Data transmission method and device, equipment, storage medium and program product

Abstract

The embodiment of the invention provides a data transmission method and device, equipment, a storage medium and a program product. The method comprises the following steps: performing sensitive field detection on initial data to obtain a detection result; sensitive fields in the detection results are subjected to de-identification processing, and a de-identification result table is obtained; rating the de-identified result table to obtain a rating result; transmitting the target data to the second client based on the rating result; wherein the target data comprises a non-sensitive field and a de-identified sensitive field. According to the embodiment of the invention, the initial data, the de-identification result table and the target data can be stored through the database in the first client or the second client, and an appropriate de-identification algorithm can be recommended for each sensitive field through the artificial intelligence model such as a set recommendation model, so that the sensitive fields are de-identified, and the user experience is improved. Therefore, data privacy protection is realized. According to the embodiment, it can be ensured that the target data does not comprise sensitive fields, and the security of data transmission is provided.

Description

Technical Field

[0001] The present application relates to the technical field of data transmission, and in particular to a data transmission method, apparatus, device, storage medium and program product. Background Art

[0002] With the rapid development of emerging fields such as the Internet and big data, people are increasingly recognizing the value of data. However, as data is made public and shared, the security of sensitive fields involved also arises.

[0003] In the scenario of cross-border data transmission, whether the transmitted data includes sensitive fields, the detection solutions of relevant technologies are difficult to meet the requirements, affecting the security of data transmission. Summary of the invention

[0004] The present application provides a data transmission method, apparatus, device, storage medium and program product, which can improve the security of data transmission.

[0005] In a first aspect, an embodiment of the present disclosure provides a data transmission method, which is applied to a first client, and includes: performing sensitive field detection on initial data to obtain a detection result; de-identifying the sensitive fields in the detection result to obtain a de-identified result table; wherein the de-identified result table includes de-identified sensitive fields; rating the de-identified result table to obtain a rating result; and transmitting target data to a second client based on the rating result; wherein the target data includes non-sensitive fields and the de-identified sensitive fields.

[0006] In the second aspect, the embodiments of the present disclosure also provide a data transmission device, which is applied to a first client, and includes: a sensitive field detection module, which is used to perform sensitive field detection on initial data to obtain a detection result; a de-identification processing module, which is used to perform de-identification processing on the sensitive fields in the detection result to obtain a de-identification result table; wherein the de-identification result table includes de-identified sensitive fields; a rating module, which is used to rate the de-identification result table to obtain a rating result; a data transmission module, which is used to transmit target data to a second client based on the rating result; wherein the target data includes non-sensitive fields and the de-identified sensitive fields.

[0007] In a third aspect, an embodiment of the present disclosure further provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data transmission method as described in the embodiment of the present disclosure.

[0008] In a fourth aspect, an embodiment of the present disclosure further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the data transmission method as described in the embodiment of the present disclosure.

[0009] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, implements the data transmission method as described in the embodiment of the present disclosure.

[0010] The technical solution of the disclosed embodiment is to perform sensitive field detection on the initial data to obtain a detection result; de-identify the sensitive fields in the detection result to obtain a de-identification result table; wherein the de-identification result table includes the de-identified sensitive fields; rate the de-identification result table to obtain a rating result; and transmit the target data to the second client based on the rating result; wherein the target data includes non-sensitive fields and the de-identified sensitive fields. The disclosed embodiment performs sensitive field detection on the initial data, de-identifies the detected sensitive fields, rates the de-identification result table, and determines whether to transmit the target data to the second client based on the rating result, thereby ensuring that the target data does not include sensitive fields and providing security for data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0012] Figure 1 A schematic diagram of a data transmission method flow provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a data transmission device provided by an embodiment of the present disclosure;

[0014] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] It should be understood that the various steps recorded in the method implementation of the present disclosure can be performed in different orders and / or in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect. The term "including" and its variations used herein are open inclusions, that is, "including but not limited to". It should be noted that the concepts of "first", "second", etc. mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units. It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more". It is understandable that the data involved in the present technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of the corresponding laws, regulations and relevant provisions.

[0017] Figure 1 The present invention provides a flow chart of a data transmission method according to an embodiment of the present invention. The method can be performed by a data transmission device, which can be implemented in the form of software and / or hardware. Optionally, the method can be implemented by an electronic device, which can be a mobile terminal, a PC, or a server. Figure 1 As shown, the method includes:

[0018] S110: Perform sensitive field detection on the initial data to obtain detection results.

[0019] In this embodiment, there is no restriction on the first client, for example, it can be a mobile phone, computer or other electronic device that can transmit data with the second client. In this embodiment, there is no restriction on the second client, for example, it can be a mobile phone, computer or other electronic device that can transmit data with the first client. The first client can be a data sender or a data receiver. The second client can be a data sender or a data receiver. In the following embodiments, unless otherwise specified, the first client will be taken as the data sender and the second client will be taken as the data receiver. In the cross-border data scenario, the physical locations of the first client and the second client can be in different countries.

[0020] In this embodiment, the specific content of the initial data is not limited, and it can be data related to any industry. The initial data is composed of multiple fields; for example, the initial data can include non-sensitive fields and sensitive fields. In this embodiment, the number of non-sensitive fields and sensitive fields is not limited.

[0021] In this embodiment, the initial data may be subjected to a sensitive field detection in any detection manner to obtain a detection result. Exemplarily, if a sensitive field exists, the detection result includes the sensitive field, and if no sensitive field exists, the detection result may be empty.

[0022] Optionally, sensitive field detection is performed on the initial data to obtain detection results, including: matching each field in the initial data with multiple sensitive identifiers in real time to obtain matching results; wherein the sensitive identifiers include direct sensitive identifiers and quasi-sensitive identifiers.

[0023] In this embodiment, after receiving the initial data, sensitive fields of the initial data are detected in real time to timely discover sensitive fields, reduce the risk of leakage of sensitive fields, and enhance data security.

[0024] Among them, sensitive identifiers are preset identifiers. The data corresponding to direct sensitive identifiers can directly determine the information of an individual, or can uniquely identify personal identity information, such as name, mobile phone number, etc. The data corresponding to quasi-sensitive identifiers cannot directly identify a specific individual, but can be combined with other information to identify the individual's information. For example, quasi-sensitive identifiers can be birthdays, gender, etc.

[0025] In this embodiment, the field consists of an identifier and a value, for example, it may be "Name: xxx".

[0026] In this embodiment, for any field, the identifier of the field can be matched with all sensitive identifiers, and if the matching result is a successful match, it means that the identifier of the corresponding field is a sensitive identifier. If the matching result is a failed match, it means that the identifier of the corresponding field is a non-sensitive identifier.

[0027] This embodiment can quickly identify sensitive fields in the initial data by performing sensitive identifier matching in real time, ensure timely privacy protection and compliance, improve the security of data transmission, and reduce the risk of sensitive field leakage.

[0028] S120: De-identify the sensitive fields in the detection result to obtain a de-identification result table.

[0029] The de-identification result table includes de-identified sensitive fields.

[0030] In this embodiment, there may be one or more sensitive fields in the detection result, and the corresponding sensitive fields may be de-identified using a de-identification algorithm corresponding to the sensitive fields, and after all sensitive fields are processed, a de-identification result table is obtained. The de-identification result table may include one or more de-identified sensitive fields.

[0031] Optionally, the sensitive fields in the detection results are de-identified to obtain a de-identification result table, including: determining the de-identification algorithm corresponding to each sensitive field based on a set recommendation model; and de-identifying the corresponding sensitive fields based on the de-identification algorithm corresponding to each sensitive field to obtain a de-identification result table.

[0032] In this embodiment, the set recommendation model is an artificial intelligence model pre-trained based on sensitive identifiers. According to the set recommendation model, the de-identification algorithm corresponding to each sensitive field is obtained. The de-identification algorithms for different sensitive fields can be the same or different. Exemplarily, the security level of the de-identification algorithm for the sensitive field containing the direct sensitive identifier is higher than that of the de-identification algorithm for the sensitive field containing the quasi-sensitive identifier.

[0033] Exemplarily, the de-identification algorithm may be an encryption algorithm, for example, encrypting the value in the sensitive field. The de-identification algorithm may be a hiding algorithm, for example, hiding or partially displaying the value in the sensitive field.

[0034] In this embodiment, by recommending a suitable de-identification algorithm for each sensitive field and de-identifying the corresponding sensitive field based on the de-identification algorithm corresponding to each sensitive field, the risk of leakage of sensitive fields can be effectively reduced, personal privacy and enterprise sensitive information can be protected, and the risk of re-identification can be reduced.

[0035] S130: Rating the de-identification result table to obtain a rating result.

[0036] In this embodiment, the de-identification result table can be rated in any manner, as long as the de-identification result table can be rated. For example, the de-identification result table can be rated using a K-anonymity model, an L-diversity model, or the like.

[0037] Optionally, the de-identification result table is rated to obtain a rating result, including: if the de-identification result table includes the data corresponding to the direct sensitive identifier, the rating result of the de-identification result table is the first level; if the de-identification result table includes the data corresponding to the quasi-sensitive identifier and does not include the data corresponding to the direct sensitive identifier, the rating result of the de-identification result table is the second level; if the de-identification result table does not include the data corresponding to the direct sensitive identifier and the re-identification risk is less than a set threshold, the rating result of the de-identification result table is the third level; if the de-identification result table includes aggregated data, the rating result of the de-identification result table is the fourth level.

[0038] The aggregated data is obtained by aggregating all sensitive fields. The re-identification risk can be obtained through a set risk assessment model. Re-identification refers to the process of re-associating de-identified sensitive fields to the original personal information subject. In this embodiment, there is no restriction on the aggregation method, for example, the aggregated data can be obtained through summary and statistical analysis. For aggregated data, personal identity cannot be identified based on the aggregated data.

[0039] In this embodiment, if the de-identification result table includes the data corresponding to the direct sensitive identifier, that is, in a specific environment, the personal identity can be identified based on the data corresponding to the direct sensitive identifier, then the rating result of the de-identification result table is the first level; if the de-identification result table includes the data corresponding to the quasi-sensitive identifier but does not include the data corresponding to the direct sensitive identifier, that is, the data corresponding to the direct sensitive identifier is deleted from the de-identification result table, but the data corresponding to the quasi-sensitive identifier is included, or the data corresponding to the direct sensitive identifier is processed and cannot directly identify the personal identity, then the rating result of the de-identification result table is the second level; if the de-identification result table does not include the data corresponding to the direct sensitive identifier and the re-identification risk is less than the set threshold, then the rating result of the de-identification result table is the third level; if the de-identification result table includes aggregated data, then the rating result of the de-identification result table is the fourth level.

[0040] In this embodiment, by rating the de-identification result table and dividing it into four levels, the effect of de-identification can be evaluated more accurately.

[0041] S140: Transmit the target data to the second client based on the rating result.

[0042] The target data includes non-sensitive fields and the de-identified sensitive fields.

[0043] In this embodiment, if the rating result meets the data transmission security requirements, it means that the de-identification effect meets the data transmission security requirements, and the target data can be sent to the second client when the second client accesses the target data or based on the data access request of the second client.

[0044] Optionally, the target data is transmitted to the second client based on the rating result, including: if the rating result of the de-identification result table is the third level or the fourth level, the target data is sent to the second client; if the rating result of the de-identification result table is the first level or the second level, the de-identified sensitive field is re-de-identified.

[0045] In this embodiment, if the rating result of the de-identification result table is the first level or the second level, feedback is given to the supervisor, and the supervisor notifies the first client so that the first client re-de-identifies the de-identified sensitive field. The supervisor generates an error message and notifies the second client so that the second client cannot access the target data.

[0046] In this embodiment, the method of sending the third-level or fourth-level target data to the second client can ensure that safe target data that does not include sensitive fields is sent to the second client, thereby reducing the risk of data leakage and protecting personal privacy.

[0047] Among them, the number of the first clients is at least one; the number of the second clients is at least one; the first client includes a first sandbox; the second client includes a second sandbox; the first sandbox includes a SQL gateway and an API gateway.

[0048] In this embodiment, the Structured Query Language (SQL) gateway can be a middleware, as an interface for accessing the driver of the SQL database, providing functions including but not limited to load balancing, SQL rule routing, intelligent routing, web operation and maintenance, security, etc. according to the configuration file. The Application Programming Interface (API) gateway can process all client requests, and its main functions include routing requests, load balancing, protocol conversion, etc.

[0049] Among them, the first sandbox and the second sandbox have the same function and can both be used to store data, which can meet the sharing scenarios within and between enterprises. Through database security, data content security, and infrastructure security, data access and controllable data export are fully guaranteed, so as to realize the safe and open sharing of data under the conditions of compliance and law. The first sandbox may include a first database (such as an SQL database). If the first sandbox acts as a data sender, the first database can be used to store initial data, de-identified result tables, target data, etc. If the first sandbox acts as a data receiver, the first sandbox can be used to store target data. The second sandbox may also include a second database (such as an SQL database). If the second sandbox acts as a data sender, the second database can be used to store initial data, de-identified result tables, target data, etc. If the second sandbox acts as a data receiver, the second sandbox can be used to store target data.

[0050] Exemplarily, after receiving the initial data, each first sandbox performs sensitive field detection on the initial data by adopting a SQL gateway and an API gateway with a distributed architecture to obtain a detection result; de-identifies the sensitive fields in the detection result to obtain a de-identified result table; wherein the de-identified result table includes de-identified sensitive fields; rates the de-identified result table to obtain a rating result; and sends the target data to at least one corresponding second sandbox based on the rating result.

[0051] In this embodiment, since the SQL gateway and the API gateway adopt a distributed architecture, each first sandbox can simultaneously receive data access requests from the corresponding second sandbox through the SQL gateway and the API gateway. After receiving the initial data, the following operations can be performed simultaneously: perform sensitive field detection on the initial data to obtain detection results; de-identify the sensitive fields in the detection results to obtain a de-identified result table; rate the de-identified result table to obtain a rating result; and send the target data to at least one second sandbox corresponding to the data access request based on the rating result. This can improve data processing performance, reduce blockages in the data transmission process, ensure that the transmitted data is processed in real time, and at the same time ensure that the target data does not include sensitive fields, thereby improving data transmission security.

[0052] The technical solution of the disclosed embodiment is to perform sensitive field detection on the initial data to obtain a detection result; de-identify the sensitive fields in the detection result to obtain a de-identification result table; wherein the de-identification result table includes the de-identified sensitive fields; rate the de-identification result table to obtain a rating result; and transmit the target data to the second client based on the rating result; wherein the target data includes non-sensitive fields and the de-identified sensitive fields. The disclosed embodiment performs sensitive field detection on the initial data, de-identifies the detected sensitive fields, rates the de-identification result table, and determines whether to transmit the target data to the second client based on the rating result, thereby ensuring that the target data does not include sensitive fields and providing security for data transmission.

[0053] Figure 2 A schematic diagram of a data transmission device structure provided by an embodiment of the present disclosure is shown in FIG. Figure 2 As shown, the device includes: a sensitive field detection module 210, a de-identification processing module 220, a rating module 230, and a data transmission module 240;

[0054] The sensitive field detection module 210 is used to perform sensitive field detection on the initial data and obtain the detection result;

[0055] A de-identification processing module 220, configured to perform de-identification processing on the sensitive fields in the detection result to obtain a de-identification result table; wherein the de-identification result table includes the de-identified sensitive fields;

[0056] A rating module 230, configured to rate the de-identification result table to obtain a rating result;

[0057] The data transmission module 240 is used to transmit the target data to the second client based on the rating result; wherein the target data includes the non-sensitive field and the de-identified sensitive field.

[0058] The technical solution of the disclosed embodiment is to perform sensitive field detection on the initial data through a sensitive field detection module to obtain a detection result; to perform de-identification processing on the sensitive fields in the detection result through a de-identification processing module to obtain a de-identification result table; wherein the de-identification result table includes de-identified sensitive fields; to rate the de-identification result table through a rating module to obtain a rating result; and to transmit the target data to the second client based on the rating result through a data transmission module; wherein the target data includes non-sensitive fields and the de-identified sensitive fields. The disclosed embodiment performs sensitive field detection on the initial data, de-identifies the detected sensitive fields, rates the de-identification result table, and determines whether to transmit the target data to the second client according to the rating result, thereby ensuring that the target data does not include sensitive fields and providing data transmission security.

[0059] The initial data consists of multiple fields.

[0060] Optionally, the sensitive field detection module is specifically used to: match each field in the initial data with multiple sensitive identifiers in real time to obtain a matching result; wherein the sensitive identifiers include direct sensitive identifiers and quasi-sensitive identifiers.

[0061] Optionally, the de-identification processing module is specifically used to: determine the de-identification algorithm corresponding to each sensitive field based on a set recommendation model; de-identify the corresponding sensitive field based on the de-identification algorithm corresponding to each sensitive field, and obtain a de-identification result table.

[0062] Optionally, the rating module is specifically used to: if the de-identification result table includes the data corresponding to the direct sensitive identifier, the rating result of the de-identification result table is the first level; if the de-identification result table includes the data corresponding to the quasi-sensitive identifier and does not include the data corresponding to the direct sensitive identifier, the rating result of the de-identification result table is the second level; if the de-identification result table does not include the data corresponding to the direct sensitive identifier and the re-identification risk is less than a set threshold, the rating result of the de-identification result table is the third level; if the de-identification result table includes aggregated data, the rating result of the de-identification result table is the fourth level; wherein the aggregated data is obtained by aggregation based on all sensitive fields.

[0063] Optionally, the data transmission module is specifically used to: if the rating result of the de-identification result table is the third level or the fourth level, send the target data to the second client; if the rating result of the de-identification result table is the first level or the second level, re-de-identify the de-identified sensitive field.

[0064] Among them, the number of the first clients is at least one; the number of the second clients is at least one; the first client includes a first sandbox; the second client includes a second sandbox; the first sandbox includes a SQL gateway and an API gateway.

[0065] The data transmission device provided in the embodiments of the present disclosure can execute the data transmission method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0066] Figure 3 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, 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 application described and / or required herein.

[0067] like Figure 3As 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.

[0068] 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.

[0069] 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, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as method data transmission.

[0070] In some embodiments, the method data transmission 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 method data transmission described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method data transmission in any other suitable manner (e.g., by means of firmware).

[0071] 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), load 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.

[0072] The computer programs for implementing the methods of the present application 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 programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs 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.

[0073] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction 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.

[0074] 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).

[0075] 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.

[0076] 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.

[0077] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the data transmission method provided in any embodiment of the present application.

[0078] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present application, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0079] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A data transmission method, characterized in that: Applied to the first client, including: Perform sensitive field detection on the initial data to obtain the detection results; De-identifying the sensitive fields in the detection result to obtain a de-identified result table; wherein the de-identified result table includes the de-identified sensitive fields; Rating the de-identification result table to obtain a rating result; The target data is transmitted to the second client based on the rating result; wherein the target data includes non-sensitive fields and the de-identified sensitive fields.

2. The method according to claim 1, characterized in that in, The initial data is composed of multiple fields; sensitive fields are detected on the initial data to obtain detection results, including: Each field in the initial data is matched with a plurality of sensitive identifiers in real time to obtain a matching result; wherein the sensitive identifiers include direct sensitive identifiers and quasi-sensitive identifiers.

3. The method according to claim 1, characterized in that De-identification is performed on the sensitive fields in the detection result to obtain a de-identification result table, including: Determine the de-identification algorithm corresponding to each sensitive field based on the set recommendation model; Based on the de-identification algorithm corresponding to each sensitive field, de-identification processing is performed on the corresponding sensitive field to obtain a de-identification result table.

4. The method according to claim 2, characterized in that: The de-identification result table is rated to obtain a rating result, including: If the de-identification result table includes data corresponding to the direct sensitive identifier, the rating result of the de-identification result table is the first level; If the de-identification result table includes the data corresponding to the quasi-sensitive identifier and does not include the data corresponding to the direct sensitive identifier, the rating result of the de-identification result table is the second level; If the de-identification result table does not include the data corresponding to the direct sensitive identifier and the re-identification risk is less than the set threshold, the rating result of the de-identification result table is the third level; If the de-identification result table includes aggregated data, the rating result of the de-identification result table is the fourth level; wherein the aggregated data is obtained by aggregating all sensitive fields.

5. The method according to claim 4, characterized in that Transmitting the target data to the second client based on the rating result includes: If the rating result of the de-identification result table is the third level or the fourth level, sending the target data to the second client; If the rating result of the de-identification result table is the first level or the second level, the de-identified sensitive field is de-identified again.

6. The method according to claim 1, characterized in that The number of the first clients is at least one; the number of the second clients is at least one; the first client includes a first sandbox; the second client includes a second sandbox; the first sandbox includes a SQL gateway and an API gateway.

7. A data transmission device, characterized in that: Applied to the first client, including: A sensitive field detection module is used to detect sensitive fields on initial data and obtain detection results; A de-identification processing module, used to perform de-identification processing on the sensitive fields in the detection results to obtain a de-identification result table; wherein the de-identification result table includes the de-identified sensitive fields; A rating module, used to rate the de-identification result table to obtain a rating result; A data transmission module is used to transmit the target data to the second client based on the rating result; wherein the target data includes non-sensitive fields and the de-identified sensitive fields.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the data transmission method according to any one of claims 1 to 6.

9. A storage medium comprising computer executable instructions, wherein the computer executable instructions are used to perform the data transmission method according to any one of claims 1 to 6 when executed by a computer processor.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the data transmission method according to any one of claims 1 to 6.

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