CN117494014A

CN117494014A - Abnormal object processing method and device, electronic equipment and readable storage medium

Info

Publication number: CN117494014A
Application number: CN202310774767.9A
Authority: CN
Inventors: 李国庆; 唐铃; 杜晓宇; 曾琳铖曦; 蒋宁
Original assignee: Mashang Consumer Finance Co Ltd
Current assignee: Mashang Consumer Finance Co Ltd
Priority date: 2023-06-27
Filing date: 2023-06-27
Publication date: 2024-02-02

Abstract

The application provides an abnormal object processing method, an abnormal object processing device, electronic equipment and a readable storage medium. The method comprises the following steps: performing privacy set intersection processing according to object data sets respectively provided by a plurality of data parties to obtain target objects with the number of hit object data sets being greater than or equal to a threshold value; the object data set of any data party comprises at least one object of which the data party confirms that an abnormality exists; for each target object, determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object from data parties corresponding to object data sets hit by the target object; and sending an invitation notice to the second data party to invite the second data party to participate in the processing task of the target object. The problem of processing resource loss caused by repeatedly sending the invitation notice to the first data party participating in the processing task is avoided, the processing resource is saved, and the use experience is improved.

Description

Abnormal object processing method and device, electronic equipment and readable storage medium

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and apparatus for processing an abnormal object, an electronic device, and a computer readable storage medium.

Background

In each industry, abnormal industries such as black ash are rampant day and night, and the characteristics of flow, normalization and organization are reflected, so that the normal operation order is seriously disturbed, and the operation cost of each mechanism in the industry is increased. In order to focus on striking abnormal activities such as black ash production, an effective scheme is to establish an industry abnormal data sharing mechanism. Each organization can share the data with the abnormality found by the organization, the data sharing platform finds out the abnormality object shared by a plurality of organizations from the shared data, and then carries out the related processing flow to all the organizations with the abnormality object.

In the process of implementing the application, the inventor finds that new institutions are added to the data sharing platform continuously and share data with institutions added before, the common abnormal objects found by the data sharing platform can be found out in the data sharing process of historical time, and if the process is still carried out according to the previous process flow, the same process flow is repeated for some institutions, so that the process resource is wasted and the experience is poor.

Disclosure of Invention

In view of this, the present application provides a method, an apparatus, an electronic device, and a computer readable storage medium for processing an abnormal object, where an invitation notification is sent only to a second data party that does not participate in a processing task of the target object, so as to avoid a problem of processing resource loss caused by repeatedly sending the invitation notification to a first data party that has participated in the processing task, save processing resources, and facilitate improvement of use experience.

In order to achieve the above purpose, the present application provides the following technical solutions:

in a first aspect, the present application provides an abnormal object handling method, the method including:

performing privacy set intersection processing on the plurality of object data sets respectively provided by the plurality of data parties to obtain target objects meeting a threshold value; the object data set of any data party comprises at least one object of which the data party confirms that an abnormality exists; the target object meeting the threshold value represents that the number of object data sets hit by the target object is larger than or equal to the threshold value;

for each target object, determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object from data parties corresponding to object data sets hit by the target object;

and sending an invitation notice to the second data party, wherein the invitation notice is used for inviting the second data party to participate in the processing task of the target object.

In a second aspect, the present application provides an abnormal object handling apparatus, including:

the target object acquisition module is used for carrying out privacy set intersection processing according to object data sets respectively provided by a plurality of data parties to obtain a target object meeting a threshold value; the object data set of any data party comprises at least one object of which the data party confirms that an abnormality exists; the target object meeting the threshold value represents that the number of object data sets hit by the target object is larger than or equal to the threshold value;

The data party classification module is used for determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object from data parties corresponding to the object data sets hit by the target object for each target object;

and the invitation module is used for sending an invitation notice to the second data party, wherein the invitation notice is used for inviting the second data party to participate in the processing task of the target object.

In a third aspect, the present application provides an electronic device comprising:

a processor;

a memory for storing processor-executable instructions;

the processor, when executing the executable instructions, is configured to implement the method of the first aspect.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of the first aspect.

The technical scheme that this application provided can include following beneficial effect:

in the application, privacy set intersection processing is carried out according to object data sets respectively provided by a plurality of data parties, and target objects with the number of hit object data sets being greater than or equal to a threshold value are obtained; then, for each target object, determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object from data parties corresponding to object data sets hit by the target object; and finally, sending an invitation notice to the second data party only to invite the second data party to participate in the processing task of the target object, thereby avoiding the problem of processing resource loss caused by repeatedly sending the invitation notice to the first data party which is already participating in the processing task, saving processing resources and being beneficial to improving the use experience.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

Fig. 1 is a block diagram of an abnormal object handling system exemplarily provided in the present application.

Fig. 2 is a flowchart of an abnormal object handling method exemplarily provided in the present application.

Fig. 3 is a schematic diagram of a processing task display page of a target abnormal object a in the a data side exemplarily provided in the present application.

Fig. 4 is a schematic diagram of a processing task display page of a target abnormal object a in a B data side exemplarily provided in the present application.

Fig. 5 is a schematic diagram of a processing task display page of a target abnormal object a in the data sharing platform provided by an exemplary embodiment of the present application.

FIG. 6 is a schematic diagram of processing a task list in a data sharing platform as exemplarily provided herein.

FIG. 7 is a schematic diagram of processing a task list in a data side as exemplarily provided herein.

FIG. 8 is a flow chart of another method for exception object handling as exemplarily provided herein.

Fig. 9 is a block diagram of an abnormal object handling apparatus exemplarily provided in the present application.

Fig. 10 is a schematic structural diagram of an electronic device exemplarily provided in the present application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments are not representative of all implementations consistent with one or more embodiments of the present application. Rather, they are merely examples of apparatus and methods that are consistent with aspects of one or more embodiments of the present application, as detailed in the accompanying claims.

It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described herein. In some other embodiments, the method may include more or fewer steps than described herein. Furthermore, individual steps described in this application may be described as being broken down into multiple steps in other embodiments; while various steps described in this application may be combined into a single step in other embodiments.

In each industry, abnormal industries such as black ash are rampant day and night, and the characteristics of flow, normalization and organization are reflected, so that the normal operation order is seriously disturbed, and the operation cost of each mechanism in the industry is increased. For example, in the financial industry, for example, for emerging financial industry institutions such as consumer finance and internet finance, due to factors such as historical data precipitation and user scale, the identity of a user is often not confirmed until loss occurs; meanwhile, when the financial industry processes suspected malicious object users, a plurality of internal departments need to be mobilized for cooperative processing, so that a large amount of cost is consumed; considerable costs are also incurred in the activities of evidence collection, reporting, litigation, etc. after the user of the malicious object is confirmed. In order to focus on striking abnormal activities such as black ash production, an effective scheme is to establish an industry abnormal data sharing mechanism. In one possible application scenario, if the organization a has a question about the application or complaint of a certain customer X, a data sharing request can be sent to the peer organization, it is possible to know the performance of the customer X in other organizations, and then determine that the customer X is an abnormal object before the loss of the organization occurs, so as to avoid the loss; even if the loss is generated, if the data can be shared with other institutions, the abnormal objects with serious hazard degree can be jointly confirmed, and evidence collection, case establishment and litigation are jointly initiated, so that on one hand, the related cost can be saved, on the other hand, the case value can be improved, and the cases to be processed by the judicial department can be reduced. In the data sharing platform, each organization can share the data with the abnormality found by the organization, and the data sharing platform can find out the abnormality object shared by a plurality of organizations from the shared data, and then carries out related processing flow on all the organizations with the abnormality object.

In the process of implementing the application, the inventor finds that new institutions are added to the data sharing platform continuously, and performs data sharing with institutions added before, the common abnormal objects found by the data sharing platform may be found by the previous data sharing process, if the process is still performed according to the previous process flow, the process flow which is the same to some institutions is repeated, so that the process resource is wasted and the experience is poor. For example, the data sharing platform may invite all of the organizations that have the abnormal object to participate in the processing task of the abnormal object after finding the abnormal object common to the organizations from the shared data provided by the organizations. If a new mechanism joins the data sharing platform and performs data sharing with a mechanism which has been joined before, the common abnormal object which has been found in the history stage is repeatedly found, and all mechanisms with the abnormal object are invited to participate in the processing task of the abnormal object, the invitation may be repeated for the mechanism which has been joined before, resulting in waste of processing resources and poor experience. For example, assuming that organization A and organization B perform data sharing, a common anomaly object is found, namely first, second, and third, the data sharing platform invites organization A and organization B to process the anomaly object. Adding a new mechanism C into a data sharing platform, and finding common abnormal objects A, B and C when the new mechanism C shares abnormal object data with the mechanism A and the mechanism B; if the data sharing platform aims at the current data sharing process, inviting the mechanism A, the mechanism B and the mechanism C to process the abnormal objects A, B and C; the problem that the invitation is repeated for the same abnormal object and further the processing is possibly repeated is generated for the mechanism A and the mechanism B, so that the processing resource is wasted and the experience is poor.

Therefore, in view of the problems existing in the above situations, the present application provides an abnormal object processing method, which can perform privacy set intersection processing according to object data sets provided by a plurality of data parties respectively to obtain a target object meeting a threshold value, where the target object meeting the threshold value indicates that the number of object data sets hit by the target object is greater than or equal to the threshold value; then, for each target object, determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object from data parties corresponding to object data sets hit by the target object; and finally, sending an invitation notice to the second data party only, wherein the invitation notice is used for inviting the second data party to participate in the processing task of the target object, so that the problem of processing resource loss caused by repeatedly sending the invitation notice to the first data party which participates in the processing task is avoided, the processing resource is saved, and the use experience is improved.

In order to better understand the method for processing abnormal objects provided in the embodiments of the present application, an application environment suitable for the embodiments of the present application is described below.

Referring to fig. 1, an abnormal object handling system is provided in an embodiment of the present application, and includes a data sharing platform 10 and a plurality of data parties 20. "plurality" includes two or more. The system is a distributed system, and the data sharing platform is in communication connection with the data parties, e.g. the data sharing platform may be connected to the respective data parties via a network. The network is used as a medium to provide a communication link between the data sharing platform and the data party. The network may include various connection types, such as wired communication links, wireless communication links, and the like, to which embodiments of the present application are not limited.

The abnormal object processing method provided by the embodiment of the application can be applied to a data sharing platform. The data side responds according to the related request sent by the data sharing platform. For example, after sending the invitation notification to the second party, the data sharing platform may return an invitation feedback indicating whether the target party is involved in the processing task of the anomalous object.

The data sharing platform may be integrated in one or more electronic devices, and the respective data parties may be integrated in one or more electronic devices, which the present embodiment is not limited to in any way. Electronic devices include, but are not limited to, servers, cloud servers, smart phones/handsets, tablet computers, personal Digital Assistants (PDAs), laptop computers, desktop computers, media content players, video gaming stations/systems, virtual reality systems, augmented reality systems, wearable devices (e.g., watches, glasses, gloves, headwear, waistcoats), remote controls, or any other type of device.

Referring to fig. 2, the present application provides an abnormal object processing method applied to the data sharing platform shown in fig. 1, where the method includes:

in S101, performing a privacy set intersection process according to object data sets provided by a plurality of data parties, to obtain a target object meeting a threshold value; the object data set of any data party comprises at least one object of which the data party confirms that an abnormality exists; the target object meeting the threshold value represents that the number of object data sets hit by the target object is greater than or equal to the threshold value.

The data sharing platform can initiate the task of asking for the privacy set periodically or aperiodically, and the data party for which the privacy set asking for the privacy set is asking for each time can be all the data parties added into the data sharing platform, or can be two or more data parties designated by users.

The data sharing platform can perform privacy set intersection processing according to object data sets respectively provided by a plurality of data parties to obtain target objects with the number of hit object data sets being greater than or equal to a threshold value. The privacy set intersection Private Set Intersection refers to that the intersection of two or more parties is solved by exchanging encrypted data by utilizing a cryptography algorithm and a protocol, and meanwhile, the non-intersection part is ensured not to be exposed to other parties.

The object data set of any one party contains at least one object for which the party confirms the presence of an anomaly. For example, each data party can find out a common object with an abnormality in order to ensure that the data party performs private collection intersection with other data parties, an object data set provided by any data party comprises all objects with the abnormality confirmed by the data party, each data party generally adopts a strategy of continuously and incrementally updating the object data set, and newly found objects with the abnormality are added to the object data set of the own party.

In an exemplary embodiment, a user may input a threshold value (assuming that N is an integer greater than 1) in the data sharing platform according to an actual need, where the data sharing platform may, in a process of performing privacy set intersection processing on a plurality of object data sets, find, in response to the input threshold value N, a target object that meets the object data sets of at least N data parties, where the target object is an intersection of the object data sets of at least N data parties.

In S102, for each target object, a first data party participating in a processing task of the target object and a second data party not participating in the processing task of the target object are determined from data parties corresponding to object data sets hit by the target object.

In the private collection exchange process of this time, after at least one target object meeting a threshold value is found, in order to avoid the problem of repeated invitation, whether the found target object is the target object found in the history time period needs to be further confirmed, and considering that the target object found in the history time period usually corresponds to a processing task, for each target object, a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object can be determined from data parties corresponding to object data sets hit by the target object; the first data party participating in the processing task of the target object is the data party which has sent the invitation, and the second data party not participating in the processing task of the target object is the data party which has not sent the invitation.

In S103, an invitation notification is sent to the second data party, the invitation notification being used to invite the second data party to participate in the processing task of the target object.

After determining that the second data party does not send the invitation, the data sharing platform may send an invitation notification to the second data party, where the invitation notification is used to invite the second data party to participate in the processing task of the target object, so as to avoid a problem of processing resource loss caused by repeatedly sending the invitation notification to the first data party that has participated in the processing task, save processing resources, and facilitate improvement of use experience.

Assuming that the threshold value is N, N is an integer greater than 0, the target object refers to an object for which at least N data parties each determine that an anomaly exists. The processing task of the target object can be specifically set according to the actual application scene. For example, in an internet scene, the processing task of the target object may be to perform a sealing process on an account corresponding to the target object. For example, in a financial scenario, the processing task of the target object may be to limit the target object to perform fund freezing processing, limit transfer credit, or jointly perform malicious object striking activities on the target object.

On the one hand, the data sharing platform performs privacy set intersection processing according to object data sets respectively provided by a plurality of data parties to obtain a target object meeting a threshold value. The target object meeting the threshold value represents that the number of object data sets hit by the target object is greater than or equal to the threshold value. The specific value of the threshold value can be specifically set according to the actual application scene under the condition that the threshold value is smaller than the total number of the object data sets participating in the privacy set intersection.

For example, considering that the objects with the anomalies may be further classified into different types according to the reasons of the anomalies, in order to improve the processing accuracy, each privacy set task may perform the task of intersection on the same type of objects. For example, if the present task of privacy collection is performed on the object of the malicious credit type, the data sharing platform needs to perform privacy collection processing according to the malicious credit data sets provided by the multiple data parties participating in the privacy collection processing, where the malicious credit data sets of the data parties include the malicious credit users confirmed by the data parties.

The privacy set intersection process (Private Set Intersection, PSI) is a privacy-preserving computing protocol for comparing intersections between two or more sets without revealing specific elements. In the private collection exchange process, the common protocols are cryptography-based methods such as zero knowledge proof, homomorphic encryption, secure multiparty computation, differential privacy, encryption hash, etc. An exemplary description is given here of a cryptographic hash-based protocol: for a plurality of data parties participating in the exchange of the privacy set, each object in the object data set of each data party carries anonymous identification and variable information, and each data party carries out encryption processing on the objects in the object data set provided by the data party so as to protect the privacy of the data; then, each data party hashes the encrypted object data set to generate a corresponding encrypted hash value and returns the corresponding encrypted hash value to the data sharing platform. The data sharing platform uses the algorithm and the protocol defined in the protocol to carry out the comparison calculation of the encryption hash value, and in the process, the collection intersection operation in the protocol can be used to find the target object hitting at least N object data sets; n is a set threshold value. After the calculation is completed, the encryption result of the intersection is converted into a plaintext through the decryption process defined by the protocol, in the intersection result of the plaintext, the anonymous identifier of each target object is determined to appear in a plurality of object data sets according to the variable information of the target object, and the target object which hits at least N object data sets is found. The variable information carried by each object in the object data set of each data party can be specifically set according to the actual application scenario, which is not limited in this embodiment.

For example, the data sharing platform may select six data parties ABCDEF to perform a task of private collection, and the threshold is set to 3, so that the result of the private collection intersection processing includes target objects hit 3,4,5 or 6 data parties. The privacy set intersection task is a joint calculation task which is jointly executed by the data sharing platform and all data parties participating in the task, specifically, the data direction data sharing platform of each participating task is required to send an encrypted object data set, the data sharing platform generates an intersection processing result after calculation according to an algorithm, namely, a target object meeting a threshold value is obtained, and the number of the object data sets hit by the target object is greater than or equal to the threshold value.

On the one hand, in order to avoid that the true identity of the abnormal object is recorded by a non-associated third party, each object in the object data set provided by each data party can carry an anonymous identifier, so that the data sharing platform only knows the anonymous identifier of each abnormal object and cannot reversely push the true identity information, and the data security is ensured.

Each object in the object dataset of any data party carries an anonymous identifier, the anonymous identifier carried by each object is dynamically generated by the data party in the privacy set exchange processing process, and each data party stores a mapping relationship between the anonymous identifier of each object and real identity information. By adopting the anonymization technology to process the information capable of identifying the real identity of the object, only the anonymization information is recorded in the data sharing platform, so that the real identity of the object is prevented from being recorded by a non-associated third party, and the security of multiparty data is ensured.

It can be understood that the generation mode of the anonymous identifier of the abnormal object is not limited, and the generation mode can be specifically set according to the actual application scene, for example, each data party can generate the anonymous identifier of each object in the object data set by adopting a random number algorithm, and even the anonymous identifier generated by the same object in different privacy set interaction tasks has variability due to the adoption of the random number algorithm, so that the data party participating in the tasks can be prevented from precipitating information about the target object by recording the identification information, and the data security is ensured.

After each data party generates the anonymous identifier, the mapping relation between the anonymous identifier of the object and the real identity information can be recorded, that is, only the data party can map the anonymous identifier back to the real identity information, and the data sharing platform only knows the anonymous identifier of each object and cannot reversely push the real identity information. For example, referring to fig. 3, 4 and 5, fig. 3 shows that in the display page of the data side a, the target object a displays corresponding real identity information, fig. 4 shows that in the display page of the data side B, the target object a displays corresponding real identity information, and fig. 5 shows that in the display page of the data sharing platform, the target object a does not display real identity information.

After determining the at least one target object, the data sharing platform may generate a platform identification corresponding to each target object, the platform identification uniquely identifying one target object for the data sharing platform. That is, the data sharing platform records the platform identifier of each target object in the data sharing platform and the anonymous identifier of each target object in the hit object dataset, and the data sharing platform cannot reversely push out the real identity information of the target object through the platform identifier or the anonymous identifier, so that the data security is ensured. It can be understood that the generation mode of the platform identifier is not limited, and the specific setting can be performed according to an actual application scene, for example, the data sharing platform can generate the platform identifier of the target object by adopting a random number algorithm.

It should be noted that, the platform identifier of the target object and the anonymous identifier of the hit data party are dynamically generated in the current object scanning task, and have no relation with the historical object scanning task, that is, the platform identifier of the same target object in different object scanning tasks and the anonymous identifier of the hit data party are different, so that any data party participating in the task can be prevented from precipitating information about the target object by recording the identifier information, and the data security is ensured.

For example, suppose a target object hits an object dataset of data party a, data party B, and data party C, refer to table 1, table 1 shows the platform identity corresponding to the target object and the anonymous identities of the hit data party a, data party B, and data party C.

TABLE 1

In one aspect, the data sharing platform performs the following operations for each target object after determining at least one target object. First, for each target object, a first data party participating in a processing task of the target object and a second data party not participating in the processing task of the target object are determined from data parties corresponding to object data sets hit by the target object.

For any one data party, if the data party participates in the processing task of any one target object in the historical time, the data party stores the processing task and task information of the target object. Therefore, for each target object, the data sharing platform can send a task query request to the data party corresponding to the object data set hit by the target object, and obtain the task query result returned by each data party; the task query request is used for querying whether a data party has a processing task of the target object and task information, the task query result comprises a participated processing task and an un-participated processing task, the participated task is used for indicating that the data party has the processing information of the processing task, and the un-participated processing task is used for indicating that the data party does not have the processing information of the processing task. The data sharing platform can determine a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object according to the task query results returned by the data parties. The method and the device can accurately determine the first data party and the second data party through the interaction process with each data party, so that the accurate sending of the subsequent invitation notification is ensured.

Wherein each object in the object dataset of any data party carries an anonymous identifier; task inquiry requests respectively sent to all data parties carry anonymous identifications of the target object in the data parties, so that the data parties can conveniently identify the target object, and further the real identity information corresponding to the target object can be known; the task query results returned by each data party also carry anonymous identifications of the target objects in the data party, so that the data sharing platform confirms the target objects based on the record information shown in table 1.

The task query results respectively returned by the data parties corresponding to the object data sets hit by the target objects are in the following three conditions:

in the first case, if the task query result returned by at least two data parties indicates that a processing task has been participated in and the task information is the same, indicating that the processing task of the target object already exists, the data party whose task query result indicates that a processing task has been participated in may be determined as the first data party, and the data party whose task query result indicates that a processing task has not been participated in may be determined as the second data party. The method and the device can accurately determine the first data party and the second data party by judging the task query results returned by the data parties.

In the second case, if all the data parties return task query results indicating that the processing task is not involved in the processing task, indicating that the processing task of the target object is not yet established, the data party, for which the task query results indicate that the processing task is not involved in, may be determined as the second data party. The second data party is accurately determined by judging the task query results returned by each data party.

In the third case, if the above two cases are not satisfied, an abnormal situation may occur, which may be caused by inconsistent data due to network or hardware, for example, only one data party returns a task query result indicating that the task has been involved in processing. A mechanism is needed at this time to build a processing task for the currently determined target object. In this case, therefore, all the data sides hit by the target object can be determined as the second data side. The task query result returned by each data party is judged, so that possible abnormal conditions can be dealt with, and better fault tolerance is achieved.

After task query results respectively returned by the data parties corresponding to the object data sets hit by the target objects are received, by judging which of the three conditions is met by all the task query results, the first data party and the second data party can be accurately determined, corresponding countermeasures are provided even if abnormal conditions occur, and better fault tolerance is achieved.

By way of example, the task information in which the processing task is stored in the data side is described as a task identifier: for each target object, the data sharing platform may send task query requests to the data parties corresponding to the object data sets hit by the target object, where the task query requests are used to query whether the data parties store task identifiers of processing tasks of the target object, where the task identifiers are identifiers generated when the processing tasks of the target object are established and used to uniquely identify the processing tasks. If the task query result returned by any data party comprises the task identifier, representing that the data party participates in the processing task of the target object; and if the task query result returned by any data party does not comprise the task identifier, representing that the data party does not participate in the processing task of the target object.

In one possible implementation, the task identifier of the processing task stored by the data side is a digital identifier, and is within a preset range of values, such as [1,100000], but is not limited thereto. If the task query result returned by any data party comprises a digital identifier and the digital identifier is in a preset digital range, representing that the data party participates in the processing task of the target object; if the content contained in the task query result returned by any data party is not in the preset digital range, the data party is characterized as not participating in the processing task of the target object.

In another possible implementation manner, the task identifier of the processing task stored by the data party may also be a character identifier, or an identifier combined with at least two of a number, a character and a punctuation mark, which is not limited in any way by the embodiment.

For example, the data party responds to the task query request to query the task list of the data party, and if the processing task of the target object is queried, the task query result carrying the task identifier of the processing task can be returned to the data sharing platform; if the processing task of the target object is not queried, a task query result carrying information indicating that the task identifier is not included can be returned to the data sharing platform. It may be appreciated that the specific content of the information indicating that the task identifier is not included may be specifically set according to the actual application scenario, which is not limited in this embodiment.

The information indicating that the task identifier is not included is '1', and the task query results respectively returned by the data parties corresponding to the object data sets hit by the target object are as follows:

in the first case, if the task query results returned by at least two data parties contain the task identity and are identical, the data party containing the task identity may be determined to be the first data party and the data party containing "-1" may be determined to be the second data party.

In the second case, if all the data parties return a task query result containing "-1", the data party containing "-1" may be determined to be the second data party.

In the third case, if the above two cases are not satisfied, an abnormal situation may occur, possibly due to data inconsistency caused by a network or hardware or the like, in which case all the data sides hit by the target object may be determined as the second data side; or determining the user-specified data party as the second data party.

By judging which of the three conditions is met by all task query results, the first data party and the second data party can be accurately determined, corresponding countermeasures are provided even if abnormal conditions occur, and better fault tolerance is achieved.

In one aspect, after determining a second data party that is not participating in the processing task of the target object, the data sharing platform may send an invitation notification to the second data party to invite the second data party to participate in the processing task of the target object. The invitation notice sent to the second data party carries anonymous identification of the target object in the second data party, so that the second data party can conveniently recognize, and further real identity information corresponding to the target object is known. The method and the device have the advantages that the invitation notification is only sent to the second data party which does not participate in the processing task, the invitation notification and the related push message do not need to be repeatedly sent to the data party which participates in the processing task, processing resources are saved, and the using experience is improved.

After receiving the invitation notification, the second data party can confirm whether to participate in the processing task of the target object according to actual needs, and returns invitation feedback to the data sharing platform. The data sharing platform can receive invitation feedback returned by each second data party, wherein the invitation feedback is used for indicating whether the second data party participates in the processing task of the object; and then determining whether to initiate the labeling flow according to the invitation feedback returned by each second data party.

Each object in the object data set of any data party carries an anonymous identifier; each of the plurality of data parties involved in the privacy set intersection corresponds to a data party identifier, and the data party identifier is used for uniquely identifying one data party; the processing task of the target object corresponds to a task identifier; the task labeling flow is used for adding a data party identifier of a second data party and an anonymous identifier of the target object in the second data party in a data party list corresponding to the processing task of the target object for each second data party to be involved in the processing task, synchronizing the task identifier of the processing task of the target object to the second data party, enabling the second data party to newly add the processing task in the processing task list, and associating the task identifier with the anonymous identifier of the target object.

According to invitation feedback returned by each second data party, whether to initiate an annotation flow is determined, wherein the following cases exist:

in the first case, if the number of the first data parties is not less than two and at least one invitation feedback returned by the second data party indicates to participate in the processing task, which indicates that the processing task of the target object is already existing, the data sharing platform may initiate a task labeling procedure according to the processing task existing in the target object. The embodiment realizes that the labeling flow is only initiated to the second data party which does not participate in the processing task and accepts the invitation, thereby avoiding repeated information transmission to the data party which participates in the processing task, saving processing resources and being beneficial to improving the use experience.

In the process of initiating a task labeling flow according to the existing processing task of the target object, the data sharing platform can query a processing task list of the data sharing platform to determine the processing task of the target object, then for each second data party to be involved in the processing task, the data party identifier of the second data party and the anonymous identifier of the target object in the second data party are added in the data party list corresponding to the processing task of the target object, and the task identifier of the processing task of the target object is synchronized to the second data party, so that the processing task is newly added in the processing task list by the second data party, and the task identifier and the anonymous identifier of the target object are associated. The data sharing platform performs the operation of adding the processing task participants according to the feedback of the second data party, so that the data party added later can be added into the processing task which is already existing and not yet finished to participate in the processing task of the target object together.

For example, referring to fig. 6 and 7, fig. 6 shows a schematic diagram of a processing task list of a data sharing platform, where the processing task list of the data sharing platform includes a platform identifier and a task identifier of a target object; fig. 7 shows a schematic diagram of processing tasks of each data party, and the processing task list of the data party includes anonymous identification, task identification and real identity information of a target object.

In the second case, if the number of the first data sides is less than two and there are at least two invitation feedback instructions returned by the second data sides to participate in the processing task, the processing task of the target object is newly built, and a task labeling flow is initiated according to the newly built processing task.

For example, in the process of newly building the processing task of the target object, a task identifier of the processing task of the target object may be generated according to a preset rule, for example, in a case of no conflict, the task identifier may be a date+sequence number, or a date+random number, or a date+platform identifier of the target object, but is not limited thereto.

For example, referring to fig. 6 and fig. 7, in the process of generating the processing task of the target object, the data sharing platform may newly add a new processing task to the processing task list, in the process of initiating a task labeling procedure according to the new processing task, for each second data party to be involved in the processing task, the data sharing platform may add, to the data party list corresponding to the processing task of the target object, a data party identifier of the second data party, and an anonymous identifier of the target object in the second data party, and synchronize the task identifier of the processing task of the target object to the second data party, so that the second data party newly adds the processing task to the processing task list, and associates the task identifier with the anonymous identifier of the target object. And the data sharing platform performs newly-built processing task operation according to the feedback of the second data party so that the second data party receiving the invitation participates in the processing task of the target object together.

In the third case, if the above two cases are not satisfied, the anomaly labeling flow is not initiated.

In one example, the data sharing platform performs privacy set intersection processing on object data sets of three data sides ABC in a first privacy set intersection task, and finds a target object meeting a threshold value: zhang Sang, and there is no processing task for the target object, a processing task of "Zhang Sang" is generated, and the task identification case_id= 20230519001.

Then the data sharing platform performs a second privacy set intersection task, namely performing privacy set intersection processing on object data sets of four data parties of the ABCD, and finding out a target object meeting a threshold value: thirdly, stretching; assuming that the object data sets of four data sides hit 'Zhang Sanling', the data sharing platform sends task query requests to the four data sides of ABCD, task identifications case_id= 20230519001 returned by the three data sides of ABC, the target object of 'Zhang Sanling' has no data record in a D mechanism, information '-1' indicating that no task identification exists is returned, because the target object of 'Zhang Sanling' is hit for the D data side for the first time, the data sharing platform sends invitation notification to the D data side, the D data side feeds back and participates in the processing task aiming at 'Zhang Sanling', a labeling flow is initiated according to the processing task of 'Zhang Sanling', the data sharing platform adds the data side identification of the D data side and the anonymous identification of 'Zhang Sanling' in the D data side in the processing task of 'Zhang Sanling', and the data sharing platform synchronizes the task identifications case_id= 20230519001 of the processing task of 'Zhang Sanling' to the D data side; the D data party can newly add the processing task in the processing task list, record the task identification case_id= 20230519001 of the processing task of 'Zhang Sanning', and correlate the task identification case_id= 20230519001 of the processing task of 'Zhang Sanning' with the anonymous identification of 'Zhang Sanning' in the D data party, so that the D data party is added to the existing processing task of 'Zhang Sanning', and the task processing is carried out by combining a plurality of data parties, thereby obtaining processing benefits.

In an exemplary embodiment, taking a financial scenario as an example, a plurality of data parties are a plurality of financial institutions, and an object with an anomaly is a malicious object in the financial industry, including but not limited to a malicious agent object, an illegal fraud object, a malicious complaint object, and the like. Malicious agent objects include, but are not limited to, illegal agents or illegal agencies, etc., that is, agents or agencies of malicious complaints individuals confirmed after inspection and verification, agents or agency of offending clients, personnel or agency structures confirming complaints of agent clients as professions, etc. The illegally frauds include, but are not limited to, fraudsters, lenders or lenders, etc., i.e., frauds of financial institutions loans with imaginary facts or hidden facts for the purpose of illegitimate possession, and refuses unrepeated persons or companies. Malicious complaint objects include, but are not limited to, malicious complaints, persons repeatedly maliciously requiring censoring, persons providing false credentials, or persons stressing the company with a group risk, etc. Various data parties in the financial industry may be various financial institutions with data sharing requirements between them to enhance the impact on malicious objects. The data sharing platform can adopt a scheme based on a privacy computing technology to find out malicious objects shared by a plurality of institutions, and then adopts a combined striking measure.

Referring to fig. 8, fig. 8 is a flow chart of another abnormal object processing method provided in the present application, where the method includes:

in S201, the data sharing platform responds to the malicious object scanning task starting instruction, and performs privacy aggregation intersection processing according to malicious object data sets of the a financial institution, the B financial institution and the C financial institution, so as to obtain a target malicious object meeting a threshold value, where the target malicious object meeting the threshold value characterizes that the number of malicious object data sets hit by the target malicious object is greater than or equal to the threshold value.

The malicious object data set of any financial institution comprises at least one malicious object confirmed by the financial institution, and each malicious object carries an anonymous identifier; after any financial institution encrypts the malicious object data set, the encrypted malicious object data set is sent to the data sharing platform for privacy exchange processing.

The anonymous identifier carried by each malicious object is dynamically generated by the financial institutions in the privacy exchange processing process, and each financial institution stores a mapping relation between the anonymous identifier of each malicious object and the real identity information. By adopting the anonymization technology to process the information capable of identifying the true identity of the malicious object, only the anonymized information is recorded in the data sharing platform, so that the true identity of the malicious object is prevented from being recorded by a non-associated third party, and the safety of multiparty data is ensured.

Assuming a threshold of 2, a target malicious object a is determined that hits the malicious object datasets for the a, B, and C financial institutions.

In S202, for the target malicious object a, the data sharing platform sends task query requests to the a financial institution, the B financial institution, and the C financial institution, respectively, where the task query requests are used to query whether the financial institution has a hit task and a task identifier of the target malicious object a.

In S203, the data sharing platform receives task query results returned by the a financial institution, the B financial institution, and the C financial institution; if the task query result returned by any financial institution comprises the task identifier, representing that the financial institution participates in the hitting task of the target malicious object; if the task query result returned by any financial institution does not include the task identifier (for example, includes information indicating that the task identifier is not included), the financial institution is characterized as not participating in the hit task of the target malicious object.

In S204, according to the task query results returned by the a-financial institution, the B-financial institution and the C-financial institution, a first financial institution participating in the task of striking the target malicious object a and a second financial institution not participating in the task of striking the target malicious object a are determined.

Assuming that the information indicating that the task identity is not included is "-1", there are three cases:

in the first case, if the task query results returned by at least two financial institutions contain the task identity and are the same, the financial institution containing the task identity may be determined to be the first financial institution and the financial institution containing "-1" may be determined to be the second financial institution.

In the second case, if all of the financial institutions returned task query results contain a "-1", then the financial institution containing the "-1" may be determined to be the second financial institution.

In the third case, if the above two cases are not satisfied, an abnormal situation may occur, possibly due to data inconsistency caused by a network or hardware or the like, in which case all financial institutions hit by the target malicious object may be determined as the second financial institution; or determining the user-specified financial institution as the second financial institution.

Assuming that the task query results returned by the a financial institution and the B financial institution include task identifications, and the task query results returned by the C financial institution include "-1" representing the non-participating task, the first situation is satisfied, the first financial institution is determined to be the a financial institution and the B financial institution, and the second financial institution is determined to be the C financial institution.

In S205, the data sharing platform sends an invitation notification to the C financial institution, the invitation notification being for inviting the C financial institution to participate in the hit task of the target malicious object a; and receiving invitation feedback returned by the C financial institution.

And C, the financial institution determines whether to participate in the hitting task according to the actual situation and returns invitation feedback. The method and the device have the advantages that the invitation notification is only sent to the second data party which does not participate in the processing task, the invitation notification and the related push message do not need to be repeatedly sent to the data party which participates in the processing task, processing resources are saved, and the using experience is improved.

In S206, the data sharing platform determines, according to the received invitation feedback, whether a malicious object labeling procedure needs to be initiated.

There are three cases:

in the first case, if the number of the first financial institutions is not less than two and at least one invitation feedback instruction returned by the second financial institutions participates in the hit task, which indicates that the hit task of the target malicious object is already existing, the data sharing platform may initiate a task annotation process according to the hit task existing by the target malicious object.

In the second case, if the number of the first financial institutions is less than two and there are at least two invitation feedback indications returned by the second financial institutions to participate in the hit task, the hit task of the target malicious object is newly built, and a task labeling flow is initiated according to the newly built hit task.

Assuming that the invitation feedback returned by the financial institution C indicates the hitting task of the target malicious object a, the data sharing platform determines that the hitting task of the target malicious object a exists, and the first condition is satisfied, and then the malicious object labeling flow needs to be initiated according to the hitting task existing in the target malicious object a. The data sharing platform performs the operation of adding the hitting task participants according to the feedback of the second financial institutions, so that the second financial institutions added later can be added into the hitting tasks which are already existing and not yet finished to participate in the hitting tasks of the target malicious objects together.

In S207, the data sharing platform may add a financial institution identifier of the C financial institution and an anonymous identifier of the target malicious object a in the C financial institution in the financial institution list corresponding to the hit task of the target malicious object a, and synchronize the task identifier of the hit task of the target malicious object a to the C financial institution, so that the C financial institution newly adds the hit task of the target malicious object a in the hit task list, and associates the task identifier with the anonymous identifier of the target malicious object a.

It will be appreciated that the above-mentioned number of financial institutions is merely illustrative, and the number of financial institutions involved in the privacy collection may be specifically set according to the actual application scenario, which is not limited in this embodiment.

The various technical features in the above embodiments may be arbitrarily combined as long as there is no conflict or contradiction between the features, but are not described in detail, and therefore, the arbitrary combination of the various technical features in the above embodiments also falls within the scope of the disclosure of the present application.

Referring to fig. 9, the present application further provides an abnormal object processing apparatus, including:

the target object obtaining module 301 is configured to perform privacy set intersection processing on a plurality of object data sets according to a plurality of data parties respectively provided to obtain a target object meeting a threshold value; the object data set of any data party comprises at least one object of which the data party confirms that an abnormality exists; the target object meeting the threshold value represents that the number of object data sets hit by the target object is greater than or equal to the threshold value.

The data party classification module 302 is configured to determine, for each target object, a first data party participating in a processing task of the target object and a second data party not participating in the processing task of the target object from data parties corresponding to object data sets hit by the target object.

And the invitation module 303 is configured to send an invitation notification to the second data party, where the invitation notification is used to invite the second data party to participate in a processing task of the target object.

In one aspect, each object in the object dataset of any data party carries an anonymous identifier, the anonymous identifier carried by each object is dynamically generated by the data party in the privacy set exchange processing process, and each data party stores a mapping relationship between the anonymous identifier of each object and real identity information.

The device is applied to a data sharing platform, and the data sharing platform records the platform identification of each target object in the data sharing platform and the anonymous identification of each target object in a hit object data set.

And the invitation notice sent to the second data party carries the anonymous identification of the target object in the second data party.

In one aspect, the data-party classification module 302 includes a query unit and a classification unit.

The query unit is used for sending a task query request to the data party corresponding to the object data set hit by each target object, and obtaining a task query result returned by each data party; the task query request is used for querying whether a data party has a processing task of the target object and task information, the task query result comprises a participated processing task or a non-participated processing task, the participated task is used for indicating that the data party has the processing information of the processing task, and the non-participated processing task is used for indicating that the data party does not have the processing information of the processing task.

The classification unit is used for determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object according to the task query results returned by the data parties.

In one aspect, the classification unit is specifically configured to: if the task query results returned by the data sides meet the preset conditions, determining the data side of which the task query result indicates to participate in processing the task as a first data side, and determining the data side of which the task query result indicates to not participate in processing the task as a second data side; if the task query results returned by the data sides do not meet the preset conditions, determining the data sides corresponding to all object data sets hit by the target object as second data sides; the preset conditions include: the task query results returned by the at least two data parties indicate that the processing task is participated and the task information is the same.

In one aspect, the task information includes a task identifier of a processing task; the task query request is used for querying whether a data party stores a task identifier of a processing task of the target object, wherein the task identifier is an identifier which is generated when the processing task of the target object is established and is used for uniquely identifying the processing task; if the task query result returned by any data party comprises the task identifier, representing that the data party participates in the processing task of the target object; and if the task query result returned by any data party does not comprise the task identifier, representing that the data party does not participate in the processing task of the target object.

In one aspect, the abnormal object processing device further includes a labeling module, where the labeling module is configured to: receiving invitation feedback returned by each second data party, wherein the invitation feedback is used for indicating whether the second data party participates in the processing task of the target object; determining whether to initiate a task annotation flow according to invitation feedback returned by each second data party, wherein each object in the object dataset of any data party carries an anonymous identifier; each of the plurality of data parties participating in the privacy set intersection corresponds to a data party identifier; the processing task of the target object corresponds to a task identifier; the task labeling flow is used for adding a data party identifier of a second data party and an anonymous identifier of the target object in the second data party in a data party list corresponding to the processing task of the target object for each second data party to be involved in the processing task, synchronizing the task identifier of the processing task of the target object to the second data party, enabling the second data party to newly add the processing task in the processing task list, and associating the task identifier with the anonymous identifier of the target object.

In one aspect, the labeling module is specifically configured to: if the number of the first data sides is not less than two and at least one invitation feedback instruction returned by the second data side participates in the processing task, initiating a task labeling flow according to the processing task existing in the target object; if the number of the first data sides is less than two and at least two invitation feedback instructions returned by the second data sides participate in the processing task, a processing task of the target object is newly built, and a task labeling flow is initiated according to the newly built processing task; otherwise, the labeling flow is not initiated.

The implementation process of the functions and roles of each module in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.

In some embodiments, the present application further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; the processor implements the method for processing an abnormal object according to any one of the above by executing the executable instructions.

For example, referring to FIG. 10, an electronic device includes a processor 402, an internal bus 404, a network interface 406, a memory 408, and a non-volatile storage 410, although other services may include hardware as desired. The exception object handling method provided herein may be implemented in a software manner, such as by the processor 402 reading a corresponding computer program from the non-volatile memory 410 into the memory 408 and then running. Of course, in addition to software implementation, one or more embodiments of the present application do not exclude other implementation, such as a logic device or a combination of software and hardware, etc., that is, the execution subject of the following process flows is not limited to each logic unit, but may also be hardware or a logic device.

The memory may include volatile memory in a computer-readable storage medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM).

In some embodiments, the present application also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of exception object handling as described in any of the above.

Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

In some embodiments, the present application also provides a computer program product, which implements the method for processing an abnormal object according to any one of the above at runtime.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The foregoing describes specific embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The terminology used in one or more embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the application. As used in this application in one or more embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in one or more embodiments of the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of one or more embodiments of the present application. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.

The foregoing description of the preferred embodiment(s) is (are) merely intended to illustrate the embodiment(s) of the present application and is not intended to limit the embodiment(s) of the present application, since any modification, equivalent replacement, improvement or the like which comes within the spirit and principles of the embodiment(s) of the present application is included within the scope of the present application.

Claims

1. A method of exception object handling, the method comprising:

performing privacy set intersection processing according to object data sets respectively provided by a plurality of data parties to obtain a target object meeting a threshold value; the object data set of any data party comprises at least one object of which the data party confirms that an abnormality exists; the target object meeting the threshold value represents that the number of object data sets hit by the target object is larger than or equal to the threshold value;

2. The method according to claim 1, wherein each object in the object dataset of any data party carries an anonymous identifier, the anonymous identifier carried by each object is dynamically generated by the data party in the privacy set exchange processing process, and each data party stores a mapping relationship between the anonymous identifier and real identity information of each object;

the abnormal object processing method is applied to a data sharing platform, and the data sharing platform records a platform identifier of each target object in the data sharing platform and an anonymous identifier of each target object in a hit object data set;

3. The method according to claim 1, wherein for each target object, determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object from data parties corresponding to the object data sets hit by the target object includes:

for each target object, sending a task query request to a data party corresponding to an object data set hit by the target object, and obtaining a task query result returned by each data party; the task query request is used for querying whether a data party has a processing task of the target object and task information, the task query result comprises a participated processing task or a non-participated processing task, the participated task is used for indicating that the data party has the processing information of the processing task, and the non-participated processing task is used for indicating that the data party does not have the processing information of the processing task;

And determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object according to the task query results returned by the data parties.

4. A method according to claim 3, wherein determining a first data party participating in the processing task of the target object and a second data party not participating in the processing task of the target object according to the task query results returned by the respective data parties comprises:

if the task query results returned by the data sides meet the preset conditions, determining the data side of which the task query result indicates to participate in processing the task as a first data side, and determining the data side of which the task query result indicates to not participate in processing the task as a second data side;

if the task query results returned by the data sides do not meet the preset conditions, determining the data sides corresponding to all object data sets hit by the target object as second data sides;

the preset conditions include: the task query results returned by the at least two data parties indicate that the processing task is participated and the task information is the same.

5. A method according to claim 3, wherein the task information comprises a task identification of a processing task;

The task query request is used for querying whether a data party stores a task identifier of a processing task of the target object, wherein the task identifier is an identifier which is generated when the processing task of the target object is established and is used for uniquely identifying the processing task;

if the task query result returned by any data party comprises the task identifier, representing that the data party participates in the processing task of the target object; and if the task query result returned by any data party does not comprise the task identifier, representing that the data party does not participate in the processing task of the target object.

6. The method according to any one of claims 1 to 5, further comprising, after said sending an invitation notification to said second data party, after said invitation notification is used to invite said second data party to participate in a processing task of the target object:

receiving invitation feedback returned by each second data party, wherein the invitation feedback is used for indicating whether the second data party participates in the processing task of the target object;

determining whether to initiate a task labeling flow according to invitation feedback returned by each second data party;

wherein, each object in the object data set of any data party carries an anonymous identifier; each of the plurality of data parties participating in the privacy set intersection corresponds to a data party identifier; the processing task of the target object corresponds to a task identifier; the task labeling flow is used for adding a data party identifier of a second data party and an anonymous identifier of the target object in the second data party in a data party list corresponding to the processing task of the target object for each second data party to be involved in the processing task, synchronizing the task identifier of the processing task of the target object to the second data party, enabling the second data party to newly add the processing task in the processing task list, and associating the task identifier with the anonymous identifier of the target object.

7. The method of claim 6, wherein determining whether to initiate the task annotation procedure based on the invitation feedback returned by each second party comprises:

if the number of the first data sides is not less than two and at least one invitation feedback instruction returned by the second data side participates in the processing task, initiating a task labeling flow according to the processing task existing in the target object;

if the number of the first data sides is less than two and at least two invitation feedback instructions returned by the second data sides participate in the processing task, a processing task of the target object is newly built, and a task labeling flow is initiated according to the newly built processing task;

otherwise, the labeling flow is not initiated.

8. An abnormal object handling apparatus, comprising:

9. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

the processor implements the exception object handling method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the method of exception object handling according to any of claims 1 to 7.