Privacy protection system based on artificial intelligence

Abstract

The invention discloses a privacy protection system based on artificial intelligence, which comprises a user terminal equipment module, a privacy data screening module and a privacy data protection module, and is characterized in that the user terminal equipment module is used for a user to log in the system to upload data and collect data operation habits of the user on terminal equipment; the privacy data screening module is used for preprocessing, identifying and screening out privacy data, and the privacy data protection module is used for adjusting an existing privacy data protection program, protecting the privacy data and controlling the access authority of the privacy data. The user terminal equipment module, the privacy data screening module and the privacy data protection module are in communication connection, the user terminal equipment module comprises a login module, a data entry module, an access module and an operation habit collection module, and the system has the advantages of being high in practicability and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and particularly to a privacy protection system based on artificial intelligence. Background Art

[0002] With the development of Internet technology, more and more data is uploaded by people to social media, including a large amount of privacy data. Due to the complex composition of the data, conventional methods cannot effectively protect privacy data. However, with the rapid development of artificial intelligence technology, it is possible to use artificial intelligence technology to protect privacy data. However, when the existing technology protects privacy data, people log in and upload data through account passwords. After use, they forget to log out, or the account passwords are stolen, resulting in the leakage of privacy data cached in the user's account. The reason for this situation is that the privacy protection system is too procedural and can only protect privacy data according to user operations or fixed procedures. This privacy protection mode cannot effectively protect user privacy and cannot meet the current situation of users' diverse needs. Moreover, the existing technology encrypts all the data uploaded by users, resulting in great inconvenience in using the data and increasing the computing pressure on the terminal device. How to use artificial intelligence technology to screen out privacy data from massive data is an urgent problem to be solved. Therefore, it is necessary to design a privacy protection system based on artificial intelligence with strong practicability and high efficiency. Summary of the Invention

[0003] The purpose of the present invention is to provide a privacy protection system based on artificial intelligence to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: A privacy protection system based on artificial intelligence, including a user terminal device module, a privacy data screening module, and a privacy data protection module, characterized in that: the user terminal device module is used for users to log in to the system and upload data, and collect the data operation habits of users on the terminal device; the privacy data screening module is used for preprocessing the data, identifying and screening out privacy data; the privacy data protection module is used for adjusting the existing privacy data protection program and protecting the data, and controlling the access rights of privacy data; the user terminal device module, the privacy data screening module, and the privacy data protection module are communicatively connected.

[0005] According to the above technical solution, the user terminal device module includes a login module, a data entry module, an access module, and an operation habit collection module. The login module is used for users to log in to the system; the data entry module is used for users to upload data to the system; the access module is used for users to access the data stored in the system; the operation habit collection module is used for collecting the operation habits of users on the terminal device.

[0006] According to the above technical solution, the privacy data screening module includes a data processing module, an intelligent recognition module, and a privacy marking module. The data processing module is used for preprocessing the data uploaded by the user. The intelligent recognition module is used for identifying the privacy data in the data uploaded by the user. The privacy marking module is used for marking the identified privacy data.

[0007] According to the above technical solution, the privacy data protection module includes a privacy protection adaptive module, an access control module, a data traceability module, and a concealment module. The privacy protection adaptive module is used for adjusting the privacy protection program. The access control module is used for controlling the access rights of the privacy data. The data traceability module is used for tracing the data access records. The concealment module is used for concealing the privacy data and concealing the access path.

[0008] According to the above technical solution, the concealment protection adaptive module includes an intelligent learning sub-module, a program adjustment sub-module, and a privacy protection sub-module. The intelligent learning sub-module is used for learning the operation habits of the user. The program adjustment sub-module is used for adjusting the privacy protection program according to the learning result to make it more suitable for the user and improve the user experience. The privacy protection sub-module is used for encrypting and protecting the selected data. The access control module includes an identity verification sub-module, an instruction verification sub-module, and a habit verification sub-module. The identity verification sub-module is used for verifying the identity data of the access data request. The instruction verification module is used for verifying the instructions carried in the access request. The habit verification sub-module is used for verifying the data usage habits of the user.

[0009] According to the above technical solution, the operation method of the privacy protection system mainly includes the following steps: Step S1: The user logs in to the system through the login module to manage the user's data. Through the data entry module, the stored data of the user is entered into the system. The user accesses the data stored in the system database through the access module. During the process of the user entering data, accessing data, and adjusting data, the operation collection module automatically collects the operations of the user on the terminal device. Step S2: After the data is entered into the system, the data processing module is started to process the data. According to the processing result, the privacy data is identified and marked by comparing with the feature library, and the marked privacy data is stored in the privacy database. Step S3: After the privacy data screening is completed, the wireless signal triggers the privacy protection adaptive module to start, and begins to protect the privacy data. Further, the privacy data is further concealed and protected through the concealment module, and the information of the data access request is recorded through the data traceability module. Step S4: When the system receives an access request, the access control module starts, obtains multiple verification factors carried in the access request, and performs multiple security verifications on the access rights of the access request.

[0010] According to the above technical solution, step S2 further includes the following steps: Step S21: Establish a privacy database, retrieve a large amount of privacy sample data from the privacy database, identify the data type of the privacy sample data, and classify the privacy sample data according to the attribute type of the privacy data; Step S22: After the classification of the privacy sample data is completed, the features of the corresponding privacy sample data are extracted according to the classification results, the extracted features of the privacy sample data are merged, and stored in the feature library of this category; Step S23: Identify the type of data uploaded by the user, retrieve the corresponding feature library according to the currently identified data type, extract the data features of the user uploaded data, compare the data features in the feature library, and if the comparison similarity is greater than the threshold, use the privacy marking module to mark the data and store it in the privacy database; if the comparison similarity is less than the threshold, store the data in the general database.

[0011] According to the above technical solution, step S3 further includes the following steps: Step S31: after the private data is identified, retrieve the private data in the private database, identify the mark on the private data, select a corresponding privacy protection method according to the marked data type, and protect the private data according to the selected privacy protection method; The privacy protection method is as follows: scanning the visual image, identifying the facial feature nodes and text in the visual image, connecting the facial feature nodes in the image, building a face model, overlapping the face model with the visual image, marking the area covered by the face model, marking the identified text area, blurring the marked area using image blurring technology, compressing the text data, and encrypting the compressed data using a key; Step S32: obtaining the access path of the private data, scanning and identifying the access address and route of the private data, releasing the relationship between the access address and the route, and binding the access address with the new random route. Furthermore, when the user locks the only valid route through the user terminal device, the access path cannot be accessed; Step S33: retrieve the collected user operation habit data, analyze the user's operation habits, summarize the user's habit characteristics, and finally optimize the privacy data protection module according to the habit characteristics.

[0012] According to the above technical solution, step S33 further includes the following steps: Step S331: Identify the user's operations in the system, and extract operation features based on the user's operations. The operation features include features of the user manually operating encrypted data, operation process features of the user browsing privacy data, etc.; Step S332: Add the feature of the user manually operating encrypted data to the privacy database to supplement the sample data in the privacy database and supplement the privacy data features in the intelligent recognition module; Step S333: Count the number of times the user browses data, calculate the data with the largest proportion of browsing times, adjust the system program according to the user operation process features, pre-load the data with the largest proportion of the user's browsing times and float it on the page in the form of a floating window.

[0013] According to the above technical solution, step S4 further includes the following steps: Step S41: Extract the authentication information in the access request carrier. The system pops up a window for the user to complete the identity information, and compare the completed information with the system database. When there is exactly the same data in the system, the verification is successful and the user has access rights; otherwise, the verification fails and the user does not have access rights; Step S42: When the identity verification is completed, extract the instruction in the access request and compare it with the encoding in the database. When the encoding exists in the database, anchor the data and enter the next verification module; otherwise, restrict its access; Step S43: Obtain the real-time user operations, segment the user operations according to the operation purpose, extract the features of each segmented operation, extract the features of the user browsing data, and compare the user's historical browsing data feature set and historical operation feature set. If the similarity is greater than the threshold, access is allowed; otherwise, access is prohibited.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In the present invention, by extracting the privacy sample data features to construct a feature library, the privacy data can be quickly separated by comparing the data to be identified with the feature library. By adopting suitable protection methods for different data types respectively, the security of the privacy data can be enhanced, the occupation of system operation resources can be saved, and the operation speed of the system can be accelerated. By locking the only valid route and using a large number of invalid random routes for jumping, the access address can be disguised as an illegal access state, and the privacy can be protected more effectively. By pre-loading and displaying the data that the user often browses on the page, the data loading time can be reduced, the system can be more in line with the user's usage habits, and a better experience can be brought to the user. By performing multiple verifications on the access request, it can be ensured that the account is used by the user himself, and the security of the system can be further strengthened. Description of the Drawings

[0015] The accompanying drawings are used to provide a further understanding of the present invention and form a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of the system module composition of the present invention. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Please refer to Figure 1 , the present invention provides a technical solution: a privacy protection system based on artificial intelligence, including a user terminal device module, a privacy data screening module, and a privacy data protection module, characterized in that: the user terminal device module is used for users to log in to the system to upload data and collect the data operation habits of users on the terminal device; the privacy data screening module is used for preprocessing the data, identifying and screening out the privacy data; the privacy data protection module is used for adjusting the existing privacy data protection program and protecting the data, controlling the access rights of the privacy data; and the user terminal device module, the privacy data screening module, and the privacy data protection module are communicatively connected.

[0018] The user terminal device module includes a login module, a data entry module, an access module, and an operation habit collection module. The login module is used for users to log in to the system; the data entry module is used for users to upload data to the system; the access module is used for users to access the data stored in the system; and the operation habit collection module is used for collecting the operation habits of users on the terminal device.

[0019] The privacy data screening module includes a data processing module, an intelligent identification module, and a privacy marking module. The data processing module is used for preprocessing the data uploaded by users; the intelligent identification module is used for identifying the privacy data in the data uploaded by users; and the privacy marking module is used for marking the identified privacy data.

[0020] The privacy data protection module includes a privacy protection adaptation module, an access control module, a data traceability module, and a concealment module. The privacy protection adaptation module is used for adjusting the privacy protection program; the access control module is used for controlling the access rights of the privacy data; the data traceability module is used for tracing the data access records; and the concealment module is used for concealing the privacy data and concealing the access path.

[0021] The stealth protection adaptive module includes an intelligent learning sub-module, a program adjustment sub-module, and a privacy protection sub-module. The intelligent learning sub-module is used to learn the user's operation habits. The program adjustment sub-module is used to adjust the privacy protection program according to the learning results to make it more suitable for the user and improve the user experience. The privacy protection sub-module is used to encrypt and protect the selected data. The access control module includes an identity authentication sub-module, an instruction verification sub-module, and a habit verification sub-module. The identity authentication sub-module is used to verify the identity data of the access data request. The instruction verification module is used to verify the instructions carried in the access request. The habit verification sub-module is used to verify the user's data usage habits.

[0022] The operation method of the privacy protection system mainly includes the following steps: Step S1: The user logs in to the system through the login module to manage the user's data. Through the data entry module, the user's stored data is entered into the system. The user accesses the data stored in the system database through the access module. During the process of the user entering data, accessing data, and adjusting data, the operation collection module automatically collects the user's operations on the terminal device. Step S2: After the data is entered into the system, the data processing module is started to process the data. According to the processing results, the privacy data is identified and marked by comparing with the feature library, and the marked privacy data is stored in the privacy database. Step S3: After the privacy data screening is completed, the wireless signal triggers the start of the privacy protection adaptive module to start protecting the privacy data. Further, the privacy data is further protected by the stealth module, and the information of the data access request is recorded by the data traceability module. Step S4: When the system receives an access request, the access control module is started to obtain multiple verification factors carried in the access request and perform multiple security verifications on the access permission of the access request.

[0023] Step S2 further includes the following steps: Step S21: Establish a privacy database, retrieve a large number of privacy sample data from the privacy database, identify the data types of the privacy sample data, and classify the privacy sample data according to the privacy data attribute types. The data type refers to the type of data, including text data, image data, etc. Step S22: After the classification of the privacy sample data is completed, the features of the corresponding privacy sample data are extracted according to the classification results, the extracted features of the privacy sample data are fused, and stored in the feature library of this category. Step S23: Identify the type of data uploaded by the user, retrieve the corresponding feature library according to the currently identified data type, extract the data features of the user uploaded data, and compare the data features in the feature library. If the comparison similarity is greater than the threshold, it means that the data meets the privacy data identification conditions. The data is marked using the privacy marking module and stored in the privacy database. If the comparison similarity is less than the threshold, it means that the data does not meet the privacy data identification conditions. The data is stored in a common database. By extracting the features of the privacy sample data to build a feature library, the privacy data can be quickly separated by comparing the data to be identified with the feature library.

[0024] Step S3 further comprises the following steps: Step S31: after the private data is identified, retrieve the private data in the private data database, identify the mark on the private data, select a corresponding privacy protection method according to the data type of the mark, and the privacy protection method includes blurring the image, encrypting the text data, etc., and protect the private data according to the selected privacy protection method; The privacy protection method is as follows: scanning the visual image, identifying the facial feature nodes and text in the visual image, connecting the facial feature nodes in the image, building a facial model, overlapping the facial model with the visual image, marking the area covered by the facial model, marking the identified text area, blurring the marked area using image blurring technology, compressing the text data, and encrypting the compressed data using a key. By using appropriate protection methods for different data types, the security of privacy data can be enhanced, the occupation of system operating resources can be saved, and the system operation rate can be accelerated. Step S32: obtaining the access path of the private data, scanning and identifying the access address and route of the private data, releasing the relationship between the access address and the route, and binding the access address to a new random route. The random route can be any route in the route library, and only one route can access the address. Furthermore, when the user locks the only valid route through the user terminal device, the access path cannot be accessed. By locking the only valid route and using a large number of invalid random routes for jumping, the access address can be disguised as an illegal access state, which can more effectively protect privacy. Step S33: retrieve the collected user operation habit data, analyze the user's operation habits, summarize the user's habit characteristics, and finally optimize the privacy data protection module according to the habit characteristics.

[0025] Step S33 further includes the following steps: Step S331: Identify the user's operations in the system, and extract operation features based on the user's operations. The operation features include features of the user manually operating encrypted data, operation process features of the user browsing privacy data, etc.; Step S332: Add the feature of the user manually operating encrypted data to the privacy database to supplement the sample data in the privacy database and supplement the privacy data features in the intelligent recognition module, which can make the privacy data recognition more accurate. Step S333: Count the number of times the user browses data, calculate the data with the largest proportion of browsing times, adjust the system program according to the user operation process features, pre-load the data with the largest proportion of the user's browsing times and float it on the page in the form of a floating window. By pre-loading and displaying the data that the user often browses on the page, the data loading time can be reduced, making the system more in line with the user's usage habits and bringing a better experience to the user.

[0026] Step S4 further includes the following steps: Step S41: Extract the authentication information in the access request carrier. The authentication information here is the account identity information of the user's login. The system pops up a window for the user to complete the identity information, and compares the completed information with the system database. When there is exactly the same data in the system, the verification is successful and the user has access rights; otherwise, the verification fails and the user does not have access rights, which can initially screen whether the access request has access rights; Step S42: After the authentication is completed, extract the instruction in the access request. The instruction is the encoding of the data to be accessed, and compare it with the encoding in the database. When the encoding exists in the database, anchor the data and enter the next verification module; otherwise, it means that there is a risk in the account, lock the account, and restrict its access; Step S43: Obtain the real-time user operations, split the user operations according to the operation purposes, extract the features of each split operation, extract the features of the user browsing data, and compare the user's historical browsing data feature set and historical operation feature set. If the similarity is greater than the threshold, it means that the account is used by the user himself and access is allowed; otherwise, it means that there is a risk in the account and access is prohibited. By performing multiple verifications on the access request, it can be ensured that the account is used by the user himself and the security of the system is further enhanced.

[0027] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0028] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A privacy protection system based on artificial intelligence, comprising a user terminal device module, a privacy data screening module, and a privacy data protection module, characterized in that: The user terminal device module is used for users to log in to the system to upload data and collect the data operation habits of users on the terminal device. The privacy data screening module is used for preprocessing the data, identifying and screening out privacy data. The privacy data protection module is used for adjusting the existing privacy data protection program and protecting privacy data, controlling the access rights of privacy data. The user terminal device module, the privacy data screening module and the privacy data protection module are communicatively connected; The user terminal device module includes a login module, a data entry module, an access module and an operation habit collection module. The login module is used for users to log in to the system. The data entry module is used for users to upload data to the system. The access module is used for users to access the data stored in the system. The operation habit collection module is used for collecting the operation habits of users on the terminal device; The privacy data screening module includes a data processing module, an intelligent identification module and a privacy marking module. The data processing module is used for preprocessing the data uploaded by users. The intelligent identification module is used for identifying the privacy data in the data uploaded by users. The privacy marking module is used for marking the identified privacy data; The privacy data protection module includes a privacy protection adaptation module, an access control module, a data traceability module and a concealment module. The privacy protection adaptation module is used for adjusting the privacy protection program. The access control module is used for controlling the access rights of privacy data. The data traceability module is used for tracing the data access records. The concealment module is used for concealing the privacy data and concealing the access path; The concealment protection adaptation module includes an intelligent learning sub-module, a program adjustment sub-module and a privacy protection sub-module. The intelligent learning sub-module is used for learning the operation habits of users. The program adjustment sub-module is used for adjusting the privacy protection program according to the learning results of users' operation habits to make it more suitable for users and improve the user experience. The privacy protection sub-module is used for encrypting and protecting the screened privacy data. The access control module includes an identity verification sub-module, an instruction verification sub-module and a habit verification sub-module. The identity verification sub-module is used for verifying the identity data of the access data request. The instruction verification module is used for verifying the instructions carried in the access request. The habit verification sub-module is used for verifying the data operation habits of users; The operation method of the privacy protection system mainly includes the following steps: Step S1: The user logs in to the system through the login module to manage the user's data, and enters the stored data of the user into the system through the data entry module. The user accesses the data stored in the system database through the access module. During the process of the user entering data, accessing data and adjusting data, the operation collection module automatically collects the operation habits of the user on the terminal device; Step S2: After the data is entered into the system, the data processing module is started to process the data. According to the processing results, the privacy data in the data is identified and marked by comparing with the feature library, and the marked privacy data is stored in the privacy database; Step S3: After the private data is identified, the wireless signal triggers the privacy protection adaptive module to start and start protecting the private data. Furthermore, the private data is further protected by the concealment module, and the information of the data access request is recorded by the data tracing module; Step S4: When the system receives an access request, the access control module starts, obtains multiple verification factors carried in the access request, and performs multiple security verifications on the access rights of the access request.

2. The privacy protection system based on artificial intelligence according to claim 1, characterized in that: The step S2 further comprises the following steps: Step S21: Establish a privacy database, retrieve a large amount of privacy sample data from the privacy database, identify the data type of the privacy sample data, and classify the privacy sample data according to the data type of the privacy sample data, where the data type includes text data and image data; Step S22: After the classification of the privacy sample data is completed, the features of the corresponding privacy sample data are extracted according to the classification results, the extracted features of the privacy sample data are merged, and stored in the feature library of this category; Step S23: Identify the data type uploaded by the user, retrieve the corresponding feature library according to the currently identified data type, extract the data features of the user uploaded data, and compare the data features in the feature library. If the comparison similarity is greater than the threshold, use the privacy marking module to mark the data and store it in the privacy database. If the comparison similarity is less than the threshold, store the data in the general database.

3. The privacy protection system based on artificial intelligence according to claim 1, characterized in that: The step S3 further comprises the following steps: Step S31: after the private data is identified, retrieve the private data in the private data database, identify the mark on the private data, select a corresponding privacy protection method according to the data type of the mark, and protect the private data according to the selected privacy protection method, wherein the privacy protection method includes blurring the image data and encrypting the text data; The privacy protection method includes: scanning the visual image, identifying the facial feature nodes and text in the visual image, connecting the facial feature nodes in the image, building a facial model, overlapping the facial model with the visual image, marking the area covered by the facial model, marking the identified text area, blurring the marked area using image blurring technology, compressing the text data, and encrypting the compressed data using a key; Step S32: obtaining the access path of the private data, scanning and identifying the access address and route of the private data, releasing the relationship between the access address and the route, and binding the access address with the new random route. Furthermore, when the user locks the only valid route through the user terminal device, the access path cannot be accessed; Step S33: retrieve the collected user operation habit data, analyze the user's operation habits, summarize the user's habit characteristics, and finally optimize the privacy data protection module according to the habit characteristics.

4. The privacy protection system based on artificial intelligence according to claim 2, wherein: The step S33 further includes the following steps: Step S331: Identify the operations of the user in the system, extract operation features according to the user operations, and the operation features include the features of the user manually operating encrypted data and the operation process features of the user browsing privacy data; Step S332: Add the user manual operation encrypted data features to the privacy database, supplement the sample data in the privacy database, and supplement the privacy data features in the intelligent recognition module; Step S333: Count the number of times the user browses data, calculate the data with the largest proportion of browsing times, adjust the system program according to the user operation process features, pre-load the data with the largest proportion of the user's browsing times and float it on the page in the form of a floating window.

5. The privacy protection system based on artificial intelligence according to claim 1, wherein: The step S4 further includes the following steps: Step S41: Extract the authentication information in the access request carrier, pop up a window for the system to enable the user to complete the identity information, compare the completed information with the system database. When there is exactly the same data in the system, the verification is successful and the user has access rights. Otherwise, the verification fails and the user does not have access rights; Step S42: After the identity verification is completed, extract the instruction in the access request. The instruction is the encoding of the data to be accessed. Compare the encoding in the database. When the encoding exists in the database, enter the next verification module. Otherwise, restrict its access; Step S43: Obtain the real-time user operations, segment the user operations according to the operation features, extract the features of each segmented operation, extract the features of the user browsing data, compare the user browsing data feature set and the historical browsing data feature set. If the similarity is greater than the threshold, access is allowed. Otherwise, access is prohibited.

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