Private data detection method and device on application

Abstract

The invention discloses a method and a device for detecting private data on an application, relates to the technical field of information security and can also be used in the financial field, and the method comprises the following steps: acquiring modification content of a source code of a root container of a cluster of a to-be-detected application; modifying the source code according to the modification content to generate a new root container mirror image; running the new root container mirror image, and obtaining a message of communication between containers in the cluster; inputting the message into a pre-trained privacy data detection model, and outputting privacy data of the to-be-detected application; the private data detection model is obtained by training a neural network model by using a deep learning algorithm, a historical message and private data in the historical message; according to the method, the privacy data compliance detection can be automatically carried out on the applications deployed by the container on a large scale, the detection difficulty is reduced, the problem that application encryption needs to be overcome in the traditional detection process is avoided, and the detection accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and can also be used in the financial field, and particularly relates to a method and device for detecting privacy data in applications. Background Art

[0002] This section aims to provide background or context for the embodiments of the present invention stated in the claims. The description herein is not admitted to be prior art merely because it is included in this section.

[0003] Privacy protection means that information that individuals or collectives and other entities do not want to be known by outsiders is properly protected. Privacy protection focuses on ensuring that organizations collect, store, transmit, and process personal data in compliance. Currently, many applications on the market have problems of collecting and storing user data information. Regulatory agencies have the responsibility to detect whether applications illegally collect and store user information. The prior art provides the front-end and back-end source codes of applications for developers. The learning cost of building a deployment environment for regulatory agencies to detect whether applications illegally collect and store user information is very high. And there are also problems that large-scale detection cannot be achieved through manual privacy compliance testing, and dynamic automated detection is difficult due to the non-uniform network communication libraries of applications. In-application detection also needs to overcome problems such as application encryption. Summary of the Invention

[0004] Embodiments of the present invention provide a method for detecting privacy data in applications, which is used to achieve large-scale and automated compliance detection of privacy data for applications deployed in containers, reduce the detection difficulty, bypass the problem of application encryption that needs to be overcome in the traditional detection process, and improve the detection accuracy. The method includes:

[0005] Obtain the modified content of the source code of the root container of the cluster of the application to be detected;

[0006] Modify the source code according to the modified content to generate a new root container image;

[0007] Run the new root container image to obtain the messages of communication between containers in the cluster;

[0008] Input the messages into a pre-trained privacy data detection model to output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using deep learning algorithms, historical messages, and privacy data in the historical messages;

[0009] Display the privacy data of the application to be detected.

[0010] An embodiment of the present invention further provides a privacy data detection device for applications, which is used to achieve large-scale and automated compliance detection of privacy data for applications deployed in containers, reduce the detection difficulty, bypass the problem of application encryption that needs to be overcome in the traditional detection process, and improve the detection accuracy. The device includes:

[0011] An acquisition module, configured to acquire the modified content of the source code of the root container of the cluster of the application to be detected;

[0012] A modification module, configured to modify the source code according to the modified content to generate a new root container image;

[0013] A running module, configured to run the new root container image to acquire the messages of communication between containers in the cluster;

[0014] An output module, configured to input the messages into a pre-trained privacy data detection model and output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using deep learning algorithms, historical messages, and privacy data in the historical messages;

[0015] A display module, configured to display the privacy data of the application to be detected.

[0016] An embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned privacy data detection method for applications is implemented.

[0017] An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned privacy data detection method for applications is implemented.

[0018] An embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned privacy data detection method for applications is implemented.

[0019] Compared with the existing privacy data detection solutions, the privacy data detection method and device for applications provided by the embodiments of the present invention obtain the modified content of the source code of the root container of the cluster of the application to be detected; modify the source code according to the modified content to generate a new root container image; run the new root container image to obtain the messages of the communication between containers in the cluster; input the messages into a pre-trained privacy data detection model to output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using deep learning algorithms, historical messages, and privacy data in the historical messages; and display the privacy data of the application to be detected, which can realize large-scale and automated compliance detection of privacy data for applications deployed in containers, reduce the detection difficulty, bypass the problem of application encryption that needs to be overcome in the traditional detection process, and improve the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings:

[0021] Figure 1 It is a flowchart of the privacy data detection method for applications in the embodiments of the present invention;

[0022] Figure 2 It is a flowchart of a specific example of the privacy data detection method for applications in the embodiments of the present invention;

[0023] Figure 3 It is a schematic diagram of the root container in the embodiments of the present invention;

[0024] Figure 4 It is a structural block diagram of the privacy data detection device for applications in the embodiments of the present invention;

[0025] Figure 5 It is a schematic diagram of the physical structure of the electronic device provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following further elaborates on the embodiments of the present invention with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.

[0027] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. Moreover, for the processing of relevant data such as collection, storage, use, processing, transmission, provision, disclosure, and application, all comply with the relevant laws, regulations, and standards of the relevant countries and regions. Necessary confidentiality measures are taken, which do not violate public order and good customs, and corresponding operation entrances are provided for users to choose to authorize or reject.

[0028] Provide corresponding operation entrances for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, then enter the expert decision-making process.

[0029] With the continuous progress of virtualization and container technologies, more and more applications choose to use containers for distribution and deployment. The feature of being easy to deploy also provides a solution to the above problems. Developers provide packaged container images and yaml files for constructing containers. The regulatory agency can build a container runtime and perform dynamic detection on it. Embodiments of the present invention provide a method for detecting private data in applications. The private data detection method is a method for detecting whether the data sent and received by applications distributed based on containers contains personal privacy information. Embodiments of the present invention mainly rewrite the root container (pause container) of k8s to complete general privacy compliance detection of applications within containers.

[0030] In order to achieve large-scale and automated privacy data compliance detection for applications deployed in containers, reduce the detection difficulty, bypass the problem of application encryption that needs to be overcome in the traditional detection process, and improve the detection accuracy, embodiments of the present invention provide a method for detecting private data in applications. Figure 1 It is a flowchart of the method for detecting private data in applications in embodiments of the present invention, as Figure 1 shown, this method may include:

[0031] Step 101, obtain the modified content of the source code of the root container of the cluster of the application to be detected;

[0032] Step 102, modify the source code according to the modified content to generate a new root container image;

[0033] Step 103, run the new root container image to obtain the messages for communication between containers in the cluster;

[0034] Step 104, input the messages into a pre-trained private data detection model to output the private data of the application to be detected; the private data detection model is obtained by training a neural network model using deep learning algorithms, historical messages, and private data in historical messages;

[0035] Step 105, display the private data of the application to be detected.

[0036] A dynamic and automated solution for detecting whether there are privacy protection issues in applications in containers proposed by an embodiment of the present invention solves the problem that large-scale detection cannot be achieved through manual privacy compliance testing for applications and solves the problem of difficult dynamic and automated detection caused by inconsistent network communication libraries in containers.

[0037] The root container, namely the Pause container, also called the Infra container, is a container that is secretly started during the operation of k8s. Its image is very small and it is always in the Pause state. The Pause container is born to solve the network problems in the cluster (Pod). Figure 3 It is a schematic diagram of the root container in an embodiment of the present invention. As Figure 3 described, the Pause container is responsible for creating and maintaining the network namespace of each Pod. In other words, there is a Pause container in each Pod, enabling the containers within the network namespace to communicate with each other. When a Pod is started, k8s will first start a Pause container, and then let all the containers in the Pod join the network namespace of this container (network namespace).

[0038] For real-time detection of privacy data in applications, it can be generally divided into three parts:

[0039] 1. Data collection: Collect data within the container by packet capture, and the data sent by the established connections such as message queues and databases can be captured.

[0040] 2. Data analysis: Analyze the data collected in the first step to detect whether there is personal privacy information, such as ID numbers, mobile phone numbers, etc.

[0041] 3. User interface (deployed separately, not in the Pause container): Provide a friendly interface for users to view the detection reports.

[0042] In one embodiment, in order to achieve large-scale and automated security and privacy compliance detection for applications deployed in containers, obtaining the modified content of the source code of the root container of the cluster of the application to be detected may include: obtaining the code for starting a subprocess, specifying a capture interface, obtaining the packets of the communication between the capture interface and other interfaces, setting multiple files according to the packet type, and storing the packets in the corresponding files; according to the modified content, modifying the source code to generate a new root container image may include: adding the code for starting a subprocess, specifying a capture interface, obtaining the packets of the communication between the capture interface and other interfaces, setting multiple files according to the packet type, and storing the packets in the corresponding files in the source code; packing the modified source code to generate a new root container image.

[0043] The core of the embodiment of the present invention is data collection and data analysis. Both parts are carried out in the root container. By modifying the source code of the root container, packet capture is performed on the network namespace where the pod is located to obtain the data of inter-container communication within the pod. The code for capturing traffic is placed in the root container rather than outside the container cluster, which reduces the coupling between the code for capturing traffic and the actual business software. The focus is on the traffic of the communication between the backend application and services such as the database, message queue, and log reporting server. The process of repackaging to generate a new root container image is as follows:

[0044] a. Open the file k8s.io / kubernetes / build / pause / pause.c, which is the source code of the root container.

[0045] b. In the main function, first determine whether the -v parameter is provided. If it is provided, return the version number and exit. This is the logic of the original code of the root container, and the role of the -v parameter is to obtain the version.

[0046] c. Then determine whether the pid is 1. The pause container needs to be the first container started in the process namespace. This is also the original logic of the root container, and the role of pid is to determine whether the root container is the first container started in the process namespace.

[0047] d. Set the handling methods of several Linux signals, including the signal for interrupting the process, the signal for requesting the process to terminate, and the signal for notifying the parent process. This is also the original logic of the root container. Setting Linux signals is to declare the following logic: when an interrupt signal is received, the program returns a value and exits.

[0048] e. Start the tcpdump subprocess, specify the interface to be captured as any, and capture the traffic of all network interfaces. This step is used to capture network communication data within the cluster. For example, the communication traffic between a web application container and a database container, and set it to be written to / tmp / myflow / . Set multiple files through the -C and -W parameters to facilitate the block analysis of traffic in the data analysis step.

[0049] In one embodiment, the privacy data detection method on the application may further include: if it is determined that the packet of the inter-container communication in the obtained cluster is incomplete, discard the obtained packet and re-obtain the packet of the inter-container communication in the cluster.

[0050] In this embodiment, after collecting the data, some steps for data exception handling can be added. For example, if it is determined that the captured pcap packet is incomplete, or the tcp connection is suddenly disconnected, resulting in the inability to parse the packets of the application, such data can be discarded.

[0051] f. The main process loop checks whether a new pcap packet has been created in this directory. If so, it calls the program that performs privacy analysis.

[0052] The methods for privacy analysis and statistics can be as follows:

[0053] 1. The privacy analysis reads the pcap packet created in the above steps.

[0054] 2. As a privacy detection solution, it mainly concerns the issue of storing user privacy information.

[0055] In one embodiment, the packets of the pcap packet can be packets of one or any combination of MySQL, HTTP, PostgreSQL, SQLServer, and Hadoop protocols. Privacy analysis supports identifying and parsing the data packets of the following protocols through packet protocol recognition: MySQL, HTTP, PostgreSQL, SQLServer, Hadoop, etc. Different protocol data packets can be identified according to the packet headers of the data packets. Different parsing methods can be adopted according to different protocol packets to screen privacy data from them.

[0056] 3. According to different categories of packets, it is detected whether the data contains specific sensitive information.

[0057] Figure 2 This is a flowchart of a specific example of the privacy data detection method in the application of the embodiments of the present invention. As Figure 2 shown, in one embodiment, the privacy data detection method in the application can further include:

[0058] Step 201, identify the type of the packet;

[0059] Step 202, according to the type of the packet, use regular expressions and / or string matching to detect whether the packet includes privacy data.

[0060] Regular expressions and string matching can be used to detect whether the data contains specific privacy data. For example, if it is necessary to detect whether the data contains information such as ID card numbers, telephone numbers, and bank card numbers, regular expressions or string matching can be used to detect this information.

[0061] Since there are many types of privacy data and they are very miscellaneous, a privacy data detection model can be pre-trained using deep learning methods, that is, inputting the packets of cluster data and outputting the contained privacy data.

[0062] In one embodiment, to improve the accuracy of privacy data detection, the privacy data detection method in the application may further include: obtaining historical packets; annotating the privacy data in the historical packets; training a neural network model using a deep learning algorithm, the historical packets, and the annotated privacy data to generate a privacy data detection model; testing the privacy data detection model; and adjusting the privacy data detection model until the test result is higher than a preset test result.

[0063] Inputting the packet into the pre-trained privacy data detection model can obtain the privacy data of the application to be detected.

[0064] 4. The matched data is constructed into a json string and sent to the user interface via an HTTP request, including the pod name, packet protocol, trigger rule, and the data sent.

[0065] In one embodiment, displaying the privacy data of the application to be detected includes: converting the privacy data into a json string; using vue to display the cluster name, packet protocol, and the converted privacy data.

[0066] In one embodiment, to improve the performance of the root container and enhance the display efficiency, displaying the privacy data of the application to be detected may include: displaying the privacy data of the application to be detected on the user interface deployed outside the root container.

[0067] Too complex logic should not be executed in the root container. Therefore, in the embodiment of the present invention, the user interface is deployed outside the root container. The user interface uses the flask framework of Python to implement the backend and runs an HTTP service for receiving and storing the data reported by privacy analysis. The front-end uses vue to complete the information display for showing all application data packets that may have privacy leaks to the user.

[0068] Compared with the privacy data detection scheme in the prior art, the privacy data detection method in the application provided by the embodiment of the present invention obtains the modified content of the source code of the root container of the cluster of the application to be detected; modifies the source code according to the modified content to generate a new root container image; runs the new root container image to obtain the packets of the communication between containers in the cluster; inputs the packets into the pre-trained privacy data detection model to output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using a deep learning algorithm, historical packets, and the privacy data in the historical packets; and displaying the privacy data of the application to be detected can achieve large-scale and automated compliance detection of privacy data for applications deployed in containers, reduce the detection difficulty, bypass the problem of application encryption that needs to be overcome in the traditional detection process, and improve the detection accuracy.

[0069] An embodiment of the present invention also provides a privacy data detection device in an application, as described in the following embodiments. Since the principle of the device for solving problems is similar to that of the privacy data detection method in the application, the implementation of the device can refer to the implementation of the privacy data detection method in the application, and the repeated parts will not be elaborated.

[0070] Figure 4 It is a structural block diagram of the privacy data detection device in the application in the embodiment of the present invention, as Figure 4 shown. The device may include:

[0071] An acquisition module 401, configured to acquire the modified content of the source code of the root container of the cluster of the application to be detected;

[0072] A modification module 402, configured to modify the source code according to the modified content to generate a new root container image;

[0073] An operation module 403, configured to run the new root container image to acquire the messages for communication between containers in the cluster;

[0074] An output module 404, configured to input the messages into a pre-trained privacy data detection model and output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using a deep learning algorithm, historical messages, and privacy data in the historical messages;

[0075] A display module 405, configured to display the privacy data of the application to be detected.

[0076] In one embodiment, the privacy data detection device in the application may further include: a training module, configured to:

[0077] Acquire historical messages;

[0078] Annotate the privacy data in the historical messages;

[0079] Use a deep learning algorithm, historical messages, and the annotated privacy data to train a neural network model to generate a privacy data detection model;

[0080] Test the privacy data detection model;

[0081] Adjust the privacy data detection model until the test result is higher than a preset test result.

[0082] In one embodiment, the acquisition module 401 is specifically configured to:

[0083] Acquire the code for starting a subprocess, specifying a capture interface, acquiring the messages for communication between the capture interface and other interfaces, setting multiple files according to the message type, and storing the messages in the corresponding files;

[0084] The modification module 402 is specifically used for:

[0085] Add code in the source code for starting a child process, specifying a scraping interface, obtaining the message for communication between the scraping interface and other interfaces, setting multiple files according to the message type, and storing the message in the corresponding file;

[0086] Package the modified source code to generate a new root container image.

[0087] In one embodiment, the privacy data detection device on the application may further include: an exception handling module, which is used to discard the obtained message and re-obtain the message for communication between containers in the cluster if it is determined that the message for communication between containers in the obtained cluster is incomplete.

[0088] In one embodiment, the privacy data detection device on the application may further include: a detection module, which is used for:

[0089] Identify the type of the message;

[0090] According to the type of the message, use regular expressions and / or string matching to detect whether the message includes privacy data.

[0091] In one embodiment, the display module 405 is specifically used for:

[0092] Convert the privacy data into a json string;

[0093] Use vue to display the cluster name, message protocol, and the converted privacy data.

[0094] In one embodiment, the display module 405 is specifically used for:

[0095] Display the privacy data of the application to be detected on the user interface deployed outside the root container.

[0096] Compared with the existing privacy data detection solutions in the prior art, the privacy data detection device on the application provided by the embodiments of the present invention obtains the modification content of the source code of the root container of the cluster of the application to be detected; modifies the source code according to the modification content to generate a new root container image; runs the new root container image to obtain the message for communication between containers in the cluster; inputs the message into a pre-trained privacy data detection model to output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using deep learning algorithms, historical messages, and privacy data in the historical messages; and displays the privacy data of the application to be detected, which can realize large-scale and automated compliance detection of privacy data for applications deployed in containers, reduce the detection difficulty, bypass the problem of application encryption that needs to be overcome in the traditional detection process, and improve the detection accuracy.

[0097] Embodiments of the present invention can achieve large-scale and automated security and privacy compliance detection for applications deployed in containers; by performing detection inside the containers, the monitoring points for detecting requests accepted by the application in the traditional detection process are bypassed, and in-application encryption is avoided. Focusing on the interactive data between containers in the pod greatly improves the detection accuracy.

[0098] It should be noted that the privacy data detection method for applications provided by the embodiments of the present invention can be used in the financial field, and can also be used in any technical field other than the financial field. The embodiments of the present invention do not limit the application field of the privacy data detection method for applications.

[0099] Figure 5 Schematic diagram of the physical structure of the electronic device provided by the embodiments of the present invention, as Figure 5 shown, the electronic device includes: a processor 501, a memory 502, and a bus 503.

[0100] Among them, the processor 501 and the memory 502 communicate with each other through the bus 503.

[0101] The processor 501 is used to call program instructions in the memory 502 to execute the privacy data detection method for applications provided by the above method embodiments.

[0102] Embodiments of the present invention also provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the privacy data detection method for applications described above is implemented.

[0103] Embodiments of the present invention also provide a computer program product, which includes a computer program, and when the computer program is executed by a processor, the privacy data detection method for applications described above is implemented.

[0104] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 in one or more blocks.

[0106] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means for implementing the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 in one or more blocks.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 in one or more blocks.

[0108] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting privacy data in an application, characterized in that: include: Obtain the modified content of the source code of the root container of the cluster of the application to be detected; Modify the source code according to the modified content and generate a new root container image; Run the new root container image to obtain the communication messages between containers in the cluster; Input the message into a pre-trained privacy data detection model, and output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using a deep learning algorithm, historical messages, and privacy data in the historical messages; Display the privacy data of the application to be tested.

2. The method according to claim 1, characterized in that Also includes: Get historical messages; Mark the privacy data in historical messages; Use deep learning algorithms, historical messages, and annotated privacy data to train the neural network model and generate a privacy data detection model; Test the privacy data detection model; Adjust the privacy data detection model until the test result is higher than the preset test result.

3. The method according to claim 1, characterized in that Get the modified contents of the source code of the root container of the cluster of the application to be tested, including: Obtain code for starting a child process, specifying a capture interface, obtaining messages for the capture interface to communicate with other interfaces, setting multiple files according to the message type, and storing the messages in corresponding files; Modify the source code according to the modified content and generate a new root container image, including: Add code in the source code for starting a child process, specifying a capture interface, obtaining messages for the capture interface to communicate with other interfaces, setting multiple files according to the message type, and storing the messages in the corresponding files; Package the modified source code to generate a new root container image.

4. The method according to claim 1, characterized in that Also includes: If it is determined that the obtained message for communication between containers in the cluster is incomplete, the obtained message is discarded, and the message for communication between containers in the cluster is obtained again.

5. The method according to claim 1, characterized in that Also includes: Identify the type of message; Depending on the type of the message, regular expressions and / or string matching are used to detect whether the message contains private data.

6. The method according to claim 4, characterized in that Display the privacy data of the application to be tested, including: Convert private data into json string; The cluster name, message protocol, and converted privacy data are displayed.

7. The method according to claim 6, characterized in that Display the privacy data of the application to be tested, including: The privacy data of the application to be detected is displayed in the user interface deployed outside the root container.

8. A privacy data detection device for an application, characterized in that: include: An acquisition module, used to acquire the modified content of the source code of the root container of the cluster of the application to be detected; A modification module is used to modify the source code according to the modification content and generate a new root container image; The running module is used to run the new root container image and obtain the communication messages between containers in the cluster; An output module, used to input the message into a pre-trained privacy data detection model and output the privacy data of the application to be detected; the privacy data detection model is obtained by training a neural network model using a deep learning algorithm, historical messages, and privacy data in the historical messages; The display module is used to display the privacy data of the application to be detected.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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