How to achieve efficient and controllable data querying and analysis while ensuring data privacy and security has become a key challenge in the current development of e-government.

[0003] Currently, common methods for querying government data mainly rely on centralized data management or authorized API calls. Centralized management requires aggregating data from various departments onto a unified platform, posing risks of data leakage, unclear responsibilities, and privacy exposure. While API calls can eliminate data aggregation, they typically require transmitting plaintext or simply anonymized data during queries, still failing to fundamentally prevent privacy leaks during transmission and computation. Furthermore, while traditional data anonymization and differential privacy technologies can enhance protection, they often come at the cost of data availability and query accuracy, making it difficult to meet the demands of high-precision government business collaboration. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for querying government data privacy based on MPC in order to solve the above-mentioned technical problems, thereby improving the efficiency and security of querying government privacy data.

[0005] In some embodiments of this application, an MPC network architecture is built by constructing security subdomains for each government department. By adjusting the differentiated trust policies of each security subdomain, the mixed processing requirements of data with different security levels in the government system are met, while improving the scalability of the MPC network architecture.

[0006] In some embodiments of this application, the original data of each department node is secretly segmented and distributed for storage. The entire query and calculation process is carried out on encrypted fragments of data, and no single node can see the complete information. Only the calculation result is output in the end, which improves the query efficiency and security of government privacy data.

[0007] In some embodiments of this application, computing resources are flexibly configured for different departments based on the data sensitivity and query load of each department node, and the best verification path is intelligently selected for each query to reduce unnecessary participants and computing overhead.

[0008] In some embodiments of this application, a method for querying government data privacy based on MPC is provided, including:

[0009] Multiple departmental nodes are set up according to the government architecture; Establish security subdomains for each department node, and build an MPC network architecture based on all security subdomains; Select a verification path based on the data query requirements, and construct a computation task package based on the verification path; Output query results based on the MPC network architecture and computation task packages.

[0010] In some embodiments of this application, the establishment of security subdomains for each department node includes: Select the target department node sequentially from all department nodes; Retrieve all stored data from the target department node; Multiple subsets of privacy data are generated based on the aggregated results of all stored data; Define the required number of nodes for each subset of privacy data; Set the primary deployment strategy for the target department nodes based on the total number of required nodes; Establish a security subdomain for the target department node based on the primary deployment strategy; The security subdomain includes multiple computing nodes; Establish security subdomains for each department node in sequence.

[0011] In some embodiments of this application, the primary deployment strategy for setting the target department node includes: Build multiple initial deployment strategies based on the total number of required nodes; A single initial setup strategy includes: the expected number of computing nodes and the mapping structure of each privacy data subset; Generate query load values ​​for each subset of privacy data based on the historical query parameters of the target department node; Select the target building strategy sequentially from all initial building strategies; Generate the expected operating values ​​of the target deployment strategy based on all query load values; The expected runtime values ​​for each building strategy are generated sequentially; Set the initial setup strategy corresponding to the maximum value among all expected running values ​​as the first-level setup strategy.

[0012] In some embodiments of this application, establishing the MPC network architecture includes: Establish a task gateway node; Obtain all computing nodes in each security subdomain and establish data connection channels between the task gateway node and each computing node; Establish an MPC network architecture, which includes: all data connection channels, all computing nodes, and task gateway nodes.

[0013] In some embodiments of this application, the step of selecting a verification path based on data query requirements includes: Generate the association values ​​between data query requirements and various department nodes; Set the department node corresponding to the maximum value among all associated values ​​as the first department node; The first-level verification path is set based on the first department node, and the number of splits of the first-level verification path is generated. Preset correlation threshold A1; Remove the first department node and select the maximum value 'a' of all associated values ​​among the remaining department nodes; If a > A1, set the department node corresponding to the maximum value a as the second department node; Based on the second department node, set up backup verification paths and generate the number of splits for backup verification paths; Set the verification path to be executed based on the primary verification path and the backup verification path.

[0014] In some embodiments of this application, the step of constructing a computing task package based on data query requirements includes: Obtain the plaintext identifier of the task based on the data query requirements; A first-level encryption instruction that sets the plaintext identifier based on the verification path to be executed; Generate a query identifier based on the encrypted instructions; Construct task logic functions; Generate a secret share set based on the number of splits in the verification path to be executed and the query identifier; Generate task identifier and task timestamp; Encapsulate the task logic function, secret share set, task identifier, and task timestamp; Generate an initial task package based on the encapsulation results; Set the secondary encryption instructions for the initial task package according to the verification path to be executed; A computational task package is generated based on the secondary encryption instructions.

[0015] In some embodiments of this application, the step of outputting query results based on the MPC network architecture and computing task packages includes: The task gateway node obtains the computing task package; Generate decryption instructions for the computation task package; Select multiple computing nodes to be run based on the decryption results; Construct the computational subtasks for each computing node to be run; The task gateway node obtains the verification results based on all computational subtasks; Output the query results based on the verification results.

[0016] In some embodiments of this application, a government data privacy query system based on MPC is provided, including: The central control unit is used to set up multiple departmental nodes according to the government architecture; Data units are used to establish security subdomains for each department node; The central control unit includes: The first processing module is used to establish an MPC network architecture based on all security subdomains; The second processing module is used to select the verification path based on data query requirements; The third processing module is used to construct a computing task package based on the verification path. The third processing module is also used to output query results based on the MPC network architecture and the computing task package. The data unit includes: The first module is used to select the target department node sequentially from all department nodes; Retrieve all stored data from the target department node; Multiple subsets of privacy data are generated based on the aggregated results of all stored data; Define the required number of nodes for each subset of privacy data; Set the primary deployment strategy for the target department nodes based on the total number of required nodes; Establish a security subdomain for the target department node based on the primary deployment strategy; The security subdomain includes multiple computing nodes; Establish security subdomains for each department node in sequence; The first optimization module is used to construct multiple initial building strategies based on the required number of nodes for each subset of privacy data in the target department nodes. A single initial setup strategy includes: the expected number of computing nodes and the mapping structure of each privacy data subset; Generate query load values ​​for each subset of privacy data based on the historical query parameters of the target department node; Select the target building strategy sequentially from all initial building strategies; Generate the expected operating values ​​of the target deployment strategy based on all query load values; The expected runtime values ​​for each building strategy are generated sequentially; Set the initial setup strategy corresponding to the maximum value among all expected running values ​​as the first-level setup strategy.

[0017] In some embodiments of this application, the first processing module is further configured to: Establish a task gateway node; Obtain all computing nodes in each security subdomain and establish data connection channels between the task gateway node and each computing node; Establish an MPC network architecture, which includes: all data connection channels, all computing nodes, and task gateway nodes.

[0018] In some embodiments of this application, the second processing module is further configured to: Generate the association values ​​between data query requirements and various department nodes; Set the department node corresponding to the maximum value among all associated values ​​as the first department node; The first-level verification path is set based on the first department node, and the number of splits of the first-level verification path is generated. Preset correlation threshold A1; Remove the first department node and select the maximum value 'a' of all associated values ​​among the remaining department nodes; If a > A1, set the department node corresponding to the maximum value a as the second department node; Based on the second department node, set up backup verification paths and generate the number of splits for backup verification paths; Set the verification path to be executed based on the primary verification path and the backup verification path.

[0019] Compared with existing technologies, the beneficial effects of the MPC-based government data privacy query method and system proposed in this application are as follows: By constructing secure subdomains for various government departments and building an MPC network architecture, the different trust policies of each secure subdomain can be adjusted to meet the mixed processing needs of data with different security levels in the government system, while improving the scalability of the MPC network architecture.

[0020] The raw data of each department node is secretly divided and distributed for storage. The entire query and calculation process is carried out on encrypted fragments of data, so no single node can see the complete information. Only the calculation result is output in the end, which improves the query efficiency and security of government privacy data.

[0021] Based on the data sensitivity and query load of each department node, computing resources are flexibly configured for different departments, and the best verification path is intelligently selected for each query to reduce unnecessary participants and computing overhead. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a preferred embodiment of a government data privacy query method based on MPC. Detailed Implementation

[0023] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0024] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0026] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0027] like Figure 1 As shown in the preferred embodiment of this application, a government data privacy query method based on MPC includes: S101: Set up multiple departmental nodes according to the government architecture; S102: Establish security subdomains for each department node, and build an MPC network architecture based on all security subdomains; S103: Select the verification path according to the data query requirements, and construct the calculation task package according to the verification path; S104: Output query results based on the MPC network architecture and computation task package.

[0028] Specifically, multiple government departments are selected based on the government architecture, and multiple department nodes are set based on all government departments, where a single department node represents a government department.

[0029] Specifically, establish security subdomains for each department node, including: Select the target department node sequentially from all department nodes; Retrieve all stored data from the target department node; Multiple subsets of privacy data are generated based on the aggregated results of all stored data; Define the required number of nodes for each subset of privacy data; Set the primary deployment strategy for the target department nodes based on the total number of required nodes; Establish a security subdomain for the target department node based on the primary deployment strategy; The security subdomain includes multiple computing nodes; Establish security subdomains for each department node in sequence.

[0030] Specifically, it iterates through all the stored data of the target department node (various categories of government data, such as personal property data and housing provident fund loan data in the Housing and Construction Bureau, and criminal record data and penalty record data in the Public Security Bureau), and selects anchor label data. In this application, the preferred anchor label data is the citizen's ID number.

[0031] Specifically, commercial cryptographic hash algorithms (such as SM3) are used to encrypt the anchor tag data (i.e., the original field of the citizen ID number is hashed), and the original field in the stored database is replaced with the hash value generated by encryption.

[0032] Specifically, all stored data in the target department node is aggregated based on the government affairs category, and multiple privacy data subsets are generated based on the aggregation results. In each privacy data subset, all data belongs to the same government affairs category.

[0033] Specifically, a key data table is constructed based on all data in the current privacy data subset. This key data table includes multiple record segments, each containing a hash value of the anchor tag data and a specific mapping value of the corresponding government data. (For example, in a privacy data subset that aggregates property registration data, the fields in the key data table include the hash value of the property owner's ID number, the unique property registration number, and the property status. A specific value in the property status field (e.g., the number "1") represents "valid owner-occupied housing").

[0034] Specifically, privacy permission values ​​are generated based on the sensitivity of the government data category corresponding to each subset of privacy data. The higher the sensitivity, the greater the corresponding privacy permission value. The greater the privacy permission value, the greater the number of required nodes. The mapping relationship between the two can be set based on historical parameters.

[0035] Specifically, the sensitivity of government data can be judged based on actual government needs. For example, the sensitivity of public security data is generally higher than that of basic public service data.

[0036] Specifically, the primary deployment strategy for the target department nodes includes: Build multiple initial deployment strategies based on the total number of required nodes; A single initial setup strategy includes: the expected number of computing nodes and the mapping structure of each privacy data subset; Generate query load values ​​for each subset of privacy data based on the historical query parameters of the target department node; Select the target building strategy sequentially from all initial building strategies; Generate the expected operating values ​​of the target deployment strategy based on all query load values; The expected runtime values ​​for each building strategy are generated sequentially; Set the initial setup strategy corresponding to the maximum value among all expected running values ​​as the first-level setup strategy.

[0037] Specifically, the required number of nodes for each privacy data subset corresponding to the target department node is obtained. Multiple initial deployment strategies are generated using an exhaustive approach. One extreme case of the initial deployment strategy is to set independent computing nodes for all privacy data subsets, i.e., setting the total number of required nodes as the number of computing nodes within the secure subdomain. The other extreme case is to select the maximum value among all required nodes as the number of computing nodes within the secure subdomain, mapping all privacy data subsets to these computing nodes. Based on the data boundaries of the computing nodes, the number of computing nodes is increased sequentially from the minimum number (i.e., the maximum value among all required nodes), and the mapping structure of each privacy data subset is dynamically adjusted, thereby generating multiple initial deployment strategies.

[0038] Specifically, the mapping structure of the privacy data subset refers to using the Shamir secret sharing protocol to divide the fields of each record in the key data table of the privacy data subset into multiple secret shares, and then the computing nodes holding these secret shares.

[0039] Specifically, the query load value is set based on the historical query frequency and peak query volume (i.e., the largest number of simultaneous queries in history) of the government data corresponding to the privacy data subset. The higher the historical query frequency and the higher the peak query volume, the larger the corresponding query load value. The rules for determining the query load value can be set based on historical parameters.

[0040] Specifically, a first reference value is set based on the number of computing nodes in the target deployment strategy. The fewer the number of technical nodes, the larger the corresponding first reference value. The mapping relationship between the two can be set based on historical parameters. A second reference value is set based on the sum of the differences between the expected load value and the load value threshold of each computing node (the load value threshold can be set based on historical operating parameters; the load value threshold refers to the reference value corresponding to the computing node reaching its optimal load state). The smaller the expected difference sum, the larger the corresponding second reference value. The mapping relationship between the two can be set based on historical parameters, and the first and second reference values ​​have the same value range. The expected operating value of the target deployment strategy is generated based on the sum of the first parameter value and the second reference value. The larger the operating value, the more suitable the corresponding initial deployment strategy is for the query needs of the target department's government privacy data.

[0041] Specifically, the expected load of a computing node is the sum of the query loads of all subsets of privacy data corresponding to the secret share it needs to store.

[0042] It is understood that in the above embodiments, computing resources are flexibly configured for different departments based on the data sensitivity and query load of each department node, and the best verification path is intelligently selected for each query to reduce unnecessary participants and computing overhead.

[0043] In a preferred embodiment of this application, an MPC network architecture is established, including: Establish a task gateway node; Obtain all computing nodes in each security subdomain and establish data connection channels between the task gateway node and each computing node; Establish an MPC network architecture, which includes: all data connection channels, all compute nodes, and task gateway nodes.

[0044] Specifically, when adding a new government department, it is only necessary to create a new security subdomain for it and connect it to the MPC network, without having to reconstruct the entire network, resulting in excellent system scalability.

[0045] It is understandable that in the above embodiments, by constructing security subdomains for each government department and building an MPC network architecture, the mixed processing requirements of data with different security levels in the government system can be met by adjusting the differentiated trust policies of each security subdomain, while improving the scalability of the MPC network architecture.

[0046] In a preferred embodiment of this application, the verification path is selected based on data query requirements, including: Generate the association values ​​between data query requirements and various department nodes; Set the department node corresponding to the maximum value among all associated values ​​as the first department node; The first-level verification path is set based on the first department node, and the number of splits of the first-level verification path is generated. Preset correlation threshold A1; Remove the first department node and select the maximum value 'a' of all associated values ​​among the remaining department nodes; If a > A1, set the department node corresponding to the maximum value a as the second department node; Based on the second department node, set up backup verification paths and generate the number of splits for backup verification paths; Set the verification path to be executed based on the primary verification path and the backup verification path.

[0047] Specifically, the text content in the data query request is parsed to determine if there is a departmental reference (i.e., which departments' data needs to be queried). At the same time, the data to be queried is obtained, and it is determined which departmental nodes the data to be queried is directly or indirectly stored in. This generates association values ​​between the data and each departmental node. The larger the association value, the more directly the results required by the data query request can be obtained in the current departmental node.

[0048] Specifically, when the primary verification path cannot obtain query results or the query load of the department node corresponding to the primary verification path is too high, the backup verification path is set as the verification path to be executed; otherwise, the primary verification path is set as the verification path to be executed.

[0049] Specifically, the correlation threshold can be set based on historical parameters. If the correlation value is greater than the preset correlation threshold, it means that the current target node can indirectly output the query results for the data query requirement. For example, when querying whether a resident owns property, the department node corresponding to the Housing and Urban-Rural Development Bureau is used as the first department node, where the property ownership status can be directly queried. At the same time, the Natural Resources Bureau is used as the second department node, and a corresponding backup verification path is constructed. When the Housing and Urban-Rural Development Bureau data cannot be matched, the backup verification path can be used for querying.

[0050] It is understandable that in the above embodiments, the main path is intelligently selected based on the "association value" between the query requirements and the department data, requiring only a portion of the security subdomains to participate in the calculation, which greatly reduces the number of participants in the MPC protocol, thereby reducing communication and computing overhead.

[0051] In a preferred embodiment of this application, a computational task package is constructed based on data query requirements, including: Obtain the plaintext identifier of the task based on the data query requirements; A first-level encryption instruction that sets the plaintext identifier based on the verification path to be executed; Generate a query identifier based on the encrypted instructions; Construct task logic functions; Generate a secret share set based on the number of splits in the verification path to be executed and the query identifier; Generate task identifier and task timestamp; Encapsulate the task logic function, secret share set, task identifier, and task timestamp; Generate an initial task package based on the encapsulation results; Set the secondary encryption instructions for the initial task package according to the verification path to be executed; A computational task package is generated based on the secondary encryption instructions.

[0052] Specifically, the task plaintext identifier is the ID number of the relevant personnel. Based on the encryption method (i.e., the first-level encryption instruction) of the anchor tag data in the corresponding security subdomain on the verification path to be executed, a corresponding hash value is generated, and this hash value is used as the query identifier.

[0053] Specifically, the corresponding task calculation process is constructed based on the query requirements, thereby generating the task logic function.

[0054] Specifically, the task timestamp is the point in time when the data query requirement is obtained.

[0055] Specifically, the initial task package is encrypted using the public key of the security subdomain corresponding to the verification path to be executed (i.e., a second-level encryption instruction), and a computation task package is generated based on the encryption result.

[0056] Specifically, the secret share set includes multiple secret shares.

[0057] Specifically, the query results are output based on the MPC network architecture and computation task packages, including: The task gateway node obtains the computing task package; Generate decryption instructions for the computation task package; Select multiple computing nodes to be run based on the decryption results; Construct the computational subtasks for each computing node to be run; The task gateway node obtains the verification results based on all computational subtasks; Output the query results based on the verification results.

[0058] Specifically, the computation task package is decoded using the corresponding private key in the secure subdomain to obtain the task logic function, secret share set, task identifier, and task timestamp. Multiple computation nodes to be run and specific computation parameters are selected based on the task logic function, and the above computation process is completed using MPC technology, thereby generating the corresponding verification result.

[0059] It is understood that in the above embodiments, the original data of each department node is secretly divided and distributed for storage. The entire query and calculation process is carried out on the encrypted fragments of data, and no single node can see the complete information. In the end, only the calculation result is output, which improves the query efficiency and security of government privacy data.

[0060] In another preferred embodiment of the MPC-based government data privacy query method based on any of the above preferred embodiments, this preferred embodiment provides an MPC-based government data privacy query system, including: The central control unit is used to set up multiple departmental nodes according to the government architecture; Data units are used to establish security subdomains for each department node; The central control unit includes: The first processing module is used to establish an MPC network architecture based on all security subdomains; The second processing module is used to select the verification path based on data query requirements; The third processing module is used to construct a computation task package based on the verification path. The third processing module is also used to output query results based on the MPC network architecture and the computation task package. The data unit includes: The first module is used to select the target department node sequentially from all department nodes; Retrieve all stored data from the target department node; Multiple subsets of privacy data are generated based on the aggregated results of all stored data; Define the required number of nodes for each subset of privacy data; Set the primary deployment strategy for the target department nodes based on the total number of required nodes; Establish a security subdomain for the target department node based on the primary deployment strategy; The security subdomain includes multiple computing nodes; Establish security subdomains for each department node in sequence; The first optimization module is used to construct multiple initial building strategies based on the required number of nodes for each subset of privacy data in the target department nodes. A single initial setup strategy includes: the expected number of computing nodes and the mapping structure of each privacy data subset; Generate query load values ​​for each subset of privacy data based on the historical query parameters of the target department node; Select the target building strategy sequentially from all initial building strategies; Generate the expected operating values ​​of the target deployment strategy based on all query load values; The expected runtime values ​​for each building strategy are generated sequentially; Set the initial setup strategy corresponding to the maximum value among all expected running values ​​as the first-level setup strategy.

[0061] In a preferred embodiment of this application, the first processing module is further configured to: Establish a task gateway node; Obtain all computing nodes in each security subdomain and establish data connection channels between the task gateway node and each computing node; Establish an MPC network architecture, which includes: all data connection channels, all compute nodes, and task gateway nodes.

[0062] Specifically, the second processing module is also used for: Generate the association values ​​between data query requirements and various department nodes; Set the department node corresponding to the maximum value among all associated values ​​as the first department node; The first-level verification path is set based on the first department node, and the number of splits of the first-level verification path is generated. Preset correlation threshold A1; Remove the first department node and select the maximum value 'a' of all associated values ​​among the remaining department nodes; If a > A1, set the department node corresponding to the maximum value a as the second department node; Based on the second department node, set up backup verification paths and generate the number of splits for backup verification paths; Set the verification path to be executed based on the primary verification path and the backup verification path.

[0063] Based on the first concept of this application, an MPC network architecture is built by constructing security subdomains for various government departments. By adjusting the differentiated trust policies of each security subdomain, the mixed processing requirements of data with different security levels in the government system are met, while improving the scalability of the MPC network architecture.

[0064] According to the second concept of this application, the original data of each department node is secretly divided and distributed for storage. The entire query and calculation process is carried out on the encrypted fragments of data, and no single node can see the complete information. In the end, only the calculation result is output, which improves the query efficiency and security of government privacy data.

[0065] According to the third concept of this application, computing resources are flexibly configured for different departments based on the data sensitivity and query load of each department node, and the best verification path is intelligently selected for each query, reducing unnecessary participants and computing overhead.

[0066] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A method for querying government data privacy based on MPC, characterized in that, include: Multiple departmental nodes are set up according to the government architecture; Establish security subdomains for each department node, and build an MPC network architecture based on all security subdomains; Select a verification path based on the data query requirements, and construct a computation task package based on the verification path; Output query results based on the MPC network architecture and computation task packages.

2. The method for querying government data privacy based on MPC as described in claim 1, characterized in that, The establishment of security subdomains for each department node includes: Select the target department node sequentially from all department nodes; Retrieve all stored data from the target department node; Multiple subsets of privacy data are generated based on the aggregated results of all stored data; Define the required number of nodes for each subset of privacy data; Set the primary deployment strategy for the target department nodes based on the total number of required nodes; Establish a security subdomain for the target department node based on the primary deployment strategy; The security subdomain includes multiple computing nodes; Establish security subdomains for each department node in sequence.

3. The method for querying government data privacy based on MPC as described in claim 2, characterized in that, The primary deployment strategy for setting target department nodes includes: Build multiple initial deployment strategies based on the total number of required nodes; A single initial setup strategy includes: the expected number of computing nodes and the mapping structure of each privacy data subset; Generate query load values ​​for each subset of privacy data based on the historical query parameters of the target department node; Select the target building strategy sequentially from all initial building strategies; Generate the expected operating values ​​of the target deployment strategy based on all query load values; The expected runtime values ​​for each building strategy are generated sequentially; Set the initial setup strategy corresponding to the maximum value among all expected running values ​​as the first-level setup strategy.

4. The method for querying government data privacy based on MPC as described in claim 2, characterized in that, The establishment of the MPC network architecture includes: Establish a task gateway node; Obtain all computing nodes in each security subdomain and establish data connection channels between the task gateway node and each computing node; Establish an MPC network architecture, which includes: all data connection channels, all computing nodes, and task gateway nodes.

5. The method for querying government data privacy based on MPC as described in claim 4, characterized in that, The step of selecting a verification path based on data query requirements includes: Generate the association values ​​between data query requirements and various department nodes; Set the department node corresponding to the maximum value among all associated values ​​as the first department node; The first-level verification path is set based on the first department node, and the number of splits of the first-level verification path is generated. Preset correlation threshold A1; Remove the first department node and select the maximum value 'a' of all associated values ​​among the remaining department nodes; If a > A1, set the department node corresponding to the maximum value a as the second department node; Based on the second department node, set up backup verification paths and generate the number of splits for backup verification paths; Set the verification path to be executed based on the primary verification path and the backup verification path.

6. The method for querying government data privacy based on MPC as described in claim 5, characterized in that, The step of constructing a computational task package based on data query requirements includes: Obtain the plaintext identifier of the task based on the data query requirements; A first-level encryption instruction that sets the plaintext identifier based on the verification path to be executed; Generate a query identifier based on the encrypted instructions; Construct task logic functions; Generate a secret share set based on the number of splits in the verification path to be executed and the query identifier; Generate task identifier and task timestamp; Encapsulate the task logic function, secret share set, task identifier, and task timestamp; Generate an initial task package based on the encapsulation results; Set the secondary encryption instructions for the initial task package according to the verification path to be executed; A computational task package is generated based on the secondary encryption instructions.

7. The method for querying government data privacy based on MPC as described in claim 6, characterized in that, The output of query results based on the MPC network architecture and computation task packages includes: The task gateway node obtains the computing task package; Generate decryption instructions for the computation task package; Select multiple computing nodes to be run based on the decryption results; Construct the computational subtasks for each computing node to be run; The task gateway node obtains the verification results based on all computational subtasks; Output the query results based on the verification results.

8. A government data privacy query system based on MPC, employing the government data privacy query method based on MPC as described in any one of claims 1-7, characterized in that, include: The central control unit is used to set up multiple departmental nodes according to the government architecture; Data units are used to establish security subdomains for each department node; The central control unit includes: The first processing module is used to establish an MPC network architecture based on all security subdomains; The second processing module is used to select the verification path based on data query requirements; The third processing module is used to construct a computing task package based on the verification path. The third processing module is also used to output query results based on the MPC network architecture and the computing task package. The data unit includes: The first module is used to select the target department node sequentially from all department nodes; Retrieve all stored data from the target department node; Multiple subsets of privacy data are generated based on the aggregated results of all stored data; Define the required number of nodes for each subset of privacy data; Set the primary deployment strategy for the target department nodes based on the total number of required nodes; Establish a security subdomain for the target department node based on the primary deployment strategy; The security subdomain includes multiple computing nodes; Establish security subdomains for each department node in sequence; The first optimization module is used to construct multiple initial building strategies based on the required number of nodes for each subset of privacy data in the target department nodes. A single initial setup strategy includes: the expected number of computing nodes and the mapping structure of each privacy data subset; Generate query load values ​​for each subset of privacy data based on the historical query parameters of the target department node; Select the target building strategy sequentially from all initial building strategies; Generate the expected operating values ​​of the target deployment strategy based on all query load values; The expected runtime values ​​for each building strategy are generated sequentially; Set the initial setup strategy corresponding to the maximum value among all expected running values ​​as the first-level setup strategy.

9. The government data privacy query system based on MPC as described in claim 8, characterized in that, The first processing module is also used for: Establish a task gateway node; Obtain all computing nodes in each security subdomain and establish data connection channels between the task gateway node and each computing node; Establish an MPC network architecture, which includes: all data connection channels, all computing nodes, and task gateway nodes.

10. The government data privacy query system based on MPC as described in claim 9, characterized in that, The second processing module is also used for: Generate the association values ​​between data query requirements and various department nodes; Set the department node corresponding to the maximum value among all associated values ​​as the first department node; The first-level verification path is set based on the first department node, and the number of splits of the first-level verification path is generated. Preset correlation threshold A1; Remove the first department node and select the maximum value 'a' of all associated values ​​among the remaining department nodes; If a > A1, set the department node corresponding to the maximum value a as the second department node; Based on the second department node, set up backup verification paths and generate the number of splits for backup verification paths; Set the verification path to be executed based on the primary verification path and the backup verification path.

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