Data sharing method and device, equipment, medium and program product

Abstract

The embodiment of the invention discloses a data sharing method and device, equipment, a medium and a program product. The method comprises the steps that a first server side corresponding to a first main body receives a target data request sent by a second server side corresponding to a second main body, and the target data request carries a target user identifier corresponding to a target user; in response to the target data request, obtaining first local user data corresponding to the target user based on the target user identifier; performing privacy calculation on the first local user data to obtain a first local user feature, the first local user feature being used for distinguishing target user group information; and sending the first local user feature to a second server, so that the second server determines a target data processing result corresponding to the target user based on the first local user feature.

Description

Technical Field

[0001] The present specification relates to the field of computer technology, and in particular to a data sharing method, device, equipment, medium and program product. Background Art

[0002] In the era of artificial intelligence (AI), data plays an important role as the energy that drives AI engines. In areas related to individual consumers, such as product recommendations, how to obtain as much user information as possible while protecting personal privacy and security, and recommend products based on understanding user needs through data analysis and model training will become an important issue under AI privacy compliance. Summary of the invention

[0003] The embodiments of this specification provide a data sharing method, device, equipment, medium and program product, which can realize the online real-time sharing of a single user's privacy-protected data, avoid the problem of user privacy leakage during data sharing, and ensure user privacy security. The above technical solution is as follows:

[0004] In a first aspect, an embodiment of the present specification provides a data sharing method, which is applied to a first server corresponding to a first subject, and includes: receiving a target data request sent by a second server corresponding to a second subject; the target data request carries a target user identifier corresponding to a target user; in response to the target data request, obtaining first local user data corresponding to the target user based on the target user identifier; performing privacy calculation on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information; and sending the first local user feature to the second server, so that the second server determines a target data processing result corresponding to the target user based on the first local user feature.

[0005] In a possible implementation, performing privacy calculation on the first local user data to obtain first local user features includes: encoding the first local user data to obtain target encoded data; and performing feature processing on the target encoded data to obtain first local user features.

[0006] In a possible implementation, the performing feature processing on the target coded data to obtain the first local user feature includes: performing feature processing on the target coded data based on a target algorithm model to obtain the first local user feature;

[0007] The above-mentioned sending the above-mentioned first local user feature to the above-mentioned second server so that the above-mentioned second server determines the target data processing result corresponding to the above-mentioned target user based on the above-mentioned first local user feature, including: sending the above-mentioned first local user feature to the above-mentioned second server so that the above-mentioned second server inputs the above-mentioned first local user feature into the above-mentioned target algorithm model, and outputs the target data processing result corresponding to the above-mentioned target user; the above-mentioned target algorithm model is obtained by reinforcement learning based on historical local user features of known actual data processing results.

[0008] In a possible implementation, after receiving the target data request sent by the second server corresponding to the second subject, and before responding to the target data request and obtaining the first local user data corresponding to the target user based on the target user identifier, the method further includes:

[0009] Determine whether to reject the target data request according to the preset data sharing rules; if the target data request is allowed, execute the step of responding to the target data request and obtaining the first local user data corresponding to the target user based on the target user identifier.

[0010] In a possible implementation, the above-mentioned determination of whether to reject the target data request according to preset data sharing rules includes: determining whether the target user has rejected data sharing in the first subject; and / or, determining whether the target data request violates the target compliance policy based on the target user information corresponding to the target user; and / or, determining whether the user corresponding to the target user identifier in the first subject is a non-target user or is empty.

[0011] In a possible implementation, after determining whether to reject the target data request according to the preset data sharing rule, the method further includes:

[0012] If the target data request is rejected, a target rejection result is sent to the second server; the target rejection result includes a target rejection factor corresponding to the target data request.

[0013] In a possible implementation manner, the target data request further carries the target data request type corresponding to the target user;

[0014] The above-mentioned response to the above-mentioned target data request and obtaining the first local user data corresponding to the above-mentioned target user based on the above-mentioned target user identifier includes: responding to the above-mentioned target data request and obtaining the first local user data corresponding to the above-mentioned target user based on the above-mentioned target user identifier and the above-mentioned target data request type.

[0015] In the second aspect, an embodiment of the present specification provides a data sharing method, which is applied to a second server corresponding to a second subject, and the method includes: sending a target data request corresponding to a target user to a first server corresponding to a first subject, so that the first server obtains the first local user data corresponding to the target user based on the target user identifier carried in the target data request, and performs privacy calculation on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information; receiving the first local user feature sent by the first server; and determining the target data processing result corresponding to the target user based on the first local user feature.

[0016] In a possible implementation manner, the first local user feature is obtained by the first server performing feature processing on target encoded data after encoding the first local user data based on a target algorithm model;

[0017] The above-mentioned determination of the target data processing result corresponding to the target user based on the above-mentioned first local user feature includes: inputting the above-mentioned first local user feature into the above-mentioned target algorithm model, and outputting the target data processing result corresponding to the above-mentioned target user; the above-mentioned target algorithm model is obtained by reinforcement learning based on historical local user features of known actual data processing results.

[0018] In a possible implementation, the historical local user feature includes the first historical local user feature corresponding to the first server and / or the second historical local user feature corresponding to the second server;

[0019] The above target algorithm model is updated as the above historical local user features are updated.

[0020] In a possible implementation, before determining the target data processing result corresponding to the target user based on the first local user feature, the method further includes: acquiring second local user data of the target user in the second subject; determining a second local user feature corresponding to the target user based on the second local user data;

[0021] The determining the target data processing result corresponding to the target user based on the first local user feature includes: determining the target data processing result corresponding to the target user based on the first local user feature and the second local user feature.

[0022] In a possible implementation, after sending the target data request corresponding to the target user to the first server corresponding to the first subject, the method further includes: upon receiving the target rejection result corresponding to the target data request sent by the first server, determining the target data processing result corresponding to the target user based on the second local user feature.

[0023] In a third aspect, an embodiment of the present specification provides a data sharing device, which is applied to a first server corresponding to a first subject, and includes:

[0024] A first receiving module is used to receive a target data request sent by a second server corresponding to a second subject; the target data request carries a target user identifier corresponding to a target user;

[0025] A first acquisition module, configured to respond to the target data request and acquire first local user data corresponding to the target user based on the target user identifier;

[0026] A privacy calculation module, used to perform privacy calculation on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information;

[0027] The first sending module is used to send the first local user feature to the second server, so that the second server determines the target data processing result corresponding to the target user based on the first local user feature.

[0028] In a fourth aspect, an embodiment of the present specification provides a data sharing device, which is applied to a second server corresponding to a second subject, and includes:

[0029] A second sending module is used to send a target data request corresponding to the target user to the first server corresponding to the first subject, so that the first server obtains the first local user data corresponding to the target user based on the target user identifier carried in the target data request, and performs privacy calculation on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information;

[0030] A second receiving module, configured to receive the first local user feature sent by the first server;

[0031] A result determination module is used to determine a target data processing result corresponding to the target user based on the first local user feature.

[0032] In a fifth aspect, an embodiment of this specification provides an electronic device, including: a processor and a memory;

[0033] The processor is connected to the memory;

[0034] The above-mentioned memory is used to store executable program code;

[0035] The above-mentioned processor runs the program corresponding to the above-mentioned executable program code by reading the executable program code stored in the above-mentioned memory, so as to execute the data sharing method provided by the first aspect or any possible implementation of the first aspect or the second aspect or any possible implementation of the second aspect of the embodiment of this specification.

[0036] In a sixth aspect, an embodiment of the present specification provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the data sharing method provided by the first aspect of the embodiment of the present specification or any possible implementation of the first aspect or the second aspect or any possible implementation of the second aspect.

[0037] In the seventh aspect, an embodiment of the present specification provides a computer program product comprising instructions, which, when the above-mentioned computer program product runs on a computer or a processor, enables the above-mentioned computer or the above-mentioned processor to execute the data sharing method provided by the first aspect of the embodiment of the present specification or any possible implementation of the first aspect or the second aspect or any possible implementation of the second aspect.

[0038] In an embodiment of the present specification, the first server corresponding to the first subject can perform privacy calculation on the local user data (first local user data) corresponding to the target user, convert it into a first local user feature that can only be used to distinguish the target user group information, and then share the first local user feature with the second server corresponding to the other subject (the second subject) that sends a target data request carrying the target user identifier corresponding to the target user, thereby ensuring that the second server corresponding to the other subject (the second subject) can obtain the first local user feature of the target user to perform corresponding data processing to obtain the corresponding target data processing result, and by only sharing the first local user feature that can only be used to distinguish the target user group information, rather than the specific data of a single user or the full user data corresponding to the first subject, the shared data itself does not reveal personal related information, thereby achieving online real-time sharing of privacy-protected data of a single user, avoiding the problem of user privacy leakage during data sharing, and ensuring user privacy security. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of a data sharing implementation process provided in the related art;

[0041] Figure 2 A schematic diagram of another data sharing implementation process provided in the related art;

[0042] Figure 3 A schematic diagram of a data sharing implementation process provided by an exemplary embodiment of this specification;

[0043] Figure 4 A schematic diagram of the architecture of a data sharing system provided as an exemplary embodiment of this specification;

[0044] Figure 5 A flowchart of a data sharing method provided as an exemplary embodiment of this specification;

[0045] Figure 6 A flowchart of another data sharing method provided as an exemplary embodiment of this specification;

[0046] Figure 7 A flowchart of another data sharing method provided as an exemplary embodiment of this specification;

[0047] Figure 8 A schematic diagram of the structure of a data sharing device provided as an exemplary embodiment of this specification;

[0048] Fig. 9 A schematic diagram of the structure of another data sharing device provided as an exemplary embodiment of this specification;

[0049] Fig.10 The present invention is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of this specification will be described clearly and completely below in conjunction with the drawings in the embodiments of this specification.

[0051] The terms "first", "second", "third", etc. in this specification and claims and the above drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0052] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the first local user data, the second local user data, etc. involved in this specification are all obtained with full authorization.

[0053] As individuals, enterprises and other institutions continue to generate and collect large amounts of data in their daily operations, how to share this data safely and effectively has become an important technical and social issue. In the process of data sharing, the risk of leakage of personal privacy data (such as identity information, financial information, health data, etc.) has increased significantly. Therefore, it is necessary to develop effective privacy protection technologies to ensure the security and privacy of data during the sharing process.

[0054] There are two main types of data sharing technologies. Figure 1 As shown, when the user is experiencing the application scenario corresponding to the first subject (for example, but not limited to using the first application corresponding to the first subject), if the user wants to share the user data that the user has authorized to the second subject (for example, but not limited to the basic information, browsing habits, preference information, etc. filled in by the user when using the second application corresponding to the second subject) for data processing (such as product recommendations, etc.), it is necessary to request authorization from the user. That is, based on the user's authorization, including pop-up authorization (a small amount of information) and protocol authorization (a large amount of information), user data is shared between multiple subjects under the user's perception. This solution will block the user experience. For example, when a user is browsing a short video, the user is suddenly asked to authorize his browsing history to an e-commerce company, and then a product recommendation is inserted. This will most likely cause the user to choose to refuse sharing and complain about the browsing experience.

[0055] In order to solve the above problems, Figure 2 Another solution is shown in FIG. Figure 2As shown in the figure, by establishing a neutral information processing center (neutral subject), the subjects of information sharing (such as the first subject, the second subject, etc.) provide user data respectively, that is, the authorized user data is output to the neutral subject. Then, privacy calculation is performed in the information processing center (neutral subject) through a variety of privacy protection algorithms to calculate the data processing results required for the application scenario, such as the above-mentioned product recommendations of interest to the user. This solution usually requires offline calculation in advance, lacks flexibility, and requires each subject to transmit a large amount of user data to a neutral subject, which has a high risk of data leakage.

[0056] Based on this, this specification embodiment provides a data sharing method, specifically as follows Figure 3 As shown, before the second subject shares the user data obtained by authorization, it is necessary to first perform privacy calculation on the user data of the second subject (the second local user data), for example but not limited to first encoding the information in the user data (the second local user data), and then processing the encoded user data to convert it into user features (the second local user features) that can only be used to distinguish user group information, and then privately share it with the first subject for data processing in the corresponding application scenario of the user, thereby avoiding blocking the user experience in the application process, and there is no need for multiple subjects to provide a large amount of user data to the neutral subject to calculate the data processing results in advance. It also ensures that the subject (such as the first subject) who needs to perform data processing in the application scenario can obtain the user features (the second local user features) shared by other subjects (such as the second subject) for corresponding data processing to obtain the corresponding data processing results. By only sharing user features that can only be used to distinguish user group information, rather than specific data or full user data of a single user, the shared data itself does not reveal personal related information, so that the privacy-protected data of a single user can be shared online in real time, avoiding the problem of user privacy leakage during data sharing, and ensuring user privacy security.

[0057] Please refer to the following Figure 4 , which is a schematic diagram of the architecture of a data sharing system provided by an exemplary embodiment of this specification. Figure 1 As shown, the data sharing system includes: a first server 110 corresponding to the first subject, a second server 120 corresponding to the second subject, and a terminal 130. Among them:

[0058] The first server 110 may be a server that can provide data sharing services, and may receive a target data request sent by the second server 120 corresponding to the second subject through the network, and the target data request carries a target user identifier corresponding to the target user. Then, in response to the target data request, the first local user data corresponding to the target user is obtained based on the target user identifier, and a privacy calculation is performed on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information. Finally, the first local user feature is sent to the second server 120, so that the second server determines the target data processing result corresponding to the target user based on the first local user feature. The first server 110 may be, but is not limited to, a hardware server, a virtual server, a cloud server, etc.

[0059] The second server 120 may be a server that can provide data processing services (such as but not limited to product recommendations, risk prediction, credit assessment, etc.), and may send a target data request corresponding to the target user to the first server 110 corresponding to the first subject through the network, so that the first server 110 obtains the first local user data corresponding to the target user based on the target user identifier carried in the target data request, and performs privacy calculation on the first local user data to obtain the first local user feature, which is used to distinguish the target user group information. At the same time, the first local user feature sent by the first server 110 may also be received through the network, and the target data processing result corresponding to the target user may be determined based on the first local user feature. The second server 120 may be but not limited to a hardware server, a virtual server, a cloud server, etc.

[0060] The terminal 130 may be a user terminal corresponding to the first subject and / or the second subject, and the terminal 130 may be installed with, but not limited to, a program or application corresponding to the first subject and / or the second subject, so that the user can use the program or application to experience the services provided by the first subject and / or the second subject. The terminal 130 may interact with the first server 110 corresponding to the first subject and the second server 120 corresponding to the second subject through the network, for example, sending the first user data and the first user operation to the first server 110 corresponding to the first subject, and sending the second user data and the second user operation to the second server 120 corresponding to the second subject. The terminal 130 may be hardware or software. When the terminal 130 is hardware, it may be various electronic devices, including but not limited to smart watches, smart phones, tablet computers, laptop portable computers, and desktop computers. When the terminal 130 is software, it may be installed in the electronic devices listed above, and it may be implemented as multiple software or software modules (for example: used to provide distributed services), or it may be implemented as a single software or software module, which is not specifically limited here.

[0061] It can be understood that the above-mentioned first server 110 can be a server that can provide data processing services (such as but not limited to product recommendations, risk prediction, credit assessment, etc.), and the above-mentioned second server 120 can also be a server that can provide data sharing services, that is, the above-mentioned first server 110 can execute the data sharing steps executed by the above-mentioned second server 120, and the above-mentioned second server 120 can also execute the data request and data processing steps executed by the above-mentioned first server 110, and the embodiments of this specification do not limit this.

[0062] The network may be a medium that provides a communication link between any two of the first server 110 corresponding to the first subject, the second server 120 corresponding to the second subject, and the terminal 130, or may be the Internet including network equipment and transmission media, but is not limited thereto. The transmission medium may be a wired link, such as but not limited to coaxial cable, optical fiber, and digital subscriber line (DSL), or a wireless link, such as but not limited to wireless fidelity (WIFI), Hypertext Transfer Protocol (HTTP), Bluetooth, and mobile device network, etc.

[0063] Understandably, Figure 1 The numbers of the first server 110, the second server 120, and the terminal 130 in the data sharing system shown are only examples. In a specific implementation, the data sharing system may include any number of first servers 110, second servers 120, and terminals 130, and this specification embodiment does not specifically limit this. For example, but not limited to, the first server 110 may be a first server cluster composed of multiple first servers, the second server 120 may be a second server cluster composed of multiple second servers, and the terminal 130 may be a terminal cluster composed of multiple terminals.

[0064] In order to solve the problems existing in the above-mentioned related technologies, Figure 3 and Figure 4 , introduces the data sharing method provided by the embodiments of this specification. For details, please refer to Figure 5 , which is a flow chart of a data sharing method provided by an exemplary embodiment of this specification. Figure 5 As shown, the data sharing method includes the following steps:

[0065] S502: The second server corresponding to the second subject sends a target data request corresponding to the target user to the first server corresponding to the first subject, where the target data request carries a target user identifier corresponding to the target user.

[0066] Specifically, the first subject and the second subject may be different legal subjects, which refer to natural persons, legal persons or unincorporated entities that are active in the law, enjoy rights, bear obligations and responsibilities, and are regulated or legally bound, such as but not limited to different companies or enterprises corresponding to different applications. When the target user enters an application scenario of the second subject, such as but not limited to browsing short videos, reading novels, etc., the second server corresponding to the second subject can perform corresponding data processing for the target user, such as but not limited to recommending products that the target user may be interested in, and converting them into transactions to achieve the ultimate goal.

[0067] In order to more accurately understand the needs of the target user and improve the transaction conversion rate, the second server corresponding to the second subject needs to obtain more target user features for more accurate data processing. Therefore, the second server corresponding to the second subject can send a target data request corresponding to the target user to the first server corresponding to at least one first subject through the network to request the corresponding data generated by the target user in other subjects (first subjects).

[0068] It can be understood that the above-mentioned target data request can be sent to one or more other entities (first entity) that have reached cooperation with the second entity, and this embodiment of the specification does not limit this. The above-mentioned first entity can be a entity that has reached cooperation with the second entity, or it can be a entity that the target user has specified in the first entity to authorize the need to initiate a target data request, and this embodiment of the specification does not limit this. The target user identifier carried by the above-mentioned target data request is the user identifier (such as but not limited to mobile phone number, account number, etc.) entered when the target user authorizes use or registers in the first entity. The user identifiers corresponding to the same user in the first entity and the second entity may be the same or different. For example, the target user identifier corresponding to the target user in the first entity may be the target user's mobile phone number A, and the target user identifier corresponding to the second entity may be the target user's identity code or mobile phone number B, etc., and this embodiment of the specification does not limit this.

[0069] S504: The first server obtains first local user data corresponding to the target user based on the target user identifier in response to the target data request.

[0070] Specifically, after receiving the target data request sent by the second server corresponding to the second subject, the first server can directly respond to the target data request and query its local database (the first database corresponding to the first subject) to obtain the first local user data corresponding to the target user according to the target user identifier carried in the target data request. The above-mentioned first local user data may include, but is not limited to, user information input by the target user in the first subject (such as but not limited to basic information during registration), operation information (such as but not limited to web page or product browsing information, etc.), order information, preference information (such as but not limited to keywords searched when browsing videos), etc.

[0071] Optionally, the target data request also carries a target data request type corresponding to the target user, and the target data request type is used to characterize the data type (data content) of the target user that the second server requests the first server to share. After the first server receives the target data request sent by the second server corresponding to the second subject, it can respond to the target data request and obtain the first local user data corresponding to the target user based on the target user identifier and the target data request type, such as but not limited to querying the first local user data of the target data request type corresponding to the target user identifier in its local database (the first database corresponding to the first subject). For example, but not limited to, when the target data request type is a basic type, the first local user data can be the basic information of the target user in the first subject; when the target data request type is an operation type, the first local user data can be the operation information of the target user in the first subject; when the target data request type is a preference type, the first local user data can be the preference information of the target user in the first subject.

[0072] In the embodiments of the present specification, the first server can more accurately obtain the data required for data processing by the second server through the target user identifier and the target data request type carried in the target data request, thereby improving the degree of refinement of data sharing and sharing only one or certain types of data required for data processing at the user granularity. This not only avoids the problem of invalid sharing of other users' data and data not required for other data processing at the user granularity, improves the effectiveness of data sharing, but also ensures the security of the user data corresponding to the first subject to a certain extent, avoiding the problem of leakage of large amounts of user data.

[0073] S506: The first server performs privacy calculation on the first local user data to obtain a first local user feature, where the first local user feature is used to distinguish target user group information.

[0074] Specifically, after the first server obtains the first local user data of the target user, in order to avoid the problem of the target user's privacy being leaked due to the data being stolen by others in the process of sharing the data with the second server, the first local user data can be firstly privacy-calculated to remove the sensitive information in the first local user data, and obtain the first local user feature that can only be used to distinguish the target user group information, thereby protecting the privacy and security of the shared user data, ensuring that sensitive information will not be leaked during the data processing process, and allowing the user data to be calculated and analyzed between different subjects to realize the value and insight of the user data. At the same time, the first local user feature does not reveal the target user's personal information (such as the specific age and address of the target user), and can only be used to distinguish which user group the target user belongs to, such as but not limited to the target user belonging to the middle-aged person, located in a first-tier city, etc. Therefore, even if the first local user feature is leaked in the process of sharing the data with the second server, the specific target user cannot be located through the first local user feature, and the specific privacy data of the target user cannot be known, thereby greatly protecting the privacy security of the user in the data sharing process.

[0075] Optionally, the implementation process of the above privacy computing may include, but is not limited to: firstly, encoding the first local user data through lightweight privacy computing to obtain target coded data, thereby achieving privacy protection of the target user to a certain extent. The above encoding rules may include, but are not limited to, first extracting keywords or entities from the first local user data, then encoding the above keywords or entities into corresponding strings according to a preset coding mapping relationship, and finally combining the strings corresponding to each keyword or entity to obtain the target coded data corresponding to the first local user data. The above preset coding mapping relationship is used to characterize the correspondence between different keywords or entities and each string. Different keywords or entities have different corresponding strings. The above encoding may be text encoding or video encoding, and may be encoded according to a preset coding mapping relationship or a coding comparison table, or other encoding methods may be used. The embodiments of this specification do not limit this. For example, but not limited to, when the first local user data is "aged in his 20s, recently bought a certain brand of milk", it can be encoded into "01010001, MABC" (target coding data).

[0076] Since it is still possible to reversely infer the original user privacy information (first local user data) from the encoded target encoded data in the abnormal scenario of partial data leakage. Therefore, before sharing the data with the second server, a second privacy computing defense can be performed, that is, feature processing (such as but not limited to vectorized representation processing) is performed on the target encoded data. Through feature processing, the target encoded data is converted into machine data (first local user features) that can only be used by specific algorithms, and the first local user features can only be used to distinguish different user groups, that is, to distinguish the target user group information, thereby avoiding the problem of the original user privacy information (first local user data) being leaked in the abnormal scenario of partial data leakage, and further improving the security of privacy protection in the data sharing process.

[0077] S508: The first server sends the first local user feature to the second server.

[0078] Specifically, after obtaining the first local user feature that can only be used to distinguish the target user group information, the first server can also send the first local user feature to the second server through the network, thereby realizing data sharing.

[0079] S510: The second server determines a target data processing result corresponding to a target user based on the first local user feature.

[0080] Specifically, after receiving the first local user feature shared by the first server, the second server may, but is not limited to, directly perform corresponding data processing based on the first local user feature to obtain a target data processing result corresponding to the target user. The above data processing may, but is not limited to, include data decision-making, product recommendation (recommending products that the target user may be interested in), risk assessment (assessing whether the target user is a risky user), etc.

[0081] In an embodiment of the present specification, the first server corresponding to the first subject can perform privacy calculation on the local user data (first local user data) corresponding to the target user, convert it into a first local user feature that can only be used to distinguish the target user group information, and then share the first local user feature with the second server corresponding to the other subject (the second subject) that sends a target data request carrying the target user identifier corresponding to the target user, thereby ensuring that the second server corresponding to the other subject (the second subject) can obtain the first local user feature of the target user to perform corresponding data processing to obtain the corresponding target data processing result, and by only sharing the first local user feature that can only be used to distinguish the target user group information, rather than the specific data of a single user or the full user data corresponding to the first subject, the shared data itself does not reveal personal related information, thereby achieving online real-time sharing of privacy-protected data of a single user, avoiding the problem of user privacy leakage during data sharing, and ensuring user privacy security.

[0082] Please refer to the following Figure 6 , which is a flow chart of another data sharing method provided by an exemplary embodiment of this specification. Figure 6 As shown, the data sharing method includes the following steps:

[0083] S602: The second server corresponding to the second subject sends a target data request corresponding to the target user to the first server corresponding to the first subject, where the target data request carries a target user identifier corresponding to the target user.

[0084] Specifically, S602 is consistent with S502 and will not be described in detail here.

[0085] S604: The first server obtains first local user data corresponding to the target user based on the target user identifier in response to the target data request.

[0086] Specifically, S604 is consistent with S504 and will not be described in detail here.

[0087] S606: The first server encodes the first local user data to obtain target encoded data.

[0088] Specifically, after obtaining the first local user data of the target user, the first local user data can be encoded by lightweight privacy calculation, for example, but not limited to first extracting keywords or entities in the first local user data, and then encoding the above keywords or entities into corresponding strings according to a preset coding mapping relationship, and finally combining the strings corresponding to each keyword or entity to obtain the target coded data corresponding to the first local user data, thereby achieving privacy protection of the target user to a certain extent. The above preset coding mapping relationship is used to characterize the correspondence between different keywords or entities and each string.

[0089] It is understandable that different keywords or entities have different corresponding character strings. The above encoding can be text encoding or video encoding, and can be encoded according to a preset encoding mapping relationship or encoding comparison table, or other encoding methods can be used, which is not limited in the embodiments of this specification. For example, but not limited to, when the first local user data is "aged in his 20s, recently bought a certain brand of milk", it can be encoded into "01010001, MABC" (target encoding data).

[0090] S608, the first server performs feature processing on the target coded data based on the target algorithm model to obtain a first local user feature, where the first local user feature is used to distinguish target user group information.

[0091] Specifically, in the abnormal scenario of partial data leakage, it is still possible to reversely infer the original user privacy information (first local user data) from the encoded target encoded data. Therefore, before sharing the data with the second server, a second privacy computing defense can be performed, that is, using the target algorithm model, the target encoded data is subjected to feature processing (for example, but not limited to vectorized representation processing), and through feature processing, the target encoded data is converted into machine data (first local user features) that can only be used by a specific algorithm (i.e., the target algorithm adopted by the target algorithm model), and the first local user features can only be used to distinguish different user groups, i.e., to distinguish the target user group information, thereby avoiding the problem of the original user privacy information (first local user data) being leaked in the abnormal scenario of partial data leakage, and further improving the security of privacy protection in the data sharing process.

[0092] It can be understood that the above-mentioned target algorithm model can be obtained by sharing the second server corresponding to the second subject. For example, but not limited to, the second server corresponding to the second subject sends the target data request corresponding to the target user to the first server corresponding to the first subject, and also sends the target algorithm model to be used for data processing to the first server, so that the first server can use the target algorithm model to process the data to be shared (first local user data) into data (first local user features) that can only be understood by the target algorithm model, which not only ensures the data privacy security in the process of data sharing, but also performs feature processing on the first local user data in advance, thereby improving the efficiency of data processing by the second server to a certain extent. The above-mentioned target algorithm model can also be an algorithm model pre-set in advance on the respective corresponding servers after the first subject and the second subject negotiate or reach a cooperation. The target algorithm model can be, but is not limited to, learning updates and parameter sharing between the respective corresponding servers of multiple subjects that have reached cooperation (for example, but not limited to the first subject and the second subject) according to the federated learning technology, and the embodiments of this specification are not limited to this.

[0093] S610: The first server sends the first local user feature to the second server.

[0094] Specifically, S610 is consistent with S508 and will not be described in detail here.

[0095] S612, the second server inputs the first local user feature into the target algorithm model, and outputs the target data processing result corresponding to the target user.

[0096] Specifically, after receiving the first local user feature shared by the first server, the second server may, but is not limited to, directly input the first local user feature into the target algorithm model and output the target data processing result corresponding to the target user. The above target algorithm model may, but is not limited to, be obtained by enhanced learning based on historical local user features with known actual data processing results.

[0097] Exemplarily, in a product recommendation scenario, the first local user data may be, but is not limited to, the target user's historical purchase information, basic information, and product browsing information corresponding to the first subject, the target algorithm model may be a product recommendation model, the target data processing results may be, but are not limited to, target product recommendation results (i.e., recommending products that may be of interest to the target user on the application page corresponding to the second subject), and the actual data processing results may be the target user's actual purchase situation of the products recommended by the second server, etc.; in a risk assessment scenario, the first local user data may be, but is not limited to, the target user's asset information, basic information, and loan information corresponding to the first subject, the target algorithm model may be a risk assessment model, the target data processing results may be, but are not limited to, target risk assessment results (for example, the probability that the target user is a risky user assessed by the second subject corresponding to the second server or the risk level of the target user), and the actual data processing results may be the actual risk situation corresponding to the second subject of the target user, etc.

[0098] Furthermore, the above-mentioned historical local user features may include, but are not limited to, the first historical local user features corresponding to the first server and / or the second historical local user features corresponding to the second server; that is, the above-mentioned target algorithm model may be trained based on the user features of the user data corresponding to the first subject of the known actual data processing results and / or the user features of the user data corresponding to the second subject. The above-mentioned target algorithm model is updated as the historical local user features are updated, that is, the target algorithm model has the ability of enhanced learning, does not need to rely on a large amount of user data for cold start, and can continuously correct and optimize model parameters and improve model effects as positive and negative samples (historical local user features of known actual data processing results) increase.

[0099] Please refer to the following Figure 7 , which is a flow chart of another data sharing method provided by an exemplary embodiment of this specification. Figure 7 As shown, the data sharing method includes the following steps:

[0100] S702: The second server corresponding to the second subject obtains second local user data of the target user in the second subject.

[0101] Specifically, when the target user enters an application scenario of the second subject, such as but not limited to browsing short videos, reading novels, browsing products online, etc., the second server corresponding to the second subject can perform corresponding data processing for the target user, such as but not limited to recommending products that the target user may be interested in, and achieve the ultimate goal of converting the transaction.

[0102] Since the second subject also has the data of the current target user, such as but not limited to the keywords for video search, the video content browsed in history, the basic information at the time of registration, etc. Therefore, in addition to sending the target data request corresponding to the target user to the first server corresponding to the first subject to request to obtain the corresponding data generated by the target user in other subjects (the first subject) to participate in the data processing corresponding to the target user, the second server can also directly query the second local user data corresponding to the target user in its local database (the second database corresponding to the second subject) through the target user identifier to participate in the data processing process, so as to improve the richness of data in the data processing process and the accuracy of data processing. The above-mentioned second local user data can include but is not limited to the user information input by the target user in the second subject (such as but not limited to the basic information at the time of registration), operation information (such as but not limited to web page or product browsing information, etc.), order information, preference information (such as but not limited to the keywords searched when browsing videos), etc.

[0103] It can be understood that the above-mentioned target user may be a user who has currently entered the application scenario corresponding to the second subject, such as but not limited to a user who is using the software or mini-program corresponding to the second subject to browse short videos, browse products, read novels, etc., and the embodiments of this specification do not limit this.

[0104] S704: The second server determines a second local user feature corresponding to the target user based on the second local user data.

[0105] Specifically, after obtaining the second local user data of the target user, the second server may directly determine the second local user data as the second local user feature corresponding to the target user, or may first encode the second local user data, and then perform feature processing on the encoded second local user data to obtain the second local user feature corresponding to the target user, which is not limited in the embodiments of this specification. The above encoding process is similar to the encoding process in S606, and the above feature processing process is similar to the feature processing process in S608, which will not be described in detail here.

[0106] S706: The second server sends a target data request corresponding to the target user to the first server corresponding to the first subject, where the target data request carries a target user identifier corresponding to the target user.

[0107] Specifically, S706 is consistent with S502 and will not be described in detail here.

[0108] It can be understood that the above S702 and S706 can be executed successively or simultaneously, and this description is an embodiment and does not limit this.

[0109] Please continue to refer to Figure 7 ,like Figure 7 As shown, after the first server receives the target data request corresponding to the target user sent by the second server, the data sharing method may also include but is not limited to:

[0110] S708: The first server determines whether to reject the target data request according to the preset data sharing rule.

[0111] Specifically, in order to ensure the legality and security of data sharing and improve user experience, after receiving the target data request corresponding to the target user sent by the second server, the first server can also first determine whether it is necessary to reject the target data request according to the preset data sharing rules, such as but not limited to determining whether the target user has rejected data sharing in the first entity, and / or, determining whether the target data request violates the target compliance policy based on the target user information corresponding to the target user, and / or determining whether the user corresponding to the target user identifier in the first entity is a non-target user or is empty, etc.

[0112] Optionally, after receiving the target data request corresponding to the target user sent by the second server, the first server may first query the target user's authorization information in the first database (i.e., the local database of the first server), and determine whether the target user has refused data sharing in the first entity based on the target user's authorization information. For example, but not limited to, the target user has clearly requested in the first entity not to participate in any marketing recommendation activities, etc. If the target user has refused data sharing in the first entity, it can be determined that the target data request needs to be rejected, that is, the relevant data of the target user will not be shared with the second server, thereby providing users with personalized authorization services in data scenarios and improving user experience.

[0113] Optionally, after receiving the target data request corresponding to the target user sent by the second server, the first server may first query the target user information corresponding to the target user in the first entity (for example, but not limited to basic information such as the target user's occupation and age), and then determine whether the target data request violates the target compliance policy based on the target user information corresponding to the target user. For example, but not limited to, when the target user is a child, according to the provision in the target compliance policy that children's data cannot be shared externally, it can be determined that the target data request violates the target compliance policy, and it can be determined that the target data request needs to be rejected, that is, the relevant data of the target user will not be shared with the second server, thereby ensuring the legality of data sharing.

[0114] Optionally, the above-mentioned target data request may also carry the user information corresponding to the target user in the second subject (such as but not limited to the mobile phone number used by the target user when registering in the second subject, the terminal device identifier used, etc.). After receiving the target data request corresponding to the target user sent by the second server, the first server may also first query the target user information corresponding to the target user identifier in the first subject (such as but not limited to the mobile phone number used by the target user identifier when registering in the first subject, the terminal device identifier used, etc.); then, determine whether the user corresponding to the target user identifier in the first subject is a non-target user or empty. If the user information corresponding to the target user identifier in the second subject is inconsistent with the target user information corresponding to the target user identifier in the first subject, it can be determined that the user corresponding to the target user identifier in the first subject is a non-target user, that is, not the target user, and it can be determined that the target data request needs to be rejected, that is, the relevant data of the target user will not be shared with the second server, thereby avoiding the target user's information being stolen or lent to others for use in the first subject, resulting in the leakage of user data in the second subject, and the data shared by the first subject is not the target user's data at all, which affects the accuracy of data processing of the second subject. If the corresponding user cannot be found in the first subject through the target user identifier, that is, whether the user corresponding to the target user identifier in the first subject is empty, for example, the target user has not generated any data in the first subject, etc., it means that the first server has no data related to the target user to share, and it can be determined that the target data request needs to be rejected, that is, the relevant data of the target user will not be shared with the second server.

[0115] Please continue to refer to Figure 7 ,like Figure 7 As shown, in the above S708, after the first server determines whether to reject the target data request according to the preset data sharing rule, the data sharing method may also include but is not limited to:

[0116] S710: If the first server allows the target data request, the first server executes a response to the target data request and obtains first local user data corresponding to the target user based on the target user identifier.

[0117] Specifically, the first server determines that the target data request will not be rejected according to the preset data sharing rule, that is, if the first server allows the target data request, it can execute the first local user data corresponding to the target user based on the target user identifier in response to the target data request. The acquisition process of the first local user data is consistent with the above S504, which is not repeated here.

[0118] S712, the first server encodes the first local user data to obtain target encoded data.

[0119] Specifically, S712 is consistent with S606 and will not be repeated here.

[0120] S714, the first server performs feature processing on the target coded data to obtain a first local user feature, where the first local user feature is used to distinguish target user group information.

[0121] Specifically, the implementation process of the feature processing in the above S714 is similar to that in S608, and will not be repeated here.

[0122] S716: The first server sends the first local user feature to the second server.

[0123] Specifically, S716 is consistent with S610 and will not be repeated here.

[0124] S718: The second server determines a target data processing result corresponding to the target user based on the first local user feature and the second local user feature.

[0125] Specifically, after the second server obtains its local second local user characteristics and the first local user characteristics shared by the first subject, it can jointly determine the target data processing results corresponding to the target user based on the first local user characteristics and the second local user characteristics, for example but not limited to directly inputting the above-mentioned first local user characteristics and the second local user characteristics into the target algorithm model, and outputting the corresponding target data processing results, thereby improving the accuracy of data processing on the second server to a certain extent.

[0126] Please continue to refer to Figure 7 ,like Figure 7 As shown, in the above S708, after the first server determines whether to reject the target data request according to the preset data sharing rule, the data sharing method may also include but is not limited to:

[0127] S720: If the first server rejects the target data request, it sends a target rejection result corresponding to the target data request to the second server.

[0128] Specifically, the first server determines according to the preset data sharing rules that rejecting the target data request indicates that the target data request is non-compliant or there is a risk of data leakage. In this case, the first server will not share the target user's data with the second server, and may send a target rejection result corresponding to the target data request to the second server. The above target rejection result may include but is not limited to the target rejection factor corresponding to the target data request, thereby informing the second subject that the target data request has been rejected and the reason for the rejection, thereby avoiding the problem that the second subject has been waiting for the first subject to share data and affecting its data processing efficiency.

[0129] Please continue to refer to Figure 7 ,like Figure 7As shown, the data sharing method may also include but is not limited to:

[0130] S722: When the second server receives the target rejection result corresponding to the target data request sent by the first server, the second server determines the target data processing result corresponding to the target user based on the second local user feature.

[0131] Specifically, after receiving the target rejection result corresponding to the target data request sent by the first server, the second server can directly determine the target data processing result corresponding to the target user based on its local second local user characteristics, for example but not limited to directly inputting the second local user characteristics into the target algorithm model, and outputting the target data processing result corresponding to the target user, thereby ensuring the efficiency and success rate of data processing performed by the second subject corresponding to the second server.

[0132] Please refer to the following Figure 8 , which is a schematic diagram of the structure of a data sharing device provided by an exemplary embodiment of this specification. The above data sharing device is applied to the first server corresponding to the first subject, such as Figure 8 As shown, the data sharing device 800 includes:

[0133] The first receiving module 810 is used to receive a target data request sent by a second server corresponding to a second subject; the target data request carries a target user identifier corresponding to a target user;

[0134] A first acquisition module 820, configured to respond to the target data request and acquire first local user data corresponding to the target user based on the target user identifier;

[0135] The privacy calculation module 830 is used to perform privacy calculation on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information;

[0136] The first sending module 840 is used to send the first local user feature to the second server, so that the second server determines the target data processing result corresponding to the target user based on the first local user feature.

[0137] In a possible implementation, the privacy computing module 830 includes:

[0138] An encoding unit, used for encoding the first local user data to obtain target encoded data;

[0139] The feature processing unit is used to perform feature processing on the target coded data to obtain a first local user feature.

[0140] In a possible implementation, the feature processing unit is specifically used to: perform feature processing on the target coded data based on a target algorithm model to obtain a first local user feature.

[0141] The above-mentioned first sending module 840 is specifically used to: send the above-mentioned first local user feature to the above-mentioned second server, so that the above-mentioned second server inputs the above-mentioned first local user feature into the above-mentioned target algorithm model, and outputs the target data processing result corresponding to the above-mentioned target user; the above-mentioned target algorithm model is obtained by enhanced learning based on the historical local user features of the known actual data processing results.

[0142] In a possible implementation, the data sharing device 800 further includes:

[0143] A judgment module, used to judge whether to reject the above target data request according to preset data sharing rules;

[0144] The execution module is used for executing the step of obtaining the first local user data corresponding to the target user based on the target user identifier in response to the target data request if the target data request is allowed.

[0145] In a possible implementation, the judgment module is specifically used to: judge whether the target user has refused data sharing in the first subject; and / or, judge whether the target data request violates the target compliance policy based on the target user information corresponding to the target user; and / or, judge whether the user corresponding to the target user identifier in the first subject is a non-target user or empty.

[0146] In a possible implementation, the data sharing device 800 further includes:

[0147] The result sending module is used to send a target rejection result to the second server if the target data request is rejected; the target rejection result includes a target rejection factor corresponding to the target data request.

[0148] In a possible implementation, the target data request further carries the target data request type corresponding to the target user; and the first acquisition module 820 is specifically configured to:

[0149] In response to the target data request, first local user data corresponding to the target user is acquired based on the target user identifier and the target data request type.

[0150] Please refer to the following Fig. 9 , which is a structural diagram of another data sharing device provided by an exemplary embodiment of this specification. The above data sharing device is applied to the second server corresponding to the second subject, such as Fig. 9As shown, the data sharing device 900 includes:

[0151] The second sending module 910 is used to send a target data request corresponding to the target user to the first server corresponding to the first subject, so that the first server obtains the first local user data corresponding to the target user based on the target user identifier carried in the target data request, and performs privacy calculation on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information;

[0152] A second receiving module 920, configured to receive the first local user feature sent by the first server;

[0153] The result determination module 930 is used to determine the target data processing result corresponding to the target user based on the first local user feature.

[0154] In a possible implementation, the first local user feature is obtained by the first server performing feature processing on target encoded data after encoding the first local user data based on a target algorithm model; the result determination module 930 is specifically used to: input the first local user feature into the target algorithm model, and output the target data processing result corresponding to the target user; the target algorithm model is obtained by reinforcement learning based on historical local user features of known actual data processing results.

[0155] In a possible implementation, the historical local user feature includes the first historical local user feature corresponding to the first server and / or the second historical local user feature corresponding to the second server;

[0156] The above target algorithm model is updated as the above historical local user features are updated.

[0157] In a possible implementation, the data sharing device 900 further includes:

[0158] A second acquisition module is used to acquire the second local user data of the target user in the second subject;

[0159] A feature determination module, configured to determine a second local user feature corresponding to the target user based on the second local user data;

[0160] The result determination module 930 is specifically used to determine the target data processing result corresponding to the target user based on the first local user feature and the second local user feature.

[0161] In a possible implementation, the result determination module 930 is further used to determine the target data processing result corresponding to the target user based on the second local user feature when receiving the target rejection result corresponding to the target data request sent by the first server.

[0162] The division of each module in the above-mentioned data sharing device is only for illustration. In other embodiments, the data sharing device can be divided into different modules as needed to complete all or part of the functions of the above-mentioned data sharing device. The implementation of each module in the data sharing device provided in the embodiments of this specification can be in the form of a computer program. The computer program can be run on a server. The program module composed of the computer program can be stored in the memory of the access server. When the computer program is executed by the processor, all or part of the steps of the data sharing method described in the embodiments of this specification are implemented.

[0163] See next Fig.10 , which is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of this specification. Fig.10 As shown, the electronic device 1000 may include: at least one processor 1010, at least one communication bus 1020, a user interface 1030, at least one network interface 1040, and a memory 1050. The communication bus 1020 may be used to realize the connection and communication of the above components.

[0164] The user interface 1030 may include a display screen (Display) and a camera (Camera), and the optional user interface may also include a standard wired interface and a wireless interface.

[0165] Among them, the network interface 1040 may optionally include a Bluetooth module, a near field communication (NFC) module, a wireless fidelity (Wi-Fi) module, etc.

[0166] The processor 1010 may include one or more processing cores. The processor 1010 uses various interfaces and lines to connect various parts of the entire electronic device 1000, and executes various functions and processes data of the routing electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1050, and calling data stored in the memory 1050. Optionally, the processor 1010 can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor 1010 can integrate one or a combination of a processor (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communication. It is understandable that the above-mentioned modem may not be integrated into the processor 1010, but may be implemented by a separate chip.

[0167] Among them, the memory 1050 may include a random access memory (RAM) and may also include a read-only memory (ROM). Optionally, the memory 1050 includes a non-transitory computer-readable medium. The memory 1050 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1050 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a privacy computing function, a data sharing function, an encoding function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 1050 may optionally be at least one storage device located away from the aforementioned processor 1010. As Fig.10 As shown, the memory 1050 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program.

[0168] In some possible embodiments, the electronic device 1000 is the first server corresponding to the first subject mentioned in the above embodiments or Figure 8In the data sharing device 800 shown in the figure, the processor 1010 can be used to call the application stored in the memory 1050, and perform the following operations: receiving a target data request sent by the second server corresponding to the second subject; the target data request carries a target user identifier corresponding to the target user; in response to the target data request, obtaining the first local user data corresponding to the target user based on the target user identifier; performing privacy calculation on the first local user data to obtain a first local user feature; the first local user feature is used to distinguish target user group information; sending the first local user feature to the second server, so that the second server determines the target data processing result corresponding to the target user based on the first local user feature.

[0169] In some possible embodiments, when the processor 1010 performs the privacy calculation on the first local user data to obtain the first local user feature, it is specifically configured to perform:

[0170] The first local user data is encoded to obtain target encoded data; and feature processing is performed on the target encoded data to obtain first local user features.

[0171] In some possible embodiments, when the processor 1010 performs the feature processing on the target coded data to obtain the first local user feature, it is specifically used to perform: based on a target algorithm model, perform feature processing on the target coded data to obtain the first local user feature.

[0172] When the processor 1010 executes the sending of the first local user feature to the second server so that the second server determines the target data processing result corresponding to the target user based on the first local user feature, it is specifically used to execute: sending the first local user feature to the second server so that the second server inputs the first local user feature into the target algorithm model and outputs the target data processing result corresponding to the target user; the target algorithm model is obtained by enhanced learning based on historical local user features with known actual data processing results.

[0173] In some possible embodiments, after the processor 1010 executes the receiving of the target data request sent by the second server corresponding to the second subject and before the obtaining of the first local user data corresponding to the target user based on the target user identifier in response to the target data request, it is further configured to execute:

[0174] Determine whether to reject the target data request according to the preset data sharing rules; if the target data request is allowed, execute the step of responding to the target data request and obtaining the first local user data corresponding to the target user based on the target user identifier.

[0175] In some possible embodiments, when the processor 1010 executes the above-mentioned determination of whether to reject the target data request according to the preset data sharing rules, it is specifically used to execute: determining whether the target user has rejected data sharing in the first subject; and / or determining whether the target data request violates the target compliance policy based on the target user information corresponding to the target user; and / or determining whether the user corresponding to the target user identifier in the first subject is a non-target user or is empty.

[0176] In some possible embodiments, after executing the above-mentioned determination of whether to reject the above-mentioned target data request according to the preset data sharing rule, the processor 1010 is further configured to execute:

[0177] If the target data request is rejected, a target rejection result is sent to the second server; the target rejection result includes a target rejection factor corresponding to the target data request.

[0178] In some possible embodiments, the target data request also carries a target data request type corresponding to the target user; when the processor 1010 executes the above-mentioned response to the target data request and obtains the first local user data corresponding to the target user based on the target user identifier, it is specifically used to execute: in response to the target data request, obtain the first local user data corresponding to the target user based on the target user identifier and the target data request type.

[0179] In some possible embodiments, the electronic device 1000 is a second server corresponding to the second subject mentioned in the above embodiments or Fig. 9 In the data sharing device 900 shown in the figure, the processor 1010 can be used to call the application stored in the memory 1050, and specifically perform the following operations: send a target data request corresponding to the target user to the first server corresponding to the first subject, so that the above-mentioned first server obtains the first local user data corresponding to the above-mentioned target user based on the target user identifier carried in the above-mentioned target data request, and performs privacy calculation on the above-mentioned first local user data to obtain a first local user feature; the above-mentioned first local user feature is used to distinguish the target user group information; receive the above-mentioned first local user feature sent by the above-mentioned first server; determine the target data processing result corresponding to the above-mentioned target user based on the above-mentioned first local user feature.

[0180] In some possible embodiments, the first local user feature is obtained by the first server performing feature processing on target encoded data after encoding the first local user data based on a target algorithm model; when the processor 1010 executes the above-mentioned determination of the target data processing result corresponding to the target user based on the first local user feature, it is specifically used to execute: inputting the first local user feature into the target algorithm model, and outputting the target data processing result corresponding to the target user; the target algorithm model is obtained by reinforcement learning based on historical local user features with known actual data processing results.

[0181] In some possible embodiments, the historical local user features include the first historical local user features corresponding to the first server and / or the second historical local user features corresponding to the second server;

[0182] The above target algorithm model is updated as the above historical local user features are updated.

[0183] In some possible embodiments, before the processor 1010 executes the processing result of determining the target data corresponding to the target user based on the first local user feature, it is further configured to execute:

[0184] Acquire second local user data of the target user in the second subject; and determine a second local user feature corresponding to the target user based on the second local user data.

[0185] When the processor 1010 executes the process of determining the target data processing result corresponding to the target user based on the first local user feature, it is specifically used to execute: determining the target data processing result corresponding to the target user based on the first local user feature and the second local user feature.

[0186] In some possible embodiments, after executing the above-mentioned sending of the target data request corresponding to the target user to the first server corresponding to the first subject, the above-mentioned processor 1010 is also used to execute: upon receiving the target rejection result corresponding to the above-mentioned target data request sent by the above-mentioned first server, determining the target data processing result corresponding to the above-mentioned target user based on the above-mentioned second local user characteristics.

[0187] The embodiments of this specification also provide a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes one or more steps in the above embodiments. If the components of the above data sharing device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium.

[0188] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The above computer program product includes one or more computer instructions. When the above computer program instructions are loaded and executed on a computer, the above process or function according to the embodiment of this specification is generated in whole or in part. The above computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The above computer instructions can be stored in a computer-readable storage medium or transmitted by the above computer-readable storage medium. The above computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The above computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more available media integrated. The above-mentioned available media can be magnetic media (for example, floppy disks, hard disks, tapes), optical media (for example, digital versatile discs (DVD)), or semiconductor media (for example, solid-state drives (SSD)), etc.

[0189] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes. In the absence of conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.

[0190] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims.

[0191] The above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims and the specification can be performed in an order different from that in the embodiment recorded in the specification and still achieve the desired results. In addition, the process depicted in the drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A data sharing method, the method being applied to a first server corresponding to a first subject, the method comprising: Receive a target data request sent by a second server corresponding to the second subject; The target data request carries a target user identifier corresponding to the target user; In response to the target data request, acquiring first local user data corresponding to the target user based on the target user identifier; Performing privacy calculation on the first local user data to obtain first local user features; The first local user feature is used to distinguish target user group information; The first local user feature is sent to the second server, so that the second server determines the target data processing result corresponding to the target user based on the first local user feature.

2. The method according to claim 1, wherein performing privacy calculation on the first local user data to obtain the first local user feature comprises: Encoding the first local user data to obtain target encoded data; The target coded data is subjected to feature processing to obtain a first local user feature.

3. The method according to claim 2, wherein the step of processing the target coded data to obtain the first local user feature comprises: Based on the target algorithm model, feature processing is performed on the target coded data to obtain a first local user feature; The sending the first local user feature to the second server so that the second server determines the target data processing result corresponding to the target user based on the first local user feature, includes: Sending the first local user feature to the second server, so that the second server inputs the first local user feature into the target algorithm model and outputs the target data processing result corresponding to the target user; The target algorithm model is obtained by reinforcement learning based on historical local user characteristics of known actual data processing results.

4. The method according to claim 1, after receiving the target data request sent by the second server corresponding to the second subject, and before responding to the target data request and acquiring the first local user data corresponding to the target user based on the target user identifier, the method further comprises: Determine whether to reject the target data request according to the preset data sharing rules; If the target data request is allowed, the step of obtaining the first local user data corresponding to the target user based on the target user identifier in response to the target data request is performed.

5. The method according to claim 4, wherein determining whether to reject the target data request according to a preset data sharing rule comprises: Determining whether the target user has refused data sharing in the first subject; and / or Determining whether the target data request violates a target compliance policy based on target user information corresponding to the target user; and / or Determine whether the user corresponding to the target user identifier in the first subject is a non-target user or is empty.

6. The method according to claim 4, after determining whether to reject the target data request according to the preset data sharing rule, the method further comprises: If the target data request is rejected, sending a target rejection result to the second server; The target rejection result includes a target rejection factor corresponding to the target data request.

7. The method according to claim 1, wherein the target data request further carries a target data request type corresponding to the target user; The step of obtaining, in response to the target data request, first local user data corresponding to the target user based on the target user identifier includes: In response to the target data request, first local user data corresponding to the target user is acquired based on the target user identifier and the target data request type.

8. A data sharing method, the method being applied to a second server corresponding to a second subject, the method comprising: Sending a target data request corresponding to the target user to a first server corresponding to the first subject, so that the first server obtains first local user data corresponding to the target user based on the target user identifier carried in the target data request, and performs privacy calculation on the first local user data to obtain first local user features; The first local user feature is used to distinguish target user group information; Receiving the first local user feature sent by the first server; A target data processing result corresponding to the target user is determined based on the first local user feature.

9. The method according to claim 1, wherein the first local user feature is obtained by the first server performing feature processing on target coded data after encoding the first local user data based on a target algorithm model; The determining the target data processing result corresponding to the target user based on the first local user feature includes: Inputting the first local user feature into the target algorithm model, and outputting a target data processing result corresponding to the target user; The target algorithm model is obtained by reinforcement learning based on historical local user characteristics of known actual data processing results.

10. The method according to claim 9, wherein the historical local user characteristics include a first historical local user characteristic corresponding to the first server and / or a second historical local user characteristic corresponding to the second server; The target algorithm model is updated as the historical local user features are updated.

11. The method according to claim 8, before determining the target data processing result corresponding to the target user based on the first local user feature, the method further comprises: Acquire second local user data of the target user in the second subject; determining a second local user feature corresponding to the target user based on the second local user data; The determining the target data processing result corresponding to the target user based on the first local user feature includes: A target data processing result corresponding to the target user is determined based on the first local user feature and the second local user feature.

12. The method according to claim 11, after sending the target data request corresponding to the target user to the first server corresponding to the first subject, the method further comprises: When a target rejection result corresponding to the target data request sent by the first server is received, a target data processing result corresponding to the target user is determined based on the second local user feature.

13. A data sharing device, the data sharing device being applied to a first server corresponding to a first subject, the data sharing device comprising: A first receiving module, used to receive a target data request sent by a second server corresponding to a second subject; The target data request carries a target user identifier corresponding to the target user; A first acquisition module, configured to respond to the target data request and acquire first local user data corresponding to the target user based on the target user identifier; a privacy computing module, configured to perform privacy computing on the first local user data to obtain first local user features; The first local user feature is used to distinguish target user group information; The first sending module is used to send the first local user feature to the second server, so that the second server determines the target data processing result corresponding to the target user based on the first local user feature.

14. A data sharing device, the data sharing device being applied to a second server corresponding to a second subject, the data sharing device comprising: A second sending module is used to send a target data request corresponding to a target user to a first server corresponding to the first subject, so that the first server obtains first local user data corresponding to the target user based on a target user identifier carried in the target data request, and performs privacy calculation on the first local user data to obtain a first local user feature; The first local user feature is used to distinguish target user group information; A second receiving module, configured to receive the first local user feature sent by the first server; A result determination module is used to determine a target data processing result corresponding to the target user based on the first local user feature.

15. An electronic device, comprising: Processor and memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 12.

16. A computer storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 12.

17. A computer program product comprising instructions, which, when executed on a computer or a processor, enables the computer or the processor to execute the data sharing method according to any one of claims 1 to 12.

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