WO2021098274A1

WO2021098274A1 - Method and apparatus for evaluating risk of leakage of private data

Info

Publication number: WO2021098274A1
Application number: PCT/CN2020/105106
Authority: WO
Inventors: 邓圆
Original assignee: 支付宝(杭州)信息技术有限公司
Priority date: 2019-11-19
Filing date: 2020-07-28
Publication date: 2021-05-27
Also published as: CN110851872B; CN110851872A; TWI734466B; TW202121329A

Abstract

A method for evaluating the risk of leakage of private data. The method comprises: firstly, acquiring several system logs and several network traffic records generated by a requester requesting the calling of private data, stored in a service platform, of a target object, wherein each system log is generated on the basis of a request message sent by the requester to the service platform for calling an API, and each network traffic record comprises at least a response message returned by the service platform with regard to the request message (210); next, performing parsing processing on the several network traffic records to obtain parsing data (220); then, acquiring, from the service platform, permission data of the requester to call the API (230); next, comparing the several system logs with the permission data to obtain a first comparison result, and comparing the parsing data with the permission data to obtain a second comparison result (240); and then at least on the basis of the first comparison result and the second comparison result, evaluating the risk of leakage of private data with regard to the requester calling the API (250).

Description

Risk assessment method and device for privacy data leakage

Technical field

One or more embodiments of this specification relate to the technical field of data information security, and in particular, to a risk assessment method and device for private data leakage.

Background technique

API (Application Programming Interface) has the advantages of convenient calling and strong versatility, and has gradually become the main way of providing Internet network services. Therefore, API calls have also become a key focus area to prevent data leakage.

The data stored by the service platform usually includes the basic information data of the objects it serves (such as individuals or enterprises, etc.), as well as the service data generated during the use of the service. In the case that the service object is authorized, the service platform can provide API call services to the data demander (such as research institutions or merchants, etc.) based on these data. Under normal circumstances, the data demander (or requester) can only obtain the data for which it has permission to use it through API calls. However, the software and hardware environments, IT architectures, and business scenarios of different requesters (including requesters scattered in different regions, such as cross-border merchants, etc.) are often different, and there are large differences, resulting in a complex API call system and easy to be illegal. Molecular use causes data leakage, which undoubtedly brings great challenges to the data protection of API calls. Especially considering that the leaked data is likely to include the user's personal information and other private data, the prevention of data leaks is becoming more urgent.

Therefore, a reasonable and reliable solution is needed to conduct timely and accurate assessment of the risk of data leakage due to API calls, especially the risk of private data leakage, so as to effectively prevent the leakage of private data.

Summary of the invention

One or more embodiments of this specification describe a risk assessment method and device for privacy data leakage, which can conduct timely and accurate assessment of the risk of privacy data leakage due to API calls, so as to effectively prevent the leakage of privacy data.

According to the first aspect, a risk assessment method for privacy data leakage is provided. The method includes: obtaining a number of system logs and a number of network traffic records generated by a requesting party requesting to call the privacy data of a target object stored in a service platform; wherein, Each system log is generated based on the request message for calling the API sent by the request to the service platform, and includes a number of first target APIs determined according to the request message, and first parameters input for the number of first target APIs , And several first privacy categories corresponding to the first parameter; each network traffic record includes at least a response message returned by the service platform for the request message. Analyzing the several network traffic records to obtain parsed data, which includes at least several second privacy categories corresponding to the API output data. Obtain from the service platform the permission data of the requester to call the API, the permission data includes the API set that the requester has the right to call, the parameter set composed of the parameters that the API set has the right to pass in, and all The privacy category set corresponding to the parameter set. The plurality of system logs are compared with the authority data to obtain a first comparison result, and the analysis data is compared with the authority data to obtain a second comparison result. Based on at least the first comparison result and the second comparison result, assess the privacy data leakage risk of the requester calling the API.

In one embodiment, obtaining several system logs and several network traffic records generated by the requester requesting to call the privacy data of the target object stored in the service platform includes: obtaining the requestor generated by calling the API provided by the service platform Multiple system logs and multiple network traffic records; based on multiple preset privacy categories, filter the multiple system logs and multiple network traffic records to obtain the multiple system logs and multiple network traffic records.

In a specific embodiment, filtering the multiple system logs and multiple network traffic records to obtain the multiple system logs and multiple network traffic records includes: using the multiple privacy categories to perform A plurality of system logs are matched, and the successfully matched system logs are used as the plurality of system logs; the filter items set based on the plurality of privacy categories in advance are used to filter the plurality of network traffic records from the plurality of network traffic records For traffic records, the form of the filtering item includes at least one of the following: a custom UDF function, a key field, and a regular item.

In one embodiment, the analytic processing of the plurality of network traffic records to obtain analytical data includes: analytic processing of the plurality of network traffic records to obtain the API output data, and the API output data includes multiple Fields; determine several third privacy categories corresponding to several privacy fields in the multiple fields; use the several third privacy categories as the several second privacy categories; or, based on the field values of the several privacy fields, Perform verification processing on the plurality of third privacy categories, and classify the verified third privacy categories into the plurality of second privacy categories.

In a specific embodiment, determining a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields includes: determining, based on a pre-trained natural language processing model, the number of privacy fields corresponding to the plurality of fields Several third privacy categories; or, based on multiple preset regular matching rules, determine several third privacy categories corresponding to several privacy fields in the multiple fields.

In a specific embodiment, the plurality of privacy fields includes any first field corresponding to the first category of the plurality of third privacy categories; wherein, based on the field content of the plurality of privacy fields, the Performing verification processing for the third category includes: matching the first field by using a plurality of pre-stored legal field values corresponding to the first category, and in the case of a successful match, determining the first category Pass the verification; or, use a pre-trained classification model for the first category to classify the first field, and if the classification result indicates that the first field belongs to the first category, determine the The first category passed verification.

In one embodiment, evaluating the privacy data leakage risk of the requester calling API based on at least the first comparison result and the second comparison result includes: comparing the first comparison result with the second comparison result. The results are jointly input into the pre-trained first risk assessment model, and the first prediction result is obtained, indicating the risk of leakage of the privacy data.

In an embodiment, evaluating the privacy data leakage risk of the requester calling API based on at least the first comparison result and the second comparison result includes: according to the several system logs and several network traffic records, Determining an indicator value of a monitoring indicator, the monitoring indicator being preset for the requesting party's API call behavior; comparing the pre-obtained historical indicator value of the requesting party with the indicator value to obtain a third comparison result; Based on the first comparison result, the second comparison result, and the third comparison result, the privacy data leakage risk of the requester calling the API is evaluated.

In a specific embodiment, based on the first comparison result, the second comparison result, and the third comparison result, assessing the privacy data leakage risk of the requester calling the API includes: combining with a preset assessment Rule, according to the first comparison result, the second comparison result, and the third comparison result, determine whether privacy data leakage occurs; or, compare the first comparison result, the second comparison result and the third comparison result The results are jointly input into the pre-trained second risk assessment model, and the second prediction result is obtained, indicating the risk of leakage of the private data.

According to a second aspect, a risk assessment device for privacy data leakage is provided. The device includes: a first acquiring unit configured to acquire a number of system logs and a number of system logs generated by a requester requesting to call the privacy data of a target object stored in a service platform Several network traffic records; among them, each system log is generated based on the request message for calling the API sent by the request to the service platform, and includes a number of first target APIs determined according to the request message, for a number of first The first parameter input by the target API, and several first privacy categories corresponding to the first parameter; each network traffic record includes at least the response message returned by the service platform for the request message. The parsing unit is configured to perform parsing processing on the plurality of network traffic records to obtain parsing data, which includes at least a plurality of second privacy categories corresponding to the API output data. The second obtaining unit is configured to obtain from the service platform the permission data of the requester to call the API, the permission data including the API set that the requester has the right to call, and the parameters that the API set has the right to pass The composed parameter set, and the privacy category set corresponding to the parameter set. The comparison unit is configured to compare the plurality of system logs with the authority data to obtain a first comparison result, and to compare the analysis data with the authority data to obtain a second comparison result . The evaluation unit is configured to evaluate the privacy data leakage risk of the requester calling the API based on at least the first comparison result and the second comparison result.

According to a third aspect, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method of the first aspect.

According to a fourth aspect, there is provided a computing device, including a memory and a processor, the memory stores executable code, and the processor implements the method of the first aspect when the executable code is executed by the processor.

In summary, in the risk assessment method and device for privacy data leakage provided in the embodiments of this specification, the network traffic is performed by obtaining the system log and network traffic record generated by the requester calling API, and the permission data of the requesting party calling API. Analyze the parsed data, compare the parsed data with the permission data, and compare the system log with the permission data. Combine the two comparison results to assess the risk of privacy data leakage caused by the requester's API call, and timely detect, Violations and abnormal calling behaviors of the requesting party were found. Furthermore, the obtained system log and the parsed network traffic record can also be used to determine the indicator value of the monitoring indicator set for the requester’s behavior, and then compare the indicator value with the historical indicator value, thereby further improving the risk assessment Accuracy and availability of results.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the following will briefly introduce the drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can be obtained from these drawings.

Fig. 1 shows a schematic diagram of an implementation scenario of a risk assessment method according to an embodiment.

Fig. 2 shows a flowchart of a risk assessment method for privacy data leakage according to an embodiment.

Fig. 3 shows a structural diagram of a risk assessment device for privacy data leakage according to an embodiment.

Detailed ways

The following describes the solutions provided in this specification with reference to the accompanying drawings.

As mentioned earlier, there is a risk of leaking private data during the current API call. In a scenario where the requesting party is a cross-border requesting party (such as a cross-border merchant), it is particularly urgent to detect the risk of privacy data leakage. Specifically, some large domestic enterprises (such as Alibaba) have expanded their business scope overseas, so there are a large number of overseas merchants, and cross-border data transfer has become the norm. The software and hardware environment and business scenarios of overseas merchants are different from those in China, and the existing data protection architecture will inevitably have inadequacies, resulting in leakage of user privacy data. Furthermore, the IT architectures of different overseas merchants are usually different, resulting in a complex API call system, difficult to sort out, and easy to be used by criminals, resulting in the leakage of private data (such as sensitive data of domestic users).

In addition, due to the large number of APIs and the difficulty of avoiding API development and management vulnerabilities, there may be differences between the data content actually output by the API and the data actually requested by the requester or the data for which the requester has usage rights. For example, an API that a certain requesting party does not have the right to call is illegally called by the certain requesting party due to omissions in API authority management, and outputs the user's sensitive personal information, resulting in leakage of user privacy.

For another example, a requesting party has the right to call a certain API, but its contract data with the service platform only includes part of the data in the full amount of data that can be output by the certain API (such as user gender, user address, and user mobile phone number) Content (such as user gender). However, when the certain requester calls the certain API, in addition to passing in the input parameters corresponding to the part of the data content to the certain API, it also passes in other data content (such as the user address) corresponding to the full amount of data. The input parameters of, due to omissions in API authority management, etc., the data returned by the API to the requester (such as user gender and user address) exceeds the contracted data range (such as user gender).

For another example, the API interface called by the requester, due to some old and unupdated field settings (such as the business personnel splicing the user's mobile phone number and ID number into one field), resulting in the range of data output by the API interface (such as the user's mobile phone number) And the ID number) is inconsistent with the requesting party's contract data range (such as the user's mobile phone number).

Based on the above, the inventor proposes a risk assessment method and device for privacy data leakage. In an embodiment, FIG. 1 shows a schematic diagram of an implementation scenario of a risk assessment method according to an embodiment. As shown in FIG. 1, the requester personnel can send an API call request (or request message) to the service platform through the requester client. ), correspondingly, the service platform can generate a corresponding system log according to the request message, and return an API call response (or response message) to the requesting client. It can be understood that the gateway can record the request message and the response message, and generate a corresponding network traffic record (or called a network traffic log).

Thus, the risk assessment device can obtain system logs and network traffic records from the gateway, and analyze the obtained network traffic records to obtain analytical data; on the other hand, the risk assessment device can also obtain the requester from the service platform and call the API Permission data. Further, the risk assessment device can compare the system log with the permission data, and compare the analytical data with the permission data, and then combine the two comparison results to assess the risk of privacy data leakage caused by the requester calling the API, so as to be timely Detect violations and abnormal calling behaviors of the requesting party.

The following describes the implementation steps of the above risk assessment method in conjunction with specific embodiments.

First, it should be noted that the descriptions in the embodiments of this specification are used for similar terms such as "first", "second", "third", etc., and are only used to distinguish similar things and do not have other limiting effects.

Figure 2 shows a flowchart of a method for risk assessment of privacy data leakage according to an embodiment. The execution subject of the method can be any device or device or platform or server cluster with computing and processing capabilities, for example, the The execution body may be the risk assessment device shown in FIG. 1, for another example, the execution body may also be the above-mentioned service platform.

As shown in FIG. 2, the method may include the following steps S210 to S250.

Step S210: Obtain a number of system logs and a number of network traffic records generated by the requesting party requesting to call the privacy data of the target object stored in the service platform; wherein, each system log is based on the API call sent by the request to the service platform. A request message is generated, and includes, a number of first target APIs determined according to the request message, a first parameter input for the number of first target APIs, and a number of first privacy categories corresponding to the first parameters; each The network traffic record includes at least a response message returned by the service platform in response to the request message. Step S220: Analyze the several network traffic records to obtain parsed data, which includes at least several second privacy categories corresponding to the API output data. Step S230: Obtain the permission data of the requester to call the API from the service platform, the permission data includes the API set that the requester has the right to call, and the parameter set composed of the parameters that the API set has the right to pass in. , And the privacy category set corresponding to the parameter set. Step S240, comparing the plurality of system logs with the authority data to obtain a first comparison result, and comparing the analysis data with the authority data to obtain a second comparison result. Step S250, based on at least the first comparison result and the second comparison result, assess the privacy data leakage risk of the requester calling the API.

The above steps are specifically as follows: First, in step S210, obtain a number of system logs and a number of network traffic records generated by the requesting party requesting to call the privacy data of the target object stored in the service platform.

In an embodiment, the requesting party may be an individual, an organization, or an enterprise, etc., which may log in to the service platform through an account registered in the service platform, and initiate an API call request in the process of using the service platform. In an example, the requestor may be a cross-border merchant, and the service platform may be a cross-border merchant system or a cross-border merchant open platform. It can be understood that the service platform can store basic attribute information for a large number of service objects and service data generated by a large number of service objects in the process of using the service. For example, when the service object registers in the service platform, some registration information will be filled in, or the service object will generate order data and evaluation information when using the service. In the embodiments of this specification, the service object to which the data requested by the requester is targeted is referred to as the target object. In one embodiment, the above-mentioned private data may include the entire amount of data stored in the service platform.

The process of generating system logs and network traffic is introduced below. In one embodiment, the requester may send a request message for calling the API to the service platform. After receiving the request message, the service platform records the business based on the request message, generates the corresponding system log, and generates a response to the request message Message and return the response message to the requester. It can be understood that at the physical layer, the communication between the requester and the service platform will pass through the gateway. Specifically, the request message sent by the requester will be uploaded to the gateway first, and then sent to the service platform through the gateway. During this uplink process, the network The request message can be recorded. In addition, the response message returned by the service platform to the requester will be sent to the gateway first, and then sent to the requester by the gateway. During this downlink process, the gateway can record the response message and record the response message. The request message and the corresponding response message can form a network traffic record.

For the generation of the above system log, the first thing to note is that the service platform stores the configuration information of the API service it can provide. In one embodiment, the configuration information includes the name of each API, the full number of parameters that can be passed in to each API, and the data meaning (mobile phone number) of each parameter in the full number of parameters used to call data (such as 13800001111). Further, after receiving the request message, the service platform can determine the target API included in the request message, the parameters input for the target API, and the meaning of the data corresponding to these parameters according to the stored configuration information, and then generate a system log. It should be noted that in the embodiments of this specification, the meaning of privacy-related data is referred to as privacy category. Specifically, it may include the user's mobile phone number, company switchboard number, ID number, user name, and so on.

As mentioned above, in one embodiment, the above-mentioned private data may include the full amount of data stored in the service platform. In this way, this step may include: obtaining multiple system logs and multiple network traffic records generated by the requester calling the API provided by the service platform, as the above-mentioned several system logs and several network traffic records.

In another embodiment, the risk assessment can be focused on certain privacy categories. Specifically, multiple privacy categories that need to be paid attention to can be preset. Based on this, after obtaining multiple system logs and multiple network traffic records generated by the requester calling API, it is necessary to filter the multiple system logs and multiple network traffic records according to multiple preset privacy categories , To obtain the several system logs and several network traffic records.

In a specific embodiment, the above-mentioned filtering processing may include: using the multiple privacy categories to match the multiple system logs, and use the successfully matched system logs as the plurality of system logs. It can be seen from the above that each system log includes the API determined according to the corresponding request message, the parameters passed into the API by the request, and the meaning of the callable data corresponding to the parameters. In this way, multiple privacy categories can be used to match the data meanings corresponding to the parameters in multiple system logs, so that the data meanings can be matched to the system logs that include any of the multiple privacy categories, and they are included in the above-mentioned several system logs.

In another specific embodiment, the foregoing filtering processing may further include: filtering out the plurality of network traffic records from the plurality of network traffic records by using filtering items set in advance based on the plurality of privacy categories, so The form of the filter item includes at least one of the following: custom UDF function, key field, and regular item. It needs to be understood that the network traffic record includes the request message and the corresponding response message. The data meaning of the fields included in the request message and the response message is often ambiguous, which is different from the system log including the determination from the request message based on the API configuration information Data meaning. Therefore, it is difficult to achieve filtering by using multiple privacy categories to directly match.

The above filtering items can be preset based on multiple privacy categories. In one example, it can include regular items set for mobile phone numbers to match field values with the following characteristics: the first digit is 1, and the first three digits belong to There are network numbers (such as China Mobile network numbers 138, 139, etc.) to classify network traffic records containing the value of this field into the above-mentioned several network traffic records. In an example, it may include a User-Defined Function (UDF) set for the ID number, which is used to match the value of the field that meets the ID number encoding rule, so as to record the network traffic containing the field value Included in the several network traffic records mentioned above. In another example, a key field set for the user’s name may be included. For example, an API parameter used to retrieve the user’s name (such as User_name) may be set as a key field, so that the key field may be included. The network traffic records are classified into the several network traffic records mentioned above.

In the above step S210, several system logs and several network traffic records generated by the requesting party requesting to call the private data of the target object can be obtained.

Next, in step S220, the several network traffic records are parsed to obtain parsed data, which includes at least several second privacy categories corresponding to the API output data.

In one embodiment, this step may include: first parse the several network traffic records to obtain the API output data, and the API output data includes multiple fields. It can be understood that the above-mentioned API output data is obtained by analyzing the response message in the network traffic record. Then, several third privacy categories corresponding to several privacy fields in the multiple fields are determined. Specifically, it can be implemented by means of machine learning, regular matching, etc. In a specific embodiment, a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields may be determined based on a pre-trained natural language processing model. In an example, the natural language processing model may include Transformer, Bert, etc. models. In an example, it can be determined that several privacy fields include Li Qingshen, Sihai Co., Ltd., Beijing Qingnian Road Zhenzhong Building, etc. The corresponding third privacy categories include: user name, company name, address, and so on. In another specific embodiment, a plurality of third privacy categories corresponding to a plurality of privacy fields in the plurality of fields may be determined based on a plurality of preset regular matching rules. In an example, it can be determined that the field named "phone" is a privacy field, and the corresponding third privacy category is a mobile phone number. In another example, it can be determined that the field including "@" and "@" in the field value is a privacy field, and the corresponding third privacy category is an email address. In this way, several third privacy categories can be determined.

Further, in a specific implementation, the above-mentioned several third privacy categories can be used as several second privacy categories. In another specific embodiment, verification processing is performed on the plurality of third privacy categories based on the field values of the plurality of privacy fields, and the third privacy categories that have passed the verification are classified into the plurality of second privacy categories. In an example, the plurality of privacy fields includes any first field corresponding to the first category of the plurality of third privacy categories. Accordingly, the verification process may include: using pre-stored data corresponding to the first category of the third privacy category. For multiple legal field values of a category, the first field is matched, and if the matching is successful, it is determined that the first category has passed the verification. In a specific example, suppose that the first category is the user name and the first field is "European tea". The above multiple legal field values include multiple user names that have been authenticated by real names. Therefore, you can search for multiple user names. Whether there is Oucha, if it exists, the user's name is classified into a number of second privacy categories.

In another example, the above verification processing may further include: using a pre-trained classification model for the first category to classify the first field, and the classification result indicates that the first field belongs to the first field. In the case of the category, it is determined that the first category has passed the verification. In a specific example, assume that the first category is an email address, and the first field is: remember to eat tomorrow, @小花, then the classification result indicates that the first field is not an email address, and then assume that the first field is 58978@ali .cn, the classification result indicates that the first field is an email address, and the email address is classified into several second privacy categories. In this way, on the basis of determining a number of third privacy categories, further verification can be obtained to obtain a number of second privacy categories to ensure the accuracy of the determined second privacy categories, thereby enabling subsequent risk assessments for privacy data leakage. The result is more accurate.

Above, several second privacy categories corresponding to the API output data included in the response message can be obtained. On the other hand, optionally, the request message included in the network traffic record can also be parsed. It should be noted that the generation of the above system log is implemented at the application layer, and the generation of network traffic records is at the bottom layer. In terms of engineering implementation, it is difficult to obtain the complete API stored in the above service platform to analyze the network traffic records. The configuration information is accurately analyzed. Therefore, other analytical methods often need to be considered. In an embodiment, the analysis data further includes several second target APIs obtained by parsing the request message and second parameters input for the several second target APIs. The API and parameters parsed here are less accurate and relatively rough compared to the API names and parameters included in the system log.

In a specific embodiment, API parsing rules set based on multiple APIs in advance can be used to parse the plurality of second target APIs from the plurality of network traffic records, and the API parsing rules pass at least one of the following A formal definition: custom UDF functions, key fields and regular items. In another specific embodiment, a parameter parsing rule set based on a plurality of parameters in advance can be used to parse the plurality of second parameters from the plurality of network traffic records, and the parameter parsing rule may pass at least one of the following A formal definition: custom UDF functions, key fields and regular items. It should be noted that, for the custom UDF functions, key fields, and regular items involved in the above-mentioned API parsing rules and parameter parsing rules, please refer to the relevant description of the filtering items in the foregoing embodiments, which will not be repeated here.

Above, analyze several network traffic records to obtain analytical data. On the other hand, step S230 may be performed to obtain the requester's permission data for calling the API from the service platform.

Specifically, the above-mentioned permission data includes the API set that the requester has the right to call, the parameter set composed of the parameters that the API set has the right to pass in, and the privacy category set corresponding to the parameter set. In an example, the API set may include the names of one or more APIs, such as http://yiteng.cn/data/? id=91, https://niuqi.cn/data/? id=8 and so on. In an example, the parameters in the parameter set may include gender, phone and add. In an example, the privacy categories in the privacy category set may include gender, phone number, and address.

In one embodiment, the above-mentioned service platform includes a user authorization system, a contract system, an API management system, and the like. It should be understood that the user authorization system can store part of the private data that individual users or enterprise users are authorized to allow the service platform to provide externally. The contracting system can store the data range that the requesting party can request from the service platform that the requesting party negotiates with the service platform. The API management system includes information such as API interface documents that the service platform can provide to the requester to call. Based on this, relevant data can be obtained separately from these systems, and then classified into the above-mentioned authority data after sorting.

In this way, the permission data of the requester to call the API can be obtained from the service platform.

Then, in step S240, a number of system logs are compared with the authority data to obtain a first comparison result, and the analysis data is compared with the authority data to obtain a second comparison result.

On the one hand, in an embodiment, obtaining the first comparison result described above may include: determining whether the plurality of first target APIs belong to the API set, and obtaining a first determination result, which is included in the first comparison result . It needs to be understood that for several first target APIs included in each system log in several system logs, it is necessary to determine whether they belong to the API set in the permission data. In a specific embodiment, it is assumed that the target APIs of several system logs include http://user.cn/data/? id=00, the above API set includes http://user.cn/data/? id=00 and http://company.cn/data/? id=66. Through comparison, it can be determined that the target APIs in several system logs belong to the API set, and the number that does not belong to the API set is 0, so the first judgment result can be determined to be 0.

In another embodiment, obtaining the first comparison result described above may further include: judging whether the first parameter belongs to the parameter set, and obtaining a second judgment result, which is included in the first comparison result. It needs to be understood that for the first parameter included in each system log in several system logs, it is necessary to determine whether it belongs to the parameter set in the permission data. In an example, it is assumed that the parameters in the above-mentioned several system logs include phone and IDnumber, and the above-mentioned parameter set includes phone. Through comparison, it can be determined that IDnumber does not belong to the parameter set, and thus the second judgment result can be determined as 1.

In yet another embodiment, it may further include: judging whether the several first privacy categories belong to the privacy category set, and obtaining a third judgment result, which is included in the first comparison result. It needs to be understood that for several first privacy categories included in each system log in several system logs, it is necessary to determine whether they belong to the privacy category set in the permission data. In an example, suppose that the third privacy category in the above several system logs includes mobile phone number and ID number, and the above privacy category set includes mobile phone number. Through comparison, it can be determined that the identity card number does not belong to the privacy category set. Determine the privacy category comparison result as 1.

From the above, the first judgment result, the second judgment result, and the third judgment result can be obtained as the first comparison result.

On the other hand, in one embodiment, obtaining the second comparison result above may include: determining whether the plurality of second privacy categories belong to the privacy category set, obtaining a fourth determination result, and categorizing it into the second comparison result. The result. In another embodiment, it may further include: judging whether the plurality of second target APIs belong to the API set, and obtaining a fifth judgment result, which is included in the second comparison result. In yet another embodiment, it may further include: judging whether the second parameter belongs to the parameter set, and obtaining a sixth judgment result, which is included in the second comparison result.

Above, the first comparison result and the second comparison result can be obtained. Next, in step S250, based on at least the first comparison result and the second comparison result, the privacy data leakage risk of the requester calling the API is evaluated.

In an embodiment, this step may include: inputting the first comparison result and the second comparison result into a pre-trained first risk assessment model to obtain a first prediction result, indicating that the privacy data is leaked risk. In a more specific embodiment, the first risk assessment model may use machine learning algorithms such as decision trees, random forests, adboost, neural networks, etc. In a more specific embodiment, the first prediction result may be a risk classification level, such as high, medium, and low. In another more specific embodiment, the first prediction result may be a risk assessment score, such as 20 or 85. It should be noted that the use process of the first risk assessment model is similar to the training process, so the training process will not be repeated.

In another embodiment, this step may include: firstly, according to the several system logs and several network traffic records, determining the index value of the monitoring index, the monitoring index being preset for the requesting party's API call behavior; then , Comparing the pre-obtained historical index value of the requesting party with the index value to obtain a third comparison result; then, based on the first comparison result, the second comparison result, and the third comparison result As a result, the privacy data leakage risk of the requester calling the API is evaluated.

In a specific embodiment, the above-mentioned monitoring indicators may include one or more of the following: the number of request messages sent by the requester to the service platform in a unit time, and the private data requested by the requesting party in a unit time. The number of corresponding target objects, the number of privacy categories corresponding to the privacy data requested by the requester in a unit time. In an example, the unit time can be yearly, monthly, weekly, daily, hourly, every minute, and so on. In a specific example, the monitoring indicator may include the number of user IDs (which can be parsed from the input parameters of the request message) included in the daily call request of the requesting party.

In a specific embodiment, the aforementioned historical indicator value may be determined based on historical system logs and historical network traffic records generated by the requesting party's invoking privacy data. In an example, the monitoring index may include the number of request messages sent by the requesting party per minute. Assuming that the historical index value for this number is 20, and the current determined index value is determined to be 100, it can be 4((100-20)/20) determines the comparison result for this number and belongs to the third comparison result mentioned above.

In a specific embodiment, a preset evaluation rule may be combined to determine whether privacy data leakage occurs based on the first comparison result, the second comparison result, and the third comparison result. In an example, the evaluation rule may include: if the privacy category that exceeds the permission range in the comparison result includes the user ID number, it is determined that the requesting party's API call sends privacy data leakage. In another specific embodiment, the first comparison result, the second comparison result, and the third comparison result may be jointly input into a pre-trained second risk assessment model to obtain a second prediction result, indicating that all Describe the risk of privacy data leakage. In a more specific embodiment, the second risk assessment model may use machine learning algorithms such as decision trees, random forests, adboost, and neural networks. In a more specific embodiment, the second prediction result may be a risk classification level, such as extremely high, high, medium, low, extremely low, and so on. In another more specific embodiment, the second prediction result may be a risk assessment score, such as 15 or 90. It should be noted that the use process of the second risk assessment model is similar to the training process, so the training process will not be repeated. In this way, based on the above three comparison results, the risk of data leakage invoked by the requester can be evaluated.

To sum up, in the risk assessment method for privacy data leakage provided by the embodiment of this specification, the system log and network traffic record generated by the requester calling the API, and the permission data of the requesting party calling the API are obtained by analyzing the network traffic. Analyze the data, then compare the parsed data with the permission data, and compare the system log with the permission data. Combine the two comparison results to evaluate the risk of privacy data leakage caused by the requester's API call, and detect and discover the request in a timely manner Party’s violations and abnormal calling behaviors. Furthermore, the obtained system log and the parsed network traffic record can also be used to determine the indicator value of the monitoring indicator set for the requester’s behavior, and then compare the indicator value with the historical indicator value, thereby further improving the risk assessment Accuracy and availability of results.

According to another embodiment, this specification also discloses an evaluation device. Specifically, FIG. 3 shows a structural diagram of a risk assessment device for privacy data leakage according to an embodiment. As shown in FIG. 3, the device 300 may include the following units.

The first obtaining unit 310 is configured to obtain a number of system logs and a number of network traffic records generated by the requestor requesting to call the privacy data of the target object stored in the service platform; wherein, each system log is based on the request to the service platform The issued request message for calling the API is generated and includes a number of first target APIs determined according to the request message, first parameters input for the number of first target APIs, and a number of first parameters corresponding to the first parameters. Privacy category; each network traffic record includes at least the response message returned by the service platform for the request message. The parsing unit 320 is configured to perform parsing processing on the plurality of network traffic records to obtain parsing data, which includes at least a plurality of second privacy categories corresponding to the API output data. The second obtaining unit 330 is configured to obtain from the service platform the permission data of the requester to call the API, the permission data includes the API set that the requester has the right to call, and the API set has the right to pass in A parameter set composed of parameters, and a privacy category set corresponding to the parameter set. The comparison unit 340 is configured to compare the plurality of system logs with the authority data to obtain a first comparison result, and to compare the analysis data with the authority data to obtain a second comparison result. The evaluation unit 350 is configured to evaluate the privacy data leakage risk of the requester calling the API based on at least the first comparison result and the second comparison result.

In an embodiment, the first obtaining unit 310 specifically includes: an obtaining subunit 311, configured to obtain multiple system logs and multiple network traffic records generated by the requester calling an API provided by the service platform; and a filtering subunit 312 , It is configured to filter the multiple system logs and multiple network traffic records based on multiple preset privacy categories to obtain the multiple system logs and multiple network traffic records.

In a specific embodiment, the filtering subunit 312 is specifically configured to: use the multiple privacy categories to match the multiple system logs, and use the successfully matched system logs as the plurality of system logs; Filtering out the plurality of network traffic records from the plurality of network traffic records based on the filtering items set in advance based on the multiple privacy categories, and the form of the filtering items includes at least one of the following: custom UDF function , Key fields and regular items.

In an embodiment, the network traffic record further includes the request message, and the analysis data further includes several second target APIs obtained by parsing the request message and second parameters input for the several second target APIs .

In a specific embodiment, the parsing unit 320 is further configured to parse the plurality of second target APIs from the plurality of network traffic records by using API parsing rules set based on a plurality of APIs in advance, and the API The parsing rules are defined in at least one of the following forms: custom UDF functions, key fields, and regular items; using parameter parsing rules set in advance based on multiple parameters to parse the number of network traffic records The second parameter, the parameter parsing rule is defined by at least one of the following forms: a custom UDF function, a key field, and a regular item.

In one embodiment, the parsing unit 320 specifically includes: a parsing subunit 321 configured to perform parsing processing on the plurality of network traffic records to obtain the API output data, and the API output data includes multiple fields; The determining subunit 322 is configured to determine several third privacy categories corresponding to several privacy fields in the multiple fields; the parsing unit specifically further includes: a subunit 323 configured to use the several third privacy categories as The plurality of second privacy categories; or the verification subunit 324 is configured to perform verification processing on the plurality of third privacy categories based on the field values of the plurality of privacy fields, and include the third privacy categories that have passed the verification into all Describe several second privacy categories.

In a specific embodiment, the determining subunit 322 is specifically configured to: determine a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields based on a pre-trained natural language processing model; or, based on a preset A plurality of predetermined regular matching rules are determined to determine a plurality of third privacy categories corresponding to a plurality of privacy fields in the plurality of fields.

In another specific embodiment, the plurality of privacy fields includes any first field corresponding to the first category of the plurality of third privacy categories; wherein the verification subunit 324 is specifically configured to: use a pre-stored corresponding Match the first field on the multiple legal field values of the first category, and if the matching is successful, determine that the first category has passed the verification; or, use a pre-trained target for the first The classification model of the category classifies the first field, and when the classification result indicates that the first field belongs to the first category, it is determined that the first category passes the verification.

In one embodiment, the comparison unit 340 is specifically configured to: determine whether the plurality of first target APIs belong to the API set, obtain a first determination result, and classify it into the first comparison result; determine Whether the first parameter belongs to the set of parameters, the second judgment result is obtained, and it is classified into the first comparison result; whether the plurality of first privacy categories belong to the set of privacy classifications is judged, and the third judgment result is obtained, which is classified into The first comparison result; it is determined whether the several second privacy categories belong to the privacy category set, and a fourth determination result is obtained, which is included in the second comparison result.

In an embodiment, the comparison unit 340 is further configured to: determine whether the plurality of second privacy categories belong to the privacy category set, obtain a fourth judgment result, and classify it into the second comparison result; Whether the plurality of second target APIs belong to the API set is obtained, and the fifth judgment result is obtained, and is classified into the second comparison result; whether the second parameter belongs to the parameter set is judged, and the sixth judgment result is obtained, which is classified into The second comparison result.

In one embodiment, the evaluation unit 350 is specifically configured to: input the first comparison result and the second comparison result into a pre-trained first risk assessment model to obtain a first prediction result, and instruct the Risk of privacy data leakage.

In one embodiment, the evaluation unit 350 specifically includes: a processing subunit 351 configured to determine an indicator value of a monitoring indicator based on the number of system logs and a number of network traffic records, the monitoring indicator being specific to the requesting party’s API call behavior And preset; the comparison subunit 352 is configured to compare the pre-acquired historical index value of the requester with the index value to obtain a third comparison result; the evaluation subunit 353 is configured to be based on all The first comparison result, the second comparison result, and the third comparison result are used to evaluate the privacy data leakage risk of the requester calling the API.

In a specific embodiment, the monitoring indicators include one or more of the following: the number of request messages sent by the requester to the service platform in a unit time, and the private data requested by the requester to call in a unit time The number of corresponding target objects, and the number of privacy categories corresponding to the privacy data requested by the requester in a unit time.

In another specific embodiment, the evaluation sub-unit 353 is specifically configured to: in combination with a preset evaluation rule, according to the first comparison result, the second comparison result, and the third comparison result, determine whether Leakage of privacy data occurs; or, the first comparison result, the second comparison result, and the third comparison result are jointly input into a pre-trained second risk assessment model to obtain a second prediction result, indicating the privacy data Risk of leakage.

To sum up, in the risk assessment device for privacy data leakage provided in the embodiment of this specification, the system log and network traffic record generated by the requester calling the API are obtained, and the permission data of the requesting party calling the API is obtained by analyzing the network traffic. Analyze the data, then compare the parsed data with the permission data, and compare the system log with the permission data. Combine the two comparison results to evaluate the risk of privacy data leakage caused by the requester's API call, and detect and discover the request in a timely manner Party’s violations and abnormal calling behaviors. Furthermore, the obtained system log and the parsed network traffic record can also be used to determine the indicator value of the monitoring indicator set for the requester’s behavior, and then compare the indicator value with the historical indicator value, thereby further improving the risk assessment Accuracy and availability of results.

According to another embodiment, there is also provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method described in conjunction with FIG. 2.

According to an embodiment of still another aspect, there is also provided a computing device, including a memory and a processor, the memory is stored with executable code, and when the processor executes the executable code, it implements the method described in conjunction with FIG. 2 method.

Those skilled in the art should be aware that, in one or more of the foregoing examples, the functions described in the present invention can be implemented by hardware, software, firmware, or any combination thereof. When implemented by software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on the computer-readable medium.

The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention. The protection scope, any modification, equivalent replacement, improvement, etc. made on the basis of the technical solution of the present invention shall be included in the protection scope of the present invention.

Claims

A risk assessment method for private data leakage, including:

Obtain a number of system logs and a number of network traffic records generated by the requesting party requesting to call the privacy data of the target object stored in the service platform; wherein, each system log is based on the request message for calling the API sent by the request to the service platform Generate and include, several first target APIs determined according to the request message, first parameters input for the several first target APIs, and several first privacy categories corresponding to the first parameters; each network traffic record Includes at least the response message returned by the service platform in response to the request message;

Analyzing the several network traffic records to obtain parsed data, which includes at least several second privacy categories corresponding to the API output data;

Obtain from the service platform the permission data of the requester to call the API, the permission data includes the API set that the requester has the right to call, the parameter set composed of the parameters that the API set has the right to pass in, and all The privacy category set corresponding to the parameter set;

Comparing the plurality of system logs with the authority data to obtain a first comparison result, and comparing the analysis data with the authority data to obtain a second comparison result;

Based on at least the first comparison result and the second comparison result, assess the privacy data leakage risk of the requester calling the API.
The method according to claim 1, wherein obtaining a number of system logs and a number of network traffic records generated by the requesting party requesting to call the privacy data of the target object stored in the service platform includes:

Acquiring multiple system logs and multiple network traffic records generated by the requester calling the API provided by the service platform;

Based on multiple preset privacy categories, filtering the multiple system logs and multiple network traffic records to obtain the multiple system logs and multiple network traffic records.
The method according to claim 2, wherein filtering the multiple system logs and multiple network traffic records to obtain the multiple system logs and multiple network traffic records includes:

Use the multiple privacy categories to match the multiple system logs, and use the successfully matched system logs as the multiple system logs;

The filter items set based on the plurality of privacy categories in advance are used to filter the plurality of network traffic records from the plurality of network traffic records, and the form of the filter items includes at least one of the following: custom UDF Functions, key fields and regular items.
The method according to claim 1, wherein the network traffic record further includes the request message, and the analysis data further includes a number of second target APIs obtained by parsing the request message and a number of second target APIs. Enter the second parameter.
The method according to claim 4, wherein performing the analysis processing on the plurality of network traffic records to obtain analysis data comprises:

The plurality of second target APIs are parsed from the plurality of network traffic records using API parsing rules set in advance based on multiple APIs, and the API parsing rules are defined in at least one of the following forms: custom UDF function , Key fields and regular items;

The plurality of second parameters are parsed from the plurality of network traffic records by using parameter parsing rules set in advance based on a plurality of parameters, and the parameter parsing rules are defined in at least one of the following forms: custom UDF functions, Key fields and regular items.
The method according to claim 1, wherein the parsing process on the plurality of network traffic records to obtain parsing data comprises:

Analyzing the several network traffic records to obtain the API output data, and the API output data includes a plurality of fields;

Determining a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields;

Use the plurality of third privacy categories as the plurality of second privacy categories; or,

Based on the field values of the plurality of privacy fields, the plurality of third privacy categories are verified, and the third privacy categories that have passed the verification are classified into the plurality of second privacy categories.
7. The method according to claim 6, wherein determining a plurality of third privacy categories corresponding to a plurality of privacy fields in the plurality of fields comprises:

Based on a pre-trained natural language processing model, determine a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields; or,

Based on a plurality of preset regular matching rules, a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields are determined.
The method according to claim 6, wherein the plurality of privacy fields includes any first field corresponding to the first category of the plurality of third privacy categories; wherein based on the field content of the plurality of privacy fields, The verification processing of the several third categories includes:

Use pre-stored multiple legal field values corresponding to the first category to match the first field, and in the case of a successful match, determine that the first category passes the verification; or,

Use a pre-trained classification model for the first category to classify the first field, and if the classification result indicates that the first field belongs to the first category, determine that the first category passes the verification .
The method according to claim 1, wherein comparing the plurality of system logs with the permission data to obtain the first comparison result comprises:

Determine whether the plurality of first target APIs belong to the API set, obtain a first determination result, and classify it as the first comparison result;

Judging whether the first parameter belongs to the parameter set, and obtaining a second judgment result, which is included in the first comparison result;

Judging whether the several first privacy categories belong to the privacy category set, obtaining a third judgment result, and categorizing it as the first comparison result;

The comparison between the analysis data and the authorization data to obtain a second comparison result includes:

It is determined whether the plurality of second privacy categories belong to the privacy category set, and a fourth determination result is obtained, which is included in the second comparison result.
The method according to claim 4, wherein comparing the parsed data with the permission data to obtain a second comparison result comprises:

Judging whether the plurality of second privacy categories belong to the privacy category set, obtaining a fourth judgment result, and categorizing it as the second comparison result;

Determine whether the plurality of second target APIs belong to the API set, obtain a fifth determination result, and classify it into the second comparison result;

It is judged whether the second parameter belongs to the parameter set, and a sixth judgment result is obtained, which is included in the second comparison result.
The method according to claim 1, wherein, based on at least the first comparison result and the second comparison result, evaluating the privacy data leakage risk of the requester calling the API comprises:

The first comparison result and the second comparison result are jointly input into a pre-trained first risk assessment model to obtain a first prediction result, indicating the risk of leakage of the privacy data.
The method according to claim 1, wherein, based on at least the first comparison result and the second comparison result, evaluating the privacy data leakage risk of the requester calling the API comprises:

According to the several system logs and several network traffic records, determine the index value of the monitoring index, the monitoring index is preset for the requesting party's API call behavior;

Comparing the pre-obtained historical index value of the requesting party with the index value to obtain a third comparison result;

Based on the first comparison result, the second comparison result, and the third comparison result, assess the privacy data leakage risk of the requester calling the API.
The method according to claim 12, wherein the monitoring indicators include one or more of the following: the number of request messages sent by the requester to the service platform in a unit time, and the requester requests to call The number of target objects corresponding to the private data, and the number of privacy categories corresponding to the private data requested by the requesting party in a unit time.
The method according to claim 12, wherein, based on the first comparison result, the second comparison result, and the third comparison result, evaluating the privacy data leakage risk of the requester calling the API comprises:

Combining with preset evaluation rules, determine whether privacy data leakage occurs according to the first comparison result, the second comparison result, and the third comparison result; or,

The first comparison result, the second comparison result, and the third comparison result are jointly input into a pre-trained second risk assessment model to obtain a second prediction result, indicating the risk of leakage of the private data.
A risk assessment device for private data leakage, including:

The first obtaining unit is configured to obtain a number of system logs and a number of network traffic records generated by the requesting party requesting to call the privacy data of the target object stored in the service platform; wherein, each system log is sent to the service platform based on the request The request message for calling the API is generated, and includes a number of first target APIs determined according to the request message, first parameters entered for the number of first target APIs, and a number of first privacy corresponding to the first parameters Category; each network traffic record includes at least the response message returned by the service platform for the request message;

The parsing unit is configured to perform parsing processing on the plurality of network traffic records to obtain parsing data, which includes at least a plurality of second privacy categories corresponding to the API output data;

The second obtaining unit is configured to obtain from the service platform the permission data of the requester to call the API, the permission data includes the API set that the requester has the right to call, and the parameters that the API set has the right to pass in The composed parameter set, and the privacy category set corresponding to the parameter set;

The comparison unit is configured to compare the plurality of system logs with the authority data to obtain a first comparison result, and to compare the analysis data with the authority data to obtain a second comparison result ；

The evaluation unit is configured to evaluate the privacy data leakage risk of the requester calling the API based on at least the first comparison result and the second comparison result.
The device according to claim 15, wherein the first obtaining unit specifically comprises:

The obtaining subunit is configured to obtain multiple system logs and multiple network traffic records generated by the requester calling the API provided by the service platform;

The filtering subunit is configured to filter the multiple system logs and multiple network traffic records based on multiple preset privacy categories to obtain the multiple system logs and multiple network traffic records.
The device according to claim 16, wherein the filtering subunit is specifically configured as:

Use the multiple privacy categories to match the multiple system logs, and use the successfully matched system logs as the multiple system logs;

The filter items set based on the plurality of privacy categories in advance are used to filter the plurality of network traffic records from the plurality of network traffic records, and the form of the filter items includes at least one of the following: custom UDF Functions, key fields and regular items.
The apparatus according to claim 15, wherein the network traffic record further includes the request message, and the analysis data further includes a number of second target APIs obtained by parsing the request message and a number of second target APIs. Enter the second parameter.
The device according to claim 18, wherein the parsing unit is further configured to:

The plurality of second target APIs are parsed from the plurality of network traffic records using API parsing rules set in advance based on multiple APIs, and the API parsing rules are defined in at least one of the following forms: custom UDF function , Key fields and regular items;

The plurality of second parameters are parsed from the plurality of network traffic records by using parameter parsing rules set in advance based on a plurality of parameters, and the parameter parsing rules are defined in at least one of the following forms: custom UDF functions, Key fields and regular items.
The device according to claim 15, wherein the analyzing unit specifically comprises:

A parsing subunit, configured to perform parsing processing on the several network traffic records to obtain the API output data, and the API output data includes a plurality of fields;

A determining subunit, configured to determine a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields;

The parsing unit specifically further includes: a classification subunit configured to use the plurality of third privacy categories as the plurality of second privacy categories; or a verification subunit configured to be based on the field values of the plurality of privacy fields, Perform verification processing on the plurality of third privacy categories, and classify the verified third privacy categories into the plurality of second privacy categories.
The device according to claim 20, wherein the determining subunit is specifically configured as:

Based on a pre-trained natural language processing model, determine a number of third privacy categories corresponding to a number of privacy fields in the plurality of fields; or,

Based on multiple preset regular matching rules, several third privacy categories corresponding to several privacy fields in the multiple fields are determined.
The apparatus according to claim 20, wherein the plurality of privacy fields includes any first field corresponding to the first category of the plurality of third privacy categories; wherein the verification subunit is specifically configured as:

Use pre-stored multiple legal field values corresponding to the first category to match the first field, and in the case of a successful match, determine that the first category passes the verification; or,

Use a pre-trained classification model for the first category to classify the first field, and if the classification result indicates that the first field belongs to the first category, determine that the first category passes the verification .
The device according to claim 15, wherein the comparison unit is specifically configured to:

Determine whether the plurality of first target APIs belong to the API set, obtain a first determination result, and classify it as the first comparison result;

Judging whether the first parameter belongs to the parameter set, and obtaining a second judgment result, which is included in the first comparison result;

Judging whether the several first privacy categories belong to the privacy category set, obtaining a third judgment result, and categorizing it as the first comparison result;

It is determined whether the plurality of second privacy categories belong to the privacy category set, and a fourth determination result is obtained, which is included in the second comparison result.
The device according to claim 18, wherein the comparison unit is further configured to:

Judging whether the plurality of second privacy categories belong to the privacy category set, obtaining a fourth judgment result, and categorizing it as the second comparison result;

Determine whether the plurality of second target APIs belong to the API set, obtain a fifth determination result, and classify it into the second comparison result;

It is judged whether the second parameter belongs to the parameter set, and a sixth judgment result is obtained, which is included in the second comparison result.
The device according to claim 15, wherein the evaluation unit is specifically configured to:

The first comparison result and the second comparison result are jointly input into a pre-trained first risk assessment model to obtain a first prediction result, indicating the risk of leakage of the privacy data.
The device according to claim 15, wherein the evaluation unit specifically comprises:

The processing subunit is configured to determine the indicator value of the monitoring indicator according to the several system logs and several network traffic records, the monitoring indicator being preset for the requesting party's API call behavior;

The comparison subunit is configured to compare the historical index value of the requester obtained in advance with the index value to obtain a third comparison result;

The evaluation subunit is configured to evaluate the privacy data leakage risk of the requester calling the API based on the first comparison result, the second comparison result, and the third comparison result.
The device according to claim 26, wherein the monitoring indicators include one or more of the following: the number of request messages sent by the requester to the service platform in a unit time, and the requester requests to call The number of target objects corresponding to the privacy data, and the number of privacy categories corresponding to the privacy data requested by the requester in a unit time.
The device according to claim 26, wherein the evaluation subunit is specifically configured as:

Combining with preset evaluation rules, determine whether privacy data leakage occurs according to the first comparison result, the second comparison result, and the third comparison result; or,

The first comparison result, the second comparison result, and the third comparison result are jointly input into a pre-trained second risk assessment model to obtain a second prediction result, indicating the risk of leakage of the private data.
A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1-14.
A computing device, comprising a memory and a processor, wherein executable code is stored in the memory, and when the processor executes the executable code, the method according to any one of claims 1-14 is implemented.