Data processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method relates to the field of privacy computing, and comprises the following steps: when a computing request is received, extracting request information from the computing request, the computing request being used for requesting privacy computing of financial data; demand information of the calculation request is determined based on the request information, and the demand information comprises at least one of a performance demand index value and a security demand index value; determining a target calculation mode from preset calculation modes based on the demand information; and performing privacy calculation based on the target calculation mode and the request information to obtain a calculation result for the calculation request. Through the data privacy calculation method and device, the problem that privacy calculation is low in flexibility due to the fact that data privacy calculation is carried out in a fixed mode in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of privacy computing, in particular to a data processing method and device, a storage medium and an electronic device. BACKGROUND

[0002] In the digital era, data has become the core asset of various industries, especially in the financial field, the protection and compliance use of data are particularly important. With the increasing popularity of data-driven business models, financial institutions have increasing demand for data sharing in improving service efficiency and optimizing risk control strategies, but at the same time, they also face the huge risk of data privacy leakage. In order to balance between data sharing and privacy protection, privacy computing technology emerges as the times require and becomes an important line of defense in financial data processing.

[0003] At present, the related technology often uses a fixed privacy computing method for data protection, which has certain limitations and deficiencies. For example, in some low-sensitivity data processing scenarios, using complex MPC (Secure Multi-Party Computation) algorithms will cause resource waste, and in high-sensitivity scenarios, simple privacy protection measures may not be enough to resist potential attacks and leakage risks. Therefore, the related technology has the problem of low privacy computing flexibility.

[0004] In view of the above problems in the related art, no effective solution has been proposed so far. SUMMARY

[0005] The main purpose of the present application is to provide a data processing method, device, storage medium and electronic device to solve the problem of low privacy computing flexibility caused by the fixed method of data privacy computing in the related art.

[0006] In order to achieve the above purpose, according to one aspect of the present application, a data processing method is provided. The method comprises: in the case of receiving a computing request, extracting request information from the computing request, wherein the computing request is used to request privacy computing on financial data; determining demand information of the computing request based on the request information, wherein the demand information includes at least one of the following: performance demand index value, security demand index value; determining a target computing method from the preset computing method based on the demand information; performing privacy computing based on the target computing method and the request information to obtain a computing result for the computing request.

[0007] Optionally, the data processing method further comprises: determining a participant associated with the target data from the request information, and determining a number of sensitive data in the target data according to the request information, wherein the target data is data used to calculate the calculation result; determining a first performance requirement value based on the number of participants, and determining a second performance requirement value based on the number of sensitive data; and determining a performance requirement index value based on the first performance requirement value and the second performance requirement value.

[0008] Optionally, the data processing method further comprises: determining a number of sensitive data in the target data according to the request information, wherein the target data is data used to calculate the calculation result; and determining a security requirement index value based on the number of sensitive data.

[0009] Optionally, the data processing method further comprises: determining a target index value based on the requirement information; determining the first calculation method as the target calculation method when the target index value is greater than a preset threshold; and determining the second calculation method as the target calculation method when the target index value is less than or equal to the preset threshold.

[0010] Optionally, the data processing method further comprises: obtaining a performance weight coefficient and a security weight coefficient; calculating a first value based on the performance weight coefficient and the performance requirement index value; calculating a second value based on the security weight coefficient and the security requirement index value; and determining the target index value based on the first value and the second value.

[0011] Optionally, the data processing method further comprises: determining a participant associated with the target data from the request information, and sending a data acquisition request to the participant, wherein the target data is data used to calculate the calculation result, the participant determines the target data after desensitization based on the data acquisition request; receiving the target data after desensitization fed back by the participant, and performing privacy calculation based on the target calculation method, the target data after desensitization, and the calculation requirement indicated by the request information to obtain the calculation result.

[0012] Optionally, the data processing method further comprises: determining the target data based on the data acquisition request; determining sensitive data from the target data; determining a target desensitization strategy based on the data type of the sensitive data, the business scenario indicated in the data acquisition request, and the data access object; and performing desensitization processing on the target data based on the target desensitization strategy to obtain the target data after desensitization.

[0013] To achieve the above object, according to another aspect of the present application, a data processing apparatus is provided. The apparatus comprises: an extraction module configured to extract request information from a computing request, wherein the computing request is used to request privacy computation on financial data; a first determination module configured to determine demand information of the computing request based on the request information, wherein the demand information comprises at least one of the following: a performance demand index value, a security demand index value; a second determination module configured to determine a target computing mode from preset computing modes based on the demand information; and a computation module configured to perform privacy computation based on the target computing mode and the request information to obtain a computation result for the computing request.

[0014] Optionally, the first determination module further comprises: a first determination submodule configured to determine a participant associated with target data from the request information, and determine a number of sensitive data in the target data according to the request information, wherein the target data is data used to compute the computation result; a second determination submodule configured to determine a first performance demand value based on the number of participants, and determine a second performance demand value based on the number of sensitive data; and a third determination submodule configured to determine the performance demand index value based on the first performance demand value and the second performance demand value.

[0015] Optionally, the first determination module further comprises: a fourth determination submodule configured to determine a number of sensitive data in target data according to the request information, wherein the target data is data used to compute the computation result; and a fifth determination submodule configured to determine the security demand index value based on the number of sensitive data.

[0016] Optionally, the second determination module further comprises: a sixth determination submodule configured to determine a target index value based on the demand information; a seventh determination submodule configured to determine the first computing mode as the target computing mode in a case that the target index value is greater than a preset threshold; and an eighth determination submodule configured to determine the second computing mode as the target computing mode in a case that the target index value is less than or equal to the preset threshold.

[0017] Optionally, the sixth determination submodule further comprises: an acquisition unit configured to acquire a performance weight coefficient and a security weight coefficient; a first calculation unit configured to calculate a first value based on the performance weight coefficient and the performance demand index value; a second calculation unit configured to calculate a second value based on the security weight coefficient and the security demand index value; and a first determination unit configured to determine the target index value based on the first value and the second value.

[0018] Optionally, the calculation module further includes: a sending submodule, used to determine the participants associated with the target data from the request information and send a data acquisition request to the participants, wherein the target data is the data used to calculate the calculation result, and the participants determine the de-identified target data based on the data acquisition request; and a calculation submodule, used to receive the de-identified target data fed back by the participants, perform privacy calculations based on the target calculation method, the de-identified target data, and the calculation requirements indicated by the request information, and obtain the calculation result.

[0019] Optionally, the sending submodule further includes: a second determining unit, used to determine target data based on the data acquisition request; a third determining unit, used to determine sensitive data from the target data; a fourth determining unit, used to determine the target de-identification strategy based on the data type of the sensitive data, the business scenario indicated in the data acquisition request, and the data access object; and a processing unit, used to perform de-identification processing on the target data based on the target de-identification strategy to obtain the de-identified target data.

[0020] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is provided, which includes a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the above-described data processing method.

[0021] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described data processing method during runtime.

[0022] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0023] In this embodiment of the application, by determining the demand information of the computing request based on the request information of the computing request, the degree of demand of the computing request in at least one aspect of performance and security is effectively determined. By determining the target computing method from the preset computing methods based on the demand information, the target computing method is dynamically determined according to the actual demand of the computing request. Thus, when performing privacy computing based on the target computing method and the request information, the flexibility of privacy computing can be effectively improved.

[0024] Therefore, the method provided in this application achieves the goal of dynamically determining the privacy computation method, realizes the technical effect of improving the flexibility of privacy computation, and solves the technical problem that related technologies use a fixed method to perform privacy computation on data, resulting in low flexibility of privacy computation. Attached Figure Description

[0025] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0026] Figure 1 This is a hardware structure block diagram of a computer terminal provided according to an embodiment of this application;

[0027] Figure 2 This is a flowchart of a data processing method provided according to an embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a data processing apparatus provided according to an embodiment of this application;

[0029] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0033] Example 1

[0034] According to an embodiment of this application, an embodiment of a data processing method is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a data processing method is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor (MCU) or a field-programmable gate array (FPGA), etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output (I / O) interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0036] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data processing method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the aforementioned data processing method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0038] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0039] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0040] Under the aforementioned operating environment, this application provides the following: Figure 2 The data processing method shown. Figure 2 This is a flowchart of the data processing method according to Embodiment 1 of this application.

[0041] Step S201: Upon receiving a computation request, extract request information from the computation request, wherein the computation request is used to request privacy computation on financial data.

[0042] Optionally, electronic devices, application systems, servers, and other devices can be used as the execution subject of this application. In this embodiment, the target processing system is used as the execution subject to execute the above-described data processing method.

[0043] In an optional embodiment, a computation request refers to a request issued by a financial institution to perform privacy-preserving computations on specific financial data. Specific financial data can also be understood as data used to obtain the results of the privacy-preserving computation. The participants (i.e., data sources) involved in the aforementioned specific financial data can be one or more. For example, specific financial data may include financial data from multiple financial institutions. Privacy-preserving computation refers to a set of technologies that enable analytical computation while protecting data from disclosure, achieving the goal of data being "usable but invisible," and supporting the secure release of data value.

[0044] The request information in the calculation request may include the participants associated with the specific financial data, the data type of the specific financial data, and the calculation objective (or calculation indicator, calculation requirement). For example, the calculation objective may be to request joint analysis of data from at least two financial institutions to identify data intersections among multiple financial institutions (e.g., common users, identical large transactions, etc.). Another example is to identify data intersections among multiple financial institutions and obtain specific information about the data intersections (e.g., specific transaction data of common users, timestamp information of identical large transactions). Yet another example is to perform risk prediction and compliance checks on financial data from a single financial institution.

[0045] Step S202: Determine the requirement information for the calculation request based on the request information, wherein the requirement information includes at least one of the following: performance requirement index value, security requirement index value.

[0046] In an optional embodiment, the request information includes at least one of a performance requirement index value and a security requirement index value, and the target processing system can directly determine the requirement information from the request information.

[0047] In an optional embodiment, the target processing system can determine the participants associated with specific financial data and the amount of sensitive data based on the request information, and then determine the performance requirement index value and the security requirement index value based on the number of participants and the amount of sensitive data. For example, the performance requirement index value is determined based on the number of participants and the amount of sensitive data, and the security requirement index value is determined based on the amount of sensitive data.

[0048] In an optional embodiment, the requirement information includes performance requirement index values ​​and security requirement index values.

[0049] Optionally, the performance requirement index value is used to reflect the resource consumption level of the computing task corresponding to the computing request (i.e., the performance requirement level), and the security requirement index value is used to reflect the data sensitivity of the computing task corresponding to the computing request (i.e., the security requirement level).

[0050] Step S203: Based on the requirement information, determine the target calculation method from the preset calculation methods.

[0051] In an optional embodiment, the preset computation methods include a first computation method and a second computation method. The first computation method is used to perform privacy-preserving computation using secure multi-party computation (SMPC) technology, and the second computation method is used to perform privacy-preserving computation within a Trusted Execution Environment (TEE). Secure multi-party computation technology refers to a technical system that allows multiple participants to complete computational tasks without disclosing their respective private data, through specially designed cryptographic algorithms and protocols, without the need for a trusted third party. A TEE is a secure area built on a computing platform using hardware and software methods, capable of ensuring the confidentiality and integrity of code and data. This technology enables the processing of sensitive data within an independent secure area.

[0052] In an optional embodiment, the target processing system may determine the target calculation method from a preset calculation method based on the maximum value in the demand information.

[0053] In another optional embodiment, the target processing system performs a weighted summation (or average calculation) on the numerical values ​​of the demand information, thereby determining the target calculation method from a preset calculation method based on the result of the weighted summation (or average calculation).

[0054] For example, if the data sensitivity is high and the computing resource requirements are high, then MPC can be selected; if the data sensitivity is low and the computing resource requirements are low, then TEE can be selected.

[0055] Step S204: Perform privacy calculations based on the target calculation method and request information to obtain the calculation result for the calculation request.

[0056] Optionally, after selecting the target computation method, the target processing system can call the corresponding privacy computation module, such as the MPC algorithm or TEE kernel, to perform privacy computation on the data according to the computation requirements indicated by the request information, so as to ensure the confidentiality and integrity of the data during the processing.

[0057] In this embodiment of the application, by determining the demand information of the computing request based on the request information of the computing request, the degree of demand of the computing request in at least one aspect of performance and security is effectively determined. By determining the target computing method from the preset computing methods based on the demand information, the target computing method is dynamically determined according to the actual demand of the computing request. Thus, when performing privacy computing based on the target computing method and the request information, the flexibility of privacy computing can be effectively improved.

[0058] Therefore, the method provided in this application achieves the goal of dynamically determining the privacy computation method, realizes the technical effect of improving the flexibility of privacy computation, and solves the technical problem that related technologies use a fixed method to perform privacy computation on data, resulting in low flexibility of privacy computation.

[0059] Optionally, in the data processing method provided in this application embodiment, when the demand information includes a performance demand index value, determining the demand information for calculating the request based on the request information includes: determining the participants associated with the target data from the request information, and determining the number of sensitive data in the target data according to the request information, wherein the target data is the data used to calculate the calculation result; determining a first performance demand value based on the number of participants, and determining a second performance demand value based on the number of sensitive data; and determining a performance demand index value based on the first performance demand value and the second performance demand value.

[0060] Optionally, the target data refers to the specific dataset mentioned in the computation request, which is used to obtain the computation result. The system performs privacy-preserving computations on this specific dataset to obtain the result. Participants refer to financial institutions that need to contribute relevant data. The request information may record the participants and data descriptions of the data each participant needs to provide. The system can directly identify the participants associated with the target data from the request information. Optionally, the system can determine the quantity of sensitive data in the target data based on the data description information. For example, the data description information may include data indicators that should be included in the target data (e.g., customer ID, customer name, transaction amount, etc.). The system has a pre-defined list of sensitive data indicators. The target processing system can identify data indicators belonging to the sensitive data indicator list in the data description information as sensitive data, thereby determining the quantity of sensitive data in the target data.

[0061] The system can pre-define a mapping table between the number of participants and performance requirements. For example, as the number of participants increases, the performance requirement also increases accordingly; the two are positively correlated, which means that more computing resources and faster processing speeds are needed. After determining the number of participants, the first performance requirement is determined based on this mapping table.

[0062] The system can also pre-define a mapping table between the amount of sensitive data and performance requirements. For example, as the amount of sensitive data increases, the performance requirement also increases accordingly, showing a positive correlation. This means that more stringent data processing and encryption techniques may be needed, which will increase computational complexity. After determining the amount of sensitive data, a second performance requirement is determined based on this mapping table.

[0063] After determining the first performance requirement value and the second performance requirement value, a performance requirement index value is determined based on the first performance requirement value and the second performance requirement value. For example, the performance requirement index value can be calculated by adding (or weighted summing) the first performance requirement value and the second performance requirement value.

[0064] It should be noted that the above method quantifies two key aspects of performance requirements in computing requests: the complexity of multi-party involvement and the burden of sensitive data protection, thereby improving the accuracy of the determined performance requirement index value.

[0065] Optionally, in the data processing method provided in this application embodiment, when the demand information includes a security demand index value, determining the demand information for calculating the request based on the request information includes: determining the number of sensitive data in the target data according to the request information, wherein the target data is the data used to calculate the calculation result; and determining the security demand index value based on the number of sensitive data.

[0066] Optionally, the request information may include data descriptions of the participating parties and the data each party needs to provide. The system can determine the quantity of sensitive data in the target data based on these data descriptions. For example, the data description information may include data indicators that should be present in the target data (such as customer ID, customer name, transaction amount, etc.). The system may have a pre-defined list of sensitive data indicators. The target processing system can identify data indicators in the data description information that belong to this list as sensitive data, thereby determining the quantity of sensitive data in the target data.

[0067] The system can also pre-define a mapping table between the quantity of sensitive data and the security requirement value. For example, as the quantity of sensitive data increases, the security requirement value also increases accordingly, showing a positive correlation. This indicates that more stringent or extensive data encryption and anonymization processing is required during the calculation process. After determining the quantity of sensitive data, the security requirement index value is determined based on this mapping table.

[0068] It should be noted that the above method enables the accurate determination of the safety demand index value.

[0069] Optionally, in the data processing method provided in this application embodiment, the preset calculation methods include a first calculation method and a second calculation method. The first calculation method is used to perform privacy calculation using secure multi-party computation technology, and the second calculation method is used to perform privacy calculation in a trusted execution environment. The step of determining a target calculation method from the preset calculation methods based on demand information includes: determining a target index value based on demand information; determining the first calculation method as the target calculation method if the target index value is greater than a preset threshold; and determining the second calculation method as the target calculation method if the target index value is less than or equal to the preset threshold.

[0070] Optionally, if the requirement information includes only a single value, that value in the requirement information can be directly determined as the target index value. For example, if the requirement information only includes a performance requirement index value, that performance requirement index value can be directly determined as the target index value.

[0071] Optionally, if the requirement information includes performance requirement index values ​​and security requirement index values, the target index value can be calculated by direct summation or weighted summation.

[0072] In an optional embodiment, the performance requirement index value and the security requirement index value are positively correlated with the target index value.

[0073] If the target index value is greater than a preset threshold, the performance and security requirements of the computation task are determined to be relatively high, and the first computation method is selected as the target computation method. If the target index value is less than or equal to the preset threshold, the performance and security requirements of the computation task are determined to be relatively low, and the second computation method is selected as the target computation method.

[0074] In an optional embodiment, the first computation method may include Private Set Intersection (PSI). Private Set Intersection is a specialized protocol in the field of secure multi-party computation that allows a group of participants to input private sets and jointly compute the intersection of the sets, ensuring that no additional element information is leaked except for the intersection result. For example, when there are two participants, the ECDH-PSI (Elliptic Curve Diffie-Hellman Private Set Intersection) protocol can be used to achieve secure computation between the two parties. ECDH-PSI is a combined algorithm based on the elliptic curve Diffie-Hellman protocol and the private set intersection protocol, used to solve the privacy protection problem between two sets. For example, assume that participants Alice and Bob have shared curve parameters (elliptic curve E, order N, base point G).

[0075] 1. Alice generates a random integer 'a' as the private key;

[0076] (1) Alice on the set The hash value of x at a certain point on E is obtained. , where the set This could be user data from the participating party Alice (e.g., at least including user IDs). In an optional embodiment, the set For the data in the target data. In another alternative embodiment, the set This refers to the data in the target data after anonymization.

[0077] (2) Alice on the set Performing an ECC dot product with private key 'a' yields A = A is sent to the target processing system.

[0078] 2. Bob generates a random integer b as the private key;

[0079] (1) Target processing system Performing an ECC dot product with private key b yields B = .

[0080] (2) Bob on the set The hash value of x at a certain point on E is obtained. , where the set This could be user data from participant Bob (e.g., at least including a user ID). In an alternative embodiment, the set For the data in the target data. In another alternative embodiment, the set This refers to the data in the target data after anonymization.

[0081] (3) Bob to Performing an ECC dot product with private key b yields C= The system sends C to the target processing system.

[0082] 3. The target processing system performs an ECC dot product on C and the private key a to obtain C`= .

[0083] The target processing system obtains the intersection of sets by comparing equal elements in C' and B; this part is the result of the privacy set intersection. After obtaining the privacy set intersection result, it is used as the computation result of the computation request. Relevant data is then fed back to the requester based on the computation goal of the request. For example, if the computation goal is to request joint analysis of data from at least two financial institutions to identify common users of these two institutions, the user ID intersection from the computation result is directly fed back to the requester. As another example, if the computation goal is to request the identification of the data intersection of multiple financial institutions and to obtain specific information about the data intersection (specific transaction data of common users), the target processing system can simultaneously request Alice and Bob to provide the transaction data (possibly anonymized) corresponding to each user ID when requesting the user ID data. Then, after obtaining the privacy set intersection result (i.e., the user ID intersection), the system determines the transaction data corresponding to the user ID intersection and feeds back the user ID intersection and its corresponding transaction data to the requester.

[0084] Optionally, if there are multiple participants (more than 2), the Multi-party Private Set Intersection (MP-PSI) algorithm can be used for privacy computation.

[0085] It should be noted that the above method enables intelligent selection of privacy-preserving computation methods for secure multi-party computation and trusted execution environments. This process not only considers the performance and / or security requirements of the computation task, but also automatically determines the appropriate computation method by comparing preset thresholds and target index values, reducing the uncertainty of human decision-making and improving the efficiency and accuracy of decision-making.

[0086] Optionally, in the data processing method provided in this application embodiment, when the demand information includes a performance demand index value and a security demand index value, determining the target index value based on the demand information includes: obtaining a performance weight coefficient and a security weight coefficient; calculating a first value based on the performance weight coefficient and the performance demand index value; calculating a second value based on the security weight coefficient and the security demand index value; and determining the target index value based on the first value and the second value.

[0087] Optionally, the performance weighting coefficient and the security weighting coefficient can be preset values.

[0088] The target processing system can multiply the performance weighting coefficient and the performance requirement index value to obtain a first value, and multiply the security weighting coefficient and the security requirement index value to obtain a second value. Then, the first and second values ​​are added together to obtain the target index value.

[0089] In an optional embodiment, the preset threshold can be greater than or equal to the target value, which refers to the minimum value of the performance requirement index when the number of participants is a preset number (e.g., 2 or 3). This ensures that when the number of participants is at least a preset number, the first calculation method will definitely be determined as the target calculation method.

[0090] It should be noted that by using a weighted summation method to calculate the target index value, the accuracy of the calculated target index value is further improved.

[0091] Optionally, in the data processing method provided in this application embodiment, privacy calculation is performed based on the target calculation method and request information to obtain the calculation result for the calculation request, including: determining the participants associated with the target data from the request information and sending a data acquisition request to the participants, wherein the target data is the data used to calculate the calculation result, and the participants determine the de-identified target data based on the data acquisition request; receiving the de-identified target data fed back by the participants, and performing privacy calculation based on the target calculation method, the de-identified target data and the calculation requirements indicated by the request information to obtain the calculation result.

[0092] Optionally, when a computation request arrives at the target processing system, the information in the request is first analyzed to identify the participants, and a data acquisition request is generated based on the data description information to request the participants to provide relevant data. The data acquisition request may contain explicit instructions to guide the participants on how to prepare and provide anonymized data.

[0093] After receiving a data acquisition request, the participating parties determine the data that needs to be provided to the target processing system, and after performing data anonymization, they feed back the anonymized data to the target processing system. The collection of anonymized data fed back by all participating parties is defined as the anonymized target data.

[0094] In an optional embodiment, the target processing system can generate a data acquisition request based on data description information and the target calculation method. For example, if the target calculation method is a second calculation method, the system guides the participants to directly feed back the anonymized data to the target processing system. If the target calculation method is a first calculation method, the system guides the participants to encrypt at least a portion of the anonymized data using the encryption method required by the first calculation method (e.g., as described above). , Feedback is sent to the target processing system.

[0095] After receiving the anonymized target data from the participants, the system performs privacy-preserving computations based on the target computation method, the anonymized target data, and the computational requirements indicated in the request information, to obtain the computation result. For example, if the target computation method is MPC, the system will use the PSI method to find the intersection of the privacy-preserving data according to the computational requirements. If the target computation method is TEE, the system will process the data in a secure, isolated environment according to the computational requirements to prevent data from being spied on during processing.

[0096] It should be noted that by obtaining anonymized target data from participating parties and processing the anonymized target data, the reliability of privacy computing is improved.

[0097] Optionally, in the data processing method provided in this application embodiment, the participating parties determine the de-identified target data based on the data acquisition request, including: determining the target data based on the data acquisition request; determining sensitive data from the target data; determining the target de-identification strategy based on the data type of the sensitive data, the business scenario indicated in the data acquisition request, and the data access object; and performing de-identification processing on the target data based on the target de-identification strategy to obtain the de-identified target data.

[0098] Optionally, after receiving a data acquisition request, the participating party may determine the target data based on the data description information in the data acquisition request.

[0099] After identifying the target data, sensitive data is determined from it. For example, sensitive data is matched from the target data based on a pre-defined list of sensitive indicators. Participants will match target anonymization strategies from a pre-defined strategy library based on the data type of the sensitive data (e.g., personal information, financial data), the business scenario of the computational task (e.g., transaction risk assessment, customer analysis), and the data access recipients (e.g., internal employees, partner institutions, regulatory agencies). The strategy library includes multiple anonymization strategies and stores the relationships between each anonymization strategy and the data type, business scenario, and data access recipient. Therefore, the target processing system can determine the target anonymization strategy corresponding to the target data based on these relationships.

[0100] After determining the target data anonymization strategy, the target data is anonymized based on this strategy to obtain the anonymized target data. For example, for personal identification information, an irreversible hash function might be used; for monetary data, a threshold might be set for obfuscation. The processed data will form the anonymized target data for subsequent calculations.

[0101] It should be noted that the above methods ensure that participants can flexibly adjust their data protection strategies for different data acquisition requests, thus avoiding the risk of data leakage while maintaining the analytical value of the data.

[0102] In an optional embodiment, after obtaining the calculation result, the target processing system can generate an operation log hash, which is then verified by the consensus node. After successful authentication, the hash is packaged into a block and stored on the chain for subsequent traceability, such as tracing the operator's digital identity, operation timestamp, data object identifier, and calculation task summary.

[0103] In an optional embodiment, the target processing system may include a data access layer, a secure computing layer, a blockchain layer, and an application layer. The data access layer receives anonymized data from financial institutions; the secure computing layer selects either MPC or TEE computing mode based on the business scenario to perform privacy-preserving computing tasks; the blockchain layer records key operations and computation results on the blockchain for evidence storage; and the application layer provides secure data services to the business system through a smart contract interface.

[0104] Therefore, the method provided in this application achieves the goal of dynamically determining the privacy computation method, realizes the technical effect of improving the flexibility of privacy computation, and solves the technical problem that related technologies use a fixed method to perform privacy computation on data, resulting in low flexibility of privacy computation.

[0105] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0106] Example 2

[0107] This application also provides a data processing apparatus. It should be noted that the data processing apparatus of this application can be used to execute the data processing method provided in this application. The data processing apparatus provided in this application will be described below.

[0108] According to an embodiment of this application, an apparatus for implementing the above-described data processing method is also provided, such as... Figure 3 As shown, the device includes:

[0109] The extraction module 301 is used to extract request information from the calculation request when a calculation request is received, wherein the calculation request is used to request privacy calculation on financial data;

[0110] The first determining module 302 is used to determine the requirement information of the calculation request based on the request information, wherein the requirement information includes at least one of the following: performance requirement index value and security requirement index value;

[0111] The second determining module 303 is used to determine the target calculation method from the preset calculation methods based on the demand information;

[0112] The calculation module 304 is used to perform privacy calculations based on the target calculation method and request information to obtain the calculation results for the calculation request.

[0113] In this embodiment of the application, by determining the demand information of the computing request based on the request information of the computing request, the degree of demand of the computing request in at least one aspect of performance and security is effectively determined. By determining the target computing method from the preset computing methods based on the demand information, the target computing method is dynamically determined according to the actual demand of the computing request. Thus, when performing privacy computing based on the target computing method and the request information, the flexibility of privacy computing can be effectively improved.

[0114] Therefore, the method provided in this application achieves the goal of dynamically determining the privacy computation method, realizes the technical effect of improving the flexibility of privacy computation, and solves the technical problem that related technologies use a fixed method to perform privacy computation on data, resulting in low flexibility of privacy computation.

[0115] Optionally, in the data processing apparatus provided in this application embodiment, the first determining module further includes: a first determining submodule, configured to determine the participants associated with the target data from the request information, and determine the number of sensitive data in the target data according to the request information, wherein the target data is data used to calculate the calculation result; a second determining submodule, configured to determine a first performance requirement value based on the number of participants, and determine a second performance requirement value based on the number of sensitive data; and a third determining submodule, configured to determine a performance requirement index value based on the first performance requirement value and the second performance requirement value.

[0116] Optionally, in the data processing apparatus provided in this application embodiment, the first determining module further includes: a fourth determining submodule, used to determine the quantity of sensitive data in the target data according to the request information, wherein the target data is the data used to calculate the calculation result; and a fifth determining submodule, used to determine the security requirement index value based on the quantity of sensitive data.

[0117] Optionally, in the data processing apparatus provided in this application embodiment, the second determining module further includes: a sixth determining submodule, used to determine the target index value based on the demand information; a seventh determining submodule, used to determine the first calculation method as the target calculation method when the target index value is greater than a preset threshold; and an eighth determining submodule, used to determine the second calculation method as the target calculation method when the target index value is less than or equal to the preset threshold.

[0118] Optionally, in the data processing apparatus provided in this application embodiment, the sixth determining submodule further includes: an acquisition unit, used to acquire performance weight coefficient and security weight coefficient; a first calculation unit, used to calculate a first value based on the performance weight coefficient and performance demand index value; a second calculation unit, used to calculate a second value based on the security weight coefficient and security demand index value; and a first determining unit, used to determine a target index value based on the first value and the second value.

[0119] Optionally, in the data processing apparatus provided in this application embodiment, the calculation module further includes: a sending submodule, used to determine the participant associated with the target data from the request information and send a data acquisition request to the participant, wherein the target data is data used to calculate the calculation result, and the participant determines the de-identified target data based on the data acquisition request; and a calculation submodule, used to receive the de-identified target data fed back by the participant, perform privacy calculation based on the target calculation method, the de-identified target data, and the calculation requirements indicated by the request information, and obtain the calculation result.

[0120] Optionally, in the data processing apparatus provided in this application embodiment, the sending submodule further includes: a second determining unit, configured to determine target data based on a data acquisition request; a third determining unit, configured to determine sensitive data from the target data; a fourth determining unit, configured to determine a target de-identification strategy based on the data type of the sensitive data, the business scenario indicated in the data acquisition request, and the data access object; and a processing unit, configured to perform de-identification processing on the target data based on the target de-identification strategy to obtain the de-identified target data.

[0121] It should be noted that the extraction module 301, the first determining module 302, the second determining module 303, and the calculation module 304 mentioned above correspond to steps S201 to S204 in Embodiment 1. The four modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0122] Example 3

[0123] Embodiments of this application may provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4(Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0124] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0125] The processor can invoke information and application programs stored in memory via a transmission device to perform the following steps: upon receiving a computation request, extracting request information from the computation request, wherein the computation request requests privacy-preserving computations on financial data; determining the computation request's requirement information based on the request information, wherein the requirement information includes at least one of the following: a performance requirement index value and a security requirement index value; determining a target computation method from preset computation methods based on the requirement information; and performing privacy-preserving computations based on the target computation method and the request information to obtain the computation result for the computation request.

[0126] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: determine the participants associated with the target data from the request information, and determine the number of sensitive data in the target data according to the request information, wherein the target data is the data used to calculate the calculation result; determine a first performance requirement value based on the number of participants, and determine a second performance requirement value based on the number of sensitive data; determine a performance requirement index value based on the first performance requirement value and the second performance requirement value.

[0127] The processor can also invoke information and applications stored in memory via a transmission device to perform the following steps: determine the amount of sensitive data in the target data based on the request information, wherein the target data is the data used to calculate the result; and determine the security requirement index value based on the amount of sensitive data.

[0128] The processor can also call the information and application program stored in the memory through the transmission device to perform the following steps: determine the target index value based on the demand information; if the target index value is greater than a preset threshold, determine the first calculation method as the target calculation method; if the target index value is less than or equal to the preset threshold, determine the second calculation method as the target calculation method.

[0129] The processor can also access information and applications stored in the memory via a transmission device to perform the following steps: obtaining performance weight coefficients and security weight coefficients; calculating a first value based on the performance weight coefficients and performance requirement index value; calculating a second value based on the security weight coefficients and security requirement index value; and determining a target index value based on the first and second values.

[0130] The processor can also invoke information and applications stored in the memory via the transmission device to perform the following steps: determine the participants associated with the target data from the request information, and send a data acquisition request to the participants, wherein the target data is the data used to calculate the calculation result, and the participants determine the de-identified target data based on the data acquisition request; receive the de-identified target data fed back by the participants, perform privacy calculations based on the target calculation method, the de-identified target data, and the calculation requirements indicated by the request information, and obtain the calculation result.

[0131] The processor can also invoke information and applications stored in the memory via the transmission device to perform the following steps: determine target data based on the data acquisition request; determine sensitive data from the target data; determine the target desensitization strategy based on the data type of the sensitive data, the business scenario indicated in the data acquisition request, and the data access object; and perform desensitization processing on the target data based on the target desensitization strategy to obtain the desensitized target data.

[0132] In this embodiment of the application, by determining the demand information of the computing request based on the request information of the computing request, the degree of demand of the computing request in at least one aspect of performance and security is effectively determined. By determining the target computing method from the preset computing methods based on the demand information, the target computing method is dynamically determined according to the actual demand of the computing request. Thus, when performing privacy computing based on the target computing method and the request information, the flexibility of privacy computing can be effectively improved.

[0133] Therefore, the method provided in this application achieves the goal of dynamically determining the privacy computation method, realizes the technical effect of improving the flexibility of privacy computation, and solves the technical problem that related technologies use a fixed method to perform privacy computation on data, resulting in low flexibility of privacy computation.

[0134] Those skilled in the art will understand that Figure 4The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0135] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0136] Example 4

[0137] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the data processing method provided in Embodiment 1.

[0138] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0139] This application also provides a computer program product, which, when executed on a data processing device, is a program adapted to perform data processing method steps.

[0140] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0141] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0143] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0145] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0146] 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 modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A data processing method, characterized in that, include: Upon receiving a computation request, request information is extracted from the computation request, wherein the computation request is used to request privacy computation on financial data; The requirement information for the computation request is determined based on the request information, wherein the requirement information includes at least one of the following: performance requirement index value, security requirement index value; Based on the aforementioned requirement information, the target calculation method is determined from the preset calculation methods; Privacy calculations are performed based on the target calculation method and the request information to obtain the calculation result for the calculation request.

2. The method according to claim 1, characterized in that, When the demand information includes the performance demand index value, determining the demand information for the computation request based on the request information includes: The participants associated with the target data are determined from the request information, and the number of sensitive data in the target data is determined based on the request information, wherein the target data is the data used to calculate the calculation result; A first performance requirement value is determined based on the number of participants, and a second performance requirement value is determined based on the amount of sensitive data. The performance requirement index value is determined based on the first performance requirement value and the second performance requirement value.

3. The method according to claim 1, characterized in that, When the demand information includes the security demand index value, determining the demand information for the calculation request based on the request information includes: The number of sensitive data in the target data is determined based on the request information, wherein the target data is the data used to calculate the calculation result; The security requirement index value is determined based on the amount of the sensitive data.

4. The method according to claim 1, characterized in that, The preset calculation methods include a first calculation method and a second calculation method. The first calculation method is used for privacy-preserving computation using secure multi-party computation technology, and the second calculation method is used for privacy-preserving computation in a trusted execution environment. The process of determining a target calculation method from the preset methods based on the required information includes: The target index value is determined based on the aforementioned demand information; If the target index value is greater than a preset threshold, the first calculation method is determined as the target calculation method; If the target index value is less than or equal to a preset threshold, the second calculation method is determined as the target calculation method.

5. The method according to claim 4, characterized in that, When the requirement information includes the performance requirement index value and the security requirement index value, determining the target index value based on the requirement information includes: Obtain the performance weighting coefficient and the security weighting coefficient; Based on the performance weighting coefficient and the performance requirement index value, the first value is calculated; The second value is calculated based on the security weight coefficient and the security requirement index value; The target index value is determined based on the first value and the second value.

6. The method according to claim 1, characterized in that, Privacy-preserving computations are performed based on the target computation method and the request information to obtain computation results for the computation request, including: The participants associated with the target data are identified from the request information, and a data acquisition request is sent to the participants. The target data is the data used to calculate the calculation result, and the participants determine the de-identified target data based on the data acquisition request. The system receives the anonymized target data from the participating parties, performs the privacy calculation based on the target calculation method, the anonymized target data, and the calculation requirements indicated by the request information, and obtains the calculation result.

7. The method according to claim 6, characterized in that, The participating parties determine the de-identified target data based on the data acquisition request, including: The target data is determined based on the data acquisition request; Identify sensitive data from the target data; Based on the data type of the sensitive data, the business scenario indicated in the data acquisition request, and the data access object, determine the target desensitization strategy; The target data is desensitized based on the target desensitization strategy to obtain the desensitized target data.

8. A data processing apparatus, characterized in that, include: An extraction module is used to extract request information from a calculation request upon receiving such a request, wherein the calculation request is used to request privacy calculations on financial data. The first determining module is used to determine the requirement information of the computing request based on the request information, wherein the requirement information includes at least one of the following: performance requirement index value and security requirement index value; The second determining module is used to determine the target calculation method from the preset calculation methods based on the requirement information; The calculation module is used to perform privacy calculations based on the target calculation method and the request information to obtain the calculation result for the calculation request.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the data processing method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the data processing method according to any one of claims 1 to 7.

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