Session privacy protection method and device based on artificial intelligence and medium

Abstract

The invention discloses a session privacy protection method and device based on artificial intelligence and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: reading historical session data of a local storage space, and carrying out the encryption processing of all historical session records in the historical session data, and obtaining encrypted historical session data; converting the encrypted historical session data into an encrypted historical session list, and displaying the encrypted historical session list in a historical list area of a user interface of the terminal device; and when a preset decryption event in the historical list area is detected, part or all of the encrypted historical session list is decrypted, and the decrypted historical session list is displayed in the historical list area. According to the invention, the session privacy protection balance based on artificial intelligence is enhanced.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to artificial intelligence session privacy protection methods, devices and media. Background Art

[0002] In AI conversation scenarios, users interact with AI systems more and more frequently. However, without privacy protection, users' conversation history is often displayed directly, which brings many security risks. First, users' personal information and conversation content may be accessed by unauthorized persons, resulting in personal privacy leakage. Second, the exposure of sensitive conversations may not only infringe users' privacy rights, but also cause psychological distress or social pressure on users. However, many current AI conversation systems lack effective privacy protection measures.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a method, device and medium for privacy protection based on artificial intelligence conversations, aiming to enhance privacy protection when conversing with artificial intelligence.

[0005] To achieve the above objectives, this application proposes a privacy protection method based on artificial intelligence conversation, including: Read the historical session data in the local storage space, and encrypt all historical session records in the historical session data to obtain encrypted historical session data; Convert the encrypted historical session data into an encrypted historical session list, and display the encrypted historical session list in a history list area of ​​a user interface of a terminal device; When a preset decryption event in the history list area is detected, part or all of the encrypted history session list is decrypted, and the decrypted history session list is displayed in the history list area.

[0006] In one embodiment, the steps of encrypting all historical session records in the historical session data to obtain the encrypted historical session data include: For each historical session record in the historical session data, convert the historical session record into binary data; Grouping the binary data according to a preset number of bytes to obtain at least one data group, wherein if the number of bytes of the last group of data groups is less than the preset number of bytes, padding the number of bytes of the last group of data groups with preset padding bytes; For each data group, according to the mapping relationship between the binary data and the preset encryption character, determine the encryption character mapped to the binary data in the data group; Merge the encrypted characters in all data groups into an encrypted string, and add a preset padding character at the end of the encrypted string to generate an encrypted historical session record, wherein the number of padding characters is consistent with the number of padding bytes; All encrypted historical session records are updated to historical session data, and the updated historical session data is used as the encrypted historical session data.

[0007] In one embodiment, after the step of displaying the decrypted history conversation list in the history list area, the method further comprises: When a preset selection event for the decrypted historical conversation list is detected, a first conversation index corresponding to the historical conversation selected by the selection event is obtained, wherein the historical conversation list includes at least one historical conversation, and one historical conversation corresponds to one historical conversation record; Reading a target historical session record corresponding to the first session index in the historical session data from the local storage space, and converting the target historical session record into a target historical session; Populate the target history conversation into the conversation content area of ​​the user interface.

[0008] In one embodiment, the step of filling the target historical conversation into the conversation content area of ​​the user interface includes: If the privacy strength of the target historical session record corresponding to the target historical session is privacy level, a prompt message indicating that a password is required is output; In response to a password input by the user, if the password is consistent with a preset key, populating the target historical session into the user interface; If the privacy strength of the target historical session is a default level, the target historical session record is filled into the user interface, wherein the privacy strength of the privacy level is greater than the privacy strength of the default level.

[0009] In one embodiment, if the privacy strength of the target historical session record corresponding to the target historical session is the privacy level, the step of outputting the prompt information requiring a password includes: In response to a privacy setting instruction triggered by a user, obtaining a second session index in the privacy setting instruction; The privacy strength of the historical session record corresponding to the second session index is set to a privacy level, wherein the privacy strength of the historical session record in the historical session data that is not set to a privacy level is a default level.

[0010] In one embodiment, the step of reading the historical session data in the local storage space includes: Generate a third conversation index, create a new historical conversation record using the third conversation index as a key, and insert the new historical conversation record into the historical conversation data, wherein the third conversation index is generated when the conversation content area is initialized; Receive a conversation message input by the user, and update the conversation message as user data into a new historical conversation record; A response message from the artificial intelligence software in response to the conversation message is obtained, and the response message is updated into a new historical conversation record as artificial intelligence data.

[0011] In one embodiment, the step of encrypting all historical session records in the historical session data includes: Traverse all historical session records in the historical session data; Delete all historical session records that have a preset validity period and whose validity period is earlier than the current time, and obtain updated historical session records; The updated historical session records are encrypted.

[0012] In one embodiment, the step of deleting all historical session records that have a preset validity period and whose validity period is earlier than the current time includes: In response to an expiration date setting instruction triggered by a user, acquiring a fourth session index in the expiration date setting instruction; The validity period of the historical session corresponding to the fourth session index is set to the validity period in the validity period setting instruction.

[0013] In addition, to achieve the above purpose, the present application also proposes an artificial intelligence-based session privacy protection device, which includes: The encryption module reads the historical session data in the local storage space and encrypts all the historical session records in the historical session data to obtain the encrypted historical session data; A display module converts the encrypted historical session data into an encrypted historical session list, and displays the encrypted historical session list in a history list area of ​​a user interface of a terminal device; The decryption module, when detecting a preset decryption event in the history list area, decrypts part or all of the encrypted history session list, and displays the decrypted history session list in the history list area.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes an artificial intelligence-based conversation privacy protection device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the artificial intelligence-based conversation privacy protection method as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the artificial intelligence-based session privacy protection method are implemented as described above.

[0016] In addition, to achieve the above-mentioned objectives, the present application also provides a product, which is a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the artificial intelligence-based session privacy protection method as described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: The present application reads the historical session data of the local storage space, and encrypts all the historical session records in the historical session data to obtain the encrypted historical session data; converts the encrypted historical session data into an encrypted historical session list, and displays the encrypted historical session list in the history list area of ​​the user interface of the terminal device, thereby improving the security of the data, so that the session content cannot be directly read even on the user interface; when a preset decryption event in the history list area is detected, part or all of the encrypted historical session list is decrypted, and the decrypted historical session list is displayed in the history list area. This dynamic display process not only ensures that users can easily access their own session records, but also enhances the privacy protection when conversing with artificial intelligence, thereby improving the user experience and security. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a flowchart of Embodiment 1 of the artificial intelligence conversation privacy protection method of this application; Figure 2 This is a flowchart of Embodiment 2 of the artificial intelligence conversation privacy protection method of this application; Figure 3 This is a process diagram of Embodiment 3 of the artificial intelligence conversation privacy protection method of this application; Figure 4 This is a schematic diagram of the module structure of the artificial intelligence conversation privacy protection device according to an embodiment of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the artificial intelligence session privacy protection method in the embodiment of the present application.

[0021] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a terminal system, etc. The following takes the system as an example to illustrate this embodiment and the following embodiments.

[0025] Based on this, this embodiment provides a privacy protection method based on artificial intelligence conversation, referring to Figure 1 , Figure 1 This is a flowchart of the artificial intelligence conversation privacy protection method of this application, which includes steps S10 to S30: Step S10, reading the historical session data in the local storage space, and encrypting all historical session records in the historical session data to obtain encrypted historical session data; Step S20, converting the encrypted historical session data into an encrypted historical session list, and displaying the encrypted historical session list in a history list area of ​​a user interface of the terminal device; Step S30: when a preset decryption event in the history list area is detected, part or all of the encrypted history conversation list is decrypted, and the decrypted history conversation list is displayed in the history list area.

[0026] It should be noted that terminal devices refer to devices with AI software installed, such as smartphones, tablets or personal computers, through which users interact with AI software. Local storage space is the space inside the terminal device for storing data, including conversation records between users and AI software. Historical session data refers to past conversation records between users and AI software, which may include questions asked by users, answers given by AI, etc. Encryption processing is a data protection method that converts raw data into ciphertext that is difficult to read directly through a specific algorithm to protect the privacy of the data. Encrypted historical session data is encrypted historical session data, which exists in ciphertext to protect the privacy of users. The history list area is the area in the user interface of the terminal device used to display the history session list. The preset decryption event is a specific operation or event triggered by the user to decrypt the encrypted historical session data, such as clicking the decryption button or entering the decryption password.

[0027] In this embodiment, when it is necessary to read historical session data, the system first accesses the local storage space. The "local storage space" here refers to the storage area located on the user's terminal device, such as the browser's localStorage (local storage method) or sessionStorage (session storage method), which is used to store temporary or long-term data. What is read from this storage area is the historical session records saved during the previous conversation, and each record is associated with a specific session ID. In order to protect the user's privacy, these historical session records are converted into an encrypted form, that is, the original text content is encoded to generate a string that cannot be directly read. The result of this step is called "encrypted historical session data."

[0028] Next, the encrypted data is organized into a list format, the "encrypted history conversation list", for easy display in the history list area of ​​the user interface. Each element in the list is an encrypted conversation fragment. This encrypted display method makes it difficult for someone to decipher the content of the historical conversation even if they gain access to the terminal device.

[0029] When the user hovers the mouse or other interactive behaviors trigger the preset decryption event, the system will decrypt part or all of the selected encrypted history conversation list. Decryption is the reverse process of encryption. It uses the same algorithm but works in the opposite way to restore the encrypted string to the original readable text. Subsequently, the decrypted content will be updated and displayed in the history list area, allowing users to view the real conversation record.

[0030] The process of this embodiment includes three main stages: first, the system extracts historical conversation records from the local storage space and encrypts them; then, the encrypted data is organized into a list format and displayed on the user interface; finally, when the user triggers a specific event, the system performs decryption and presents the real content to the user. This process ensures the confidentiality of the user's conversation content and allows the user to easily access the historical records when needed.

[0031] This embodiment also provides an optimization solution, which introduces an intelligent prediction and personalized suggestion mechanism. When a user chooses to resume a historical conversation, in addition to filling in the historical conversation content, the system uses machine learning algorithms to analyze past conversation patterns, try to predict topics or questions that the user may be interested in, and prepare answer options in advance, thereby speeding up the conversation process. In addition, the system can customize recommended content based on user preferences to enhance user experience. For example, in a customer service scenario, if it is detected that the user frequently asks about product information, the relevant information can be automatically loaded when the historical conversation is resumed, reducing the user's query time.

[0032] This embodiment reads the historical session data of the local storage space, and encrypts all historical session records in the historical session data to obtain encrypted historical session data; the encrypted historical session data is converted into an encrypted historical session list, and the encrypted historical session list is displayed in the history list area of ​​the user interface of the terminal device, thereby improving the security of the data, so that the session content cannot be directly read even on the user interface; when a preset decryption event in the history list area is detected, part or all of the encrypted historical session list is decrypted, and the decrypted historical session list is displayed in the history list area. This dynamic display process not only ensures that users can easily access their own session records, but also enhances privacy protection when conversing with artificial intelligence, thereby improving user experience and security.

[0033] In a feasible implementation manner, the steps in step S10 may include steps T10 to T50: Step T10, for each historical session record in the historical session data, convert the historical session record into binary data; Step T20, grouping the binary data according to a preset number of bytes to obtain at least one data group, wherein if the number of bytes of the last group of data groups is less than the preset number of bytes, the number of bytes of the last group of data groups is filled with preset padding bytes; Step T30, for each data group, according to the mapping relationship between the binary data and the preset encryption character, determining the encryption character mapped to the binary data in the data group; Step T40, merging the encrypted characters in all data groups into an encrypted string, and adding a preset padding character at the end of the encrypted string to generate an encrypted historical session record, wherein the number of padding characters is consistent with the number of padding bytes; Step T50, updating all encrypted historical session records to historical session data, and using the updated historical session data as encrypted historical session data.

[0034] It should be noted that binary data is a form of data stored and processed inside a computer, consisting of 0 and 1. The preset number of bytes is the number of bytes contained in each group of data when data is grouped. Data grouping is the data block into which binary data is divided according to the preset number of bytes. Padding bytes are extra bytes used to make up the number of bytes when the number of bytes in the last group of data groups is less than the preset number of bytes. Encrypted characters are encrypted characters corresponding to binary data, and are used to generate encrypted strings. Encrypted strings are strings composed of encrypted characters, and are a form of encrypted historical session records.

[0035] In this embodiment, when processing historical session data, each historical session record is first converted into binary data. The "binary data" mentioned here refers to encoding the original text information into a sequence of 0s and 1s, which is the basic format for computers to process and store information. Then, the system groups these binary data according to a preset number of bytes to form multiple data groups. If the length of the last data group is less than the preset number of bytes, preset padding bytes are added to ensure that it meets the specified size. This grouping method helps to effectively execute subsequent encryption operations.

[0036] For each data group, the system converts the binary data in the group into corresponding encrypted characters according to the preset mapping relationship. The "mapping relationship" here refers to a predefined set of rules that specifies how a specific binary sequence should be converted into specific characters. This process is the core part of encryption. Each binary sequence in the data group determined in this way will have a corresponding encrypted character. After that, the encrypted characters in all data groups are merged into a continuous string, and preset padding characters equal to the number of previous padding bytes are added to the end, and finally a complete encryption history session record is generated. This series of operations ensures that even incomplete groups in the original data can be correctly encrypted.

[0037] Finally, all encrypted historical conversation records are updated to the original historical conversation data, replacing the original unencrypted content, thus forming new encrypted historical conversation data. This process ensures the security and consistency of all historical conversation records, while not affecting the structure of the historical conversation list displayed on the user interface.

[0038] This embodiment further proposes an optimization scheme, which introduces a dynamic adjustment mechanism in the encryption process. Specifically, the preset number of bytes is automatically adjusted according to the performance of the terminal device or the network environment, making the grouping more flexible and efficient. For example, when running on a mobile device, a smaller preset number of bytes may be selected to reduce the computing burden; while on a high-performance computer, a larger preset number of bytes can be used to improve the encryption strength. In addition, the padding characters and encryption character mapping table can be intelligently selected based on the user's usage habits to improve encryption efficiency while maintaining consistency in user experience.

[0039] Through the above steps, this embodiment not only realizes the efficient encryption of historical conversation records, but also ensures data integrity and privacy protection. The dynamically adjusted optimization scheme improves the adaptability in different scenarios and ensures a good user experience and security performance. This encryption method effectively prevents unauthorized access and supports fast decryption and recovery, allowing users to review past conversations safely and conveniently.

[0040] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction, and will not be repeated in the following. Figure 2 , the step S30 also includes steps A10 to A30: Step A10, when a preset selection event for the decrypted historical conversation list is detected, obtaining a first conversation index corresponding to the historical conversation selected by the selection event, wherein the historical conversation list includes at least one historical conversation, and one historical conversation corresponds to one historical conversation record; Step A20, reading a target historical session record corresponding to the first session index in the historical session data from the local storage space, and converting the target historical session record into a target historical session; Step A30: Fill the target historical conversation into the conversation content area of ​​the user interface.

[0041] It should be noted that the preset selection event is a specific operation triggered by the user to select the decrypted historical session. The first session index is an index or identifier used to identify a specific historical session. The first session index is an identifier or index number used to uniquely identify a historical session record in the historical session list, through which the corresponding historical session record can be retrieved from the local storage space. The target historical session record is the record corresponding to the historical session selected by the user. The target historical session is the content presented by the historical session record after reading and decrypting it from the local storage space according to the user's selection (identified by the first session index). The conversation content area is an area in the user interface of the terminal device that is used to display the content of the conversation between the current user and the artificial intelligence software. When a user selects a historical session, the content of the session may be filled in this area for review.

[0042] In this embodiment, when the user interface detects that the user has performed a preset selection event for the decrypted historical session list, the system will capture this selection behavior and determine the specific historical session corresponding to the selection. Here, "preset selection event" refers to the behavior of the user selecting a specific historical session record by clicking or other interactive methods. For each such selection, the system can identify the first session index corresponding to the selected historical session, that is, the unique number used to identify the historical session. Each historical session is associated with a specific historical session record, and these records together constitute the historical session list.

[0043] Subsequently, the system reads the target historical session record associated with the first session index from the local storage space. The local storage space refers to the data storage area stored on the user terminal, such as localStorage or sessionStorage. Here, the target historical session record refers to the data content that matches the specific historical session selected by the user. The read data is converted into a format that can be directly displayed to the user, namely the "target historical session". This conversion process may include operations such as decryption and format adjustment to ensure that the content is presented in the form expected by the user.

[0044] Finally, the system fills the generated target historical conversation into the conversation content area of ​​the user interface, allowing users to view and review past conversation content. The conversation content area is where users interact with artificial intelligence. It not only displays the current conversation, but also loads past historical conversations, allowing users to get a complete communication experience.

[0045] This embodiment also provides an optimization solution. When the user chooses to restore a historical conversation, in addition to filling in the historical conversation content, the system attempts to intelligently predict the possible direction of the next conversation based on the context, and provides preset questions or answers to speed up the conversation process. For example, in a customer service scenario, if the user selects a historical conversation about a product issue, the system can recommend relevant solutions or FAQs based on the previous conversation content, reducing the user's query time and improving service efficiency.

[0046] Through the above steps, this embodiment achieves accurate positioning and efficient recovery of specific historical conversation records, enhancing user experience while protecting privacy. The optimized intelligent prediction function further improves the quality of human-computer interaction, making the conversation more smooth and natural, and provides users with a safe and convenient review platform.

[0047] In a feasible implementation manner, step A30 further includes steps A301 to A303: Step A301: if the privacy strength of the target historical session record corresponding to the target historical session is the privacy level, outputting a prompt message indicating that a password is required; Step A302, in response to the password input by the user, if the password is consistent with the preset key, the target historical session is filled into the user interface; Step A303: if the privacy strength of the target historical session is a default level, the target historical session record is filled into the user interface, wherein the privacy strength of the privacy level is greater than the privacy strength of the default level.

[0048] It should be noted that privacy strength is an indicator to measure the privacy protection level of historical session records. The prompt message requiring a password is a prompt message displayed to the user when the user tries to access the target historical session with a privacy strength of privacy level, requiring the user to enter the password to verify the access right. The preset key is a secret value preset within the system for comparison with the password entered by the user. Only when the password entered by the user matches this key can the user access the historical session at the privacy level. The privacy level is a privacy protection level for historical session records, which usually means that these records contain highly sensitive information and require additional security measures to protect them. The default level is relative to the privacy level, indicating the default privacy protection level for historical session records. Generally, the protection measures at this level are not as strict as the privacy level.

[0049] In this embodiment, when processing the target historical session record, the system first determines the privacy strength of the record. The "privacy strength" here refers to the different protection levels set for specific historical session records, which are divided into "privacy level" and "default level", where the privacy level provides more stringent protection. If the privacy strength corresponding to the target historical session is privacy level, the system outputs a prompt message to the user that a password is required. This prompt message is to inform the user that additional verification must be provided to access more sensitive content.

[0050] After the user responds to the prompt and enters a password, the system compares this password with the preset key. The "preset key" here is a security code specified by the user when setting up privacy protection or automatically generated and saved by the system. Only when the password entered by the user matches the preset key will the system populate the target historical session into the user interface, allowing the user to view the details. This process ensures that only authorized users can access highly protected historical sessions.

[0051] For target historical conversations with the default privacy strength, the system directly populates the target historical conversation records into the user interface without additional verification steps, allowing users to immediately view these relatively less sensitive conversation contents. The differentiation of privacy levels allows users to flexibly adjust the degree of protection of different conversation records according to actual needs.

[0052] This embodiment further proposes an optimization scheme and introduces a dynamic privacy adjustment mechanism. For example, in the internal communication scenario of an enterprise, the system allows administrators to adjust the privacy strength at any time according to the importance of the conversation content. For conversations involving sensitive business information, they can be temporarily upgraded to the privacy level, and temporary keys can be sent to relevant users via SMS or email to ensure the security of instant messaging without affecting work efficiency. This dynamic adjustment not only enhances the flexibility of the system, but also improves the user experience.

[0053] Through the above steps, this embodiment realizes the function of customizing the protection of historical conversations according to different privacy strengths, which not only ensures the security of sensitive information, but also simplifies the access process of ordinary conversations. The optimized dynamic privacy adjustment function further improves the adaptability and security of the system, providing users with a safe and convenient communication platform.

[0054] In a feasible implementation manner, step A301 further includes steps B10 to B20: Step B10, in response to the privacy setting instruction triggered by the user, obtaining a second session index in the privacy setting instruction; Step B20: setting the privacy strength of the historical session record corresponding to the second session index to a privacy level, wherein the privacy strength of the historical session record in the historical session data that is not set to a privacy level is a default level.

[0055] It should be noted that the privacy setting instruction is a user-triggered instruction used to set or change the privacy strength of a historical session record. This usually involves selecting a specific historical session record (identified by the second session index) and setting a new privacy level for it. The second session index is an identifier or index number used to uniquely identify a historical session record in the historical session list for privacy setting.

[0056] In this embodiment, when the user triggers the privacy setting instruction, the system will respond to this instruction and extract the second session index from it. The "privacy setting instruction" here refers to the user specifying which historical sessions require stricter privacy protection through specific operations (such as clicking or selecting). The "second session index" refers to the specific session identifier clearly specified in the instruction and whose privacy strength the user wants to adjust. This index is used to locate the corresponding historical session record so that the system can change its privacy level.

[0057] Next, the system sets the privacy strength of the historical conversation record corresponding to the second conversation index to the privacy level. This means that for this specific conversation record, it will be given higher security protection measures, such as additional verification may be required when accessing it. At the same time, other conversation records in the historical conversation data that are not set to a privacy level will maintain the default privacy strength. This distinction allows users to flexibly manage the security of different conversation records according to actual needs.

[0058] First, the user performs an action on the user interface, such as clicking a button or menu option, to trigger the privacy setting instruction. After receiving this instruction, the system parses the second session index information contained therein. Then, the system finds the corresponding historical session record based on this index and updates its privacy strength attribute to the privacy level. This process involves searching and modifying the data in the local storage space, ensuring that only the specified session records are protected. For the rest of the historical session records, their privacy strength remains unchanged, that is, the default level, thereby maintaining an established security standard.

[0059] This embodiment further proposes an optimization solution and introduces a batch privacy setting function. For example, in an enterprise environment, an administrator can select multiple conversation indexes at one time and set these conversations to a privacy level. In addition, the system can also provide preset rules to automatically identify and mark conversations containing sensitive keywords as privacy-level without manual intervention. This automated and batch processing method improves efficiency and is particularly suitable for scenarios where privacy settings need to be adjusted frequently.

[0060] Through the above steps, this embodiment achieves precise control over the privacy strength of specific historical conversation records, allowing users to select different protection levels based on the importance of the conversation content. The optimized batch privacy settings and automated rules not only simplify the management process, but also enhance the flexibility and security of the system, ensuring that important conversations are fully protected while ordinary conversations can be easily accessed.

[0061] Based on the first or second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first or second embodiment can be referred to the above introduction, and will not be repeated hereafter. Step S10 also includes steps C10 to C30: Step C10, generating a third conversation index, creating a new historical conversation record with the third conversation index as a key, and inserting the new historical conversation record into the historical conversation data, wherein the third conversation index is generated when the conversation content area is initialized; Step C20, receiving a conversation message input by the user, and updating the conversation message as user data into a new historical conversation record; Step C30, obtaining a response message from the artificial intelligence software in response to the conversation message, and updating the response message as artificial intelligence data into a new historical conversation record.

[0062] It should be noted that the third session index is a new session identifier generated when the conversation content area is initialized, which is used to create and identify a new historical conversation record. The new historical conversation record is a historical conversation record created using the third session index, which is used to store the latest conversation content between the user and the artificial intelligence software. User data is information input by the user into the artificial intelligence software through the terminal device, such as questions, comments, etc. Artificial intelligence data is the response or content generated by the artificial intelligence software to the user data, such as answers, suggestions, etc.

[0063] In this embodiment, when the conversation content area is initialized, the system generates a third conversation index. This "third conversation index" is a number or identifier used to uniquely identify a newly started conversation. Subsequently, a new historical conversation record is created with this third conversation index as a key and inserted into the historical conversation data. Here, "historical conversation data" refers to a data set that stores all past conversation information, and a "new historical conversation record" refers to a data entry that has just been created and is related to this conversation.

[0064] Next, the system receives the conversation message entered by the user through the conversation content area, and updates this message as user data to the new historical conversation record. This means that the user's input is recorded and associated with the current third conversation index, ensuring that every conversation is documented. Then, the system obtains the response message made by the artificial intelligence software to this conversation message, and updates this response message as artificial intelligence data to the new historical conversation record. In this way, the entire conversation process, including user input and AI response, is completely saved in the historical conversation data.

[0065] In actual operation, when a user opens a new conversation window or restarts a conversation, the system generates a new third conversation index in the local storage space. This index is used to mark the uniqueness of this conversation and serves as the primary key for the new historical conversation record. As the user enters conversation messages, these messages are immediately added to the corresponding new historical conversation record and become part of the user data. Next, the artificial intelligence software processes this message and generates a response message, which is also recorded and becomes AI data in the new historical conversation record. This process ensures that the content of each conversation can be accurately saved for subsequent viewing or analysis.

[0066] This embodiment further proposes an optimization solution, introducing an intelligent context awareness function. For example, in a customer service scenario, the system can not only save the conversation content, but also automatically identify the conversation topic and suggest whether a new conversation index needs to be started based on the topic relevance. If a significant change in the topic is detected, the system can prompt the user whether to start a new conversation record, thus better organizing and managing the conversation content. In addition, for recurring problems, the system can automatically quote previous solutions, reducing repetitive labor and improving efficiency.

[0067] Through the above steps, this embodiment realizes the accurate recording and management of conversation content, ensuring that each conversation has an independent conversation index, which is convenient for users to review and retrieve. The optimized intelligent context awareness function further improves the rationality of conversation organization, enhances the user experience and work efficiency, and makes the human-computer interaction more efficient and orderly.

[0068] Exemplarily, referring to Figure 3 , read the historical conversation data from the local storage space, convert the historical conversation data to obtain a historical conversation list, then generate a new conversation index, and insert a historical conversation record with the conversation index as the key into the local storage. Its data structure is similar to {key1:[ ]}, where key1 is the newly created conversation index 1. The user sends a message to the artificial intelligence through the dialog box in the conversation content area. The system inserts the first piece of conversation data (user data), which is the first message sent by the user, into the historical conversation record 1 with the conversation index 1 in the local storage space. The data structure is similar to {key1:[{content:“Hello AI”, role:“USER”}]}. Then, the artificial intelligence responds to the previous message, and the system inserts the second piece of conversation data (artificial intelligence data) into the historical conversation record 1 with the conversation index 1 in the space. The data content is the message content responded by the artificial intelligence. The data structure is similar to {key1:[{content:“Hello AI”,role:“USER”}, {content:“Hello user”, role:“AI”}]}. If a new conversation content area or a new conversation content is created, the process starts from the first step again to obtain the conversation index 2 and the historical conversation record 2.

[0069] In a feasible implementation manner, the steps of step S10 further include steps D10~D30: Step D10, traverse all historical conversation records in the historical conversation data; Step D20, delete all historical conversation records that have set a preset expiration date and the expiration date is earlier than the current time to obtain the updated historical conversation records; Step D30, encrypt the updated historical conversation records.

[0070] It should be noted that the validity period is the storage period set for historical session records. Once this period expires, the records will be automatically deleted by the system to protect user privacy or for data management needs.

[0071] In this embodiment, the system first traverses all historical conversation records in the historical conversation data. Here, "historical conversation data" refers to a data set that stores past conversation information, and "historical conversation records" refers to the detailed content of each specific conversation. Traversal means that the system checks these records one by one to determine which records have a preset validity period and the validity period is earlier than the current time. This check is to clean up expired conversation records to ensure the effective use of storage space and privacy protection.

[0072] Next, the system deletes the conversation records that have been set to an expiration date and have passed. After deletion, the updated historical conversation records are obtained, that is, only the conversation records that have not expired or have no expiration date are retained. This process not only helps to maintain the timeliness of historical conversation data, but also reduces unnecessary data storage burden.

[0073] Finally, the updated historical conversation records are encrypted. "Encryption" here means using a specific algorithm to convert the original text into a form that cannot be directly read to protect user privacy. The encrypted data is difficult to interpret even if it is accessed without authorization, thereby improving data security. This step ensures that even after the expired records are cleared, the remaining conversation content is still fully protected.

[0074] Specifically, when it is necessary to clean up expired conversations, the system starts a traversal process to check each historical conversation record one by one. If a record contains a preset expiration date and this date has passed, the system will mark the record for deletion. After completing the inspection of all records, the system batch deletes these marked expired records and generates an updated set of historical conversation records. Subsequently, the system applies an encryption algorithm to the updated set to convert each record into an encrypted form. For example, common encryption algorithms such as AES (Advanced Encryption Standard) can be used to implement this step to ensure data security and confidentiality.

[0075] This embodiment further proposes an optimization scheme, introducing an intelligent cleanup mechanism. For example, in an enterprise environment, the system can automatically identify the sensitivity based on the content of the conversation and dynamically adjust the validity period. For conversations involving sensitive information, the system can recommend a shorter validity period, while allowing a longer storage time in general communication. In addition, the system can also provide a regular reminder function to notify users of conversation records that are about to expire, allowing users to decide whether to extend the validity period. This personalized management not only improves the flexibility of the system, but also better meets the needs of different scenarios.

[0076] Through the above steps, this embodiment realizes the automatic management and security protection of historical conversation records, which can not only clean up the expired conversations that are no longer needed in time, but also ensure the safe storage of the existing conversation content. The optimized intelligent cleaning mechanism further improves the user experience, makes data management more intelligent and humane, and protects the privacy and security of users.

[0077] In a feasible implementation manner, step D20 further includes steps D201-D202: Step D201, in response to a validity period setting instruction triggered by a user, obtaining a fourth session index in the validity period setting instruction; Step D202: Set the validity period of the historical session corresponding to the fourth session index to the validity period in the validity period setting instruction.

[0078] It should be noted that the fourth session index is an identifier or index number used to uniquely identify a historical session record in the historical session list to set a validity period. The validity period setting instruction is a user-triggered instruction used to set a validity period for a specific historical session record. This allows users to manage the storage time of data according to their own needs.

[0079] In this embodiment, when the user triggers the validity period setting instruction, the system responds to the instruction and obtains the fourth session index from it. The "validity period setting instruction" here refers to the user specifying the valid storage time of a certain session through a specific operation (such as clicking a button or selecting a menu item). The "fourth session index" is the specific session identifier that the user wants to set the validity period for, which is clearly indicated in the instruction. This index is used to locate the corresponding historical session record so that the system can change its validity period.

[0080] Subsequently, the system sets the validity period of the historical conversation corresponding to the fourth conversation index to the validity period specified in the validity period setting instruction. This means that for this particular conversation record, it will be given a new validity period, and after this period, the record may be automatically cleaned up or archived to ensure data security and storage efficiency. This process allows users to flexibly manage the life cycle of different conversation records according to actual needs.

[0081] Specifically, when a user performs an action, such as clicking a button to set the validity period, and enters or selects a specific length of time as the validity period, the system receives the validity period setting instruction and parses the fourth session index information contained therein. Then, the system finds the corresponding historical session record based on this index and updates its validity period attribute. For example, if a user selects a session and sets a validity period of 30 days, the system will ensure that the session record is only saved for 30 days. This process involves searching and modifying data in the local storage space to ensure that only the specified session record is set with a new validity period. At the same time, the validity period of other unspecified session records remains unchanged, maintaining the original preservation strategy.

[0082] This embodiment further proposes an optimization scheme, introducing an intelligent recommendation mechanism. For example, in an enterprise environment, the system can automatically analyze the sensitivity of the conversation based on its content and recommend a reasonable validity period. For conversations involving sensitive information, the system can recommend a shorter validity period; while in general communication, a longer storage time is allowed. In addition, the system can also provide a preview function, allowing users to view the effect of applying the new validity period before setting it, so that they can make a more informed choice. This intelligent recommendation not only improves the ease of use of the system, but also better meets the needs of different scenarios.

[0083] Through the above steps, this embodiment achieves precise control over the validity period of specific historical conversation records, allowing users to set different retention periods based on the importance of the conversation content. The optimized intelligent recommendation mechanism further enhances the user experience, ensuring that important conversations are fully protected while ordinary conversations can also be efficiently managed, and improving the flexibility of data management and privacy protection.

[0084] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the artificial intelligence conversation privacy protection method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0085] This application also provides a privacy protection device based on artificial intelligence conversation, please refer to Figure 4 , artificial intelligence-based conversation privacy protection devices include: The encryption module 10 reads the historical session data in the local storage space and encrypts all the historical session records in the historical session data to obtain encrypted historical session data; The display module 20 converts the encrypted historical session data into an encrypted historical session list, and displays the encrypted historical session list in the history list area of ​​the user interface of the terminal device; The decryption module 30, when detecting a preset decryption event in the history list area, decrypts part or all of the encrypted history conversation list, and displays the decrypted history conversation list in the history list area.

[0086] The artificial intelligence conversation privacy protection device provided by the present application adopts the artificial intelligence conversation privacy protection method in the above embodiment, which can enhance the privacy protection when talking with artificial intelligence. Compared with the prior art, the beneficial effects of the artificial intelligence conversation privacy protection device provided by the present application are the same as the beneficial effects of the artificial intelligence conversation privacy protection method provided by the above embodiment, and the other technical features of the artificial intelligence conversation privacy protection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0087] The present application provides an artificial intelligence-based session privacy protection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the artificial intelligence-based session privacy protection method in the above-mentioned embodiment 1.

[0088] Reference below Figure 5 , which shows a schematic diagram of the structure of an artificial intelligence conversation privacy protection device suitable for implementing the embodiment of the present application. The artificial intelligence conversation privacy protection device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The artificial intelligence-based session privacy protection device shown is merely an example and should not bring any limitations to the functions and scope of use of the embodiments of the present application.

[0089] like Figure 5As shown, the artificial intelligence conversation privacy protection device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the artificial intelligence conversation privacy protection device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the artificial intelligence-based session privacy protection device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an artificial intelligence-based session privacy protection device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0090] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0091] The artificial intelligence conversation privacy protection device provided by the present application adopts the artificial intelligence conversation privacy protection method in the above embodiment, which can enhance the privacy protection when talking with artificial intelligence. Compared with the prior art, the beneficial effects of the artificial intelligence conversation privacy protection device provided by the present application are the same as the beneficial effects of the artificial intelligence conversation privacy protection method provided by the above embodiment, and the other technical features of the artificial intelligence conversation privacy protection device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0092] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0093] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0094] The present application provides a medium, which is a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the artificial intelligence-based session privacy protection method in the above-mentioned embodiment.

[0095] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0096] The above-mentioned computer-readable storage medium may be included in the artificial intelligence-based session privacy protection device; or it may exist independently without being assembled into the artificial intelligence-based session privacy protection device.

[0097] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the artificial intelligence conversation privacy protection device, the artificial intelligence conversation privacy protection device: Read the historical session data in the local storage space, and encrypt all historical session records in the historical session data to obtain encrypted historical session data; Convert the encrypted historical session data into an encrypted historical session list, and display the encrypted historical session list in a history list area of ​​a user interface of a terminal device; When a preset decryption event in the history list area is detected, part or all of the encrypted history session list is decrypted, and the decrypted history session list is displayed in the history list area.

[0098] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0099] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0100] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0101] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned artificial intelligence conversation privacy protection method, which can enhance privacy protection when conversing with artificial intelligence. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the artificial intelligence conversation privacy protection method provided in the above-mentioned embodiment, and will not be repeated here.

[0102] The present application also provides a product, which is a computer program product, including a computer program, which implements the steps of the above-mentioned artificial intelligence-based session privacy protection method when executed by a processor.

[0103] The computer program product provided in this application can enhance privacy protection when talking to artificial intelligence. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the artificial intelligence conversation privacy protection method provided in the above embodiment, which will not be repeated here.

[0104] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A privacy protection method based on artificial intelligence conversation, characterized in that: Applied to a terminal device provided with artificial intelligence software, the terminal device includes a local storage space storing historical conversation data of a user's conversation record with the artificial intelligence software, and the artificial intelligence conversation privacy protection method includes: Reading the historical session data in the local storage space, and encrypting all historical session records in the historical session data to obtain encrypted historical session data; Converting the encrypted historical session data into an encrypted historical session list, and displaying the encrypted historical session list in a history list area of ​​a user interface of the terminal device; When a preset decryption event in the history list area is detected, part or all of the encrypted history session list is decrypted, and the decrypted history session list is displayed in the history list area.

2. The artificial intelligence conversation privacy protection method according to claim 1, characterized in that: The step of encrypting all historical session records in the historical session data to obtain encrypted historical session data comprises: For each historical session record in the historical session data, convert the historical session record into binary data; Grouping the binary data according to a preset number of bytes to obtain at least one data group, wherein if the number of bytes of the last group of data groups is less than the preset number of bytes, padding the number of bytes of the last group of data groups with preset padding bytes; For each of the data packets, determining the encryption character mapped to the binary data in the data packet according to the mapping relationship between the binary data and the preset encryption character; Merging the encrypted characters in all the data packets into an encrypted string, and adding a preset padding character at the end of the encrypted string to generate an encrypted historical session record, wherein the number of the padding characters is consistent with the number of the padding bytes; All the encrypted historical session records are updated to the historical session data, and the updated historical session data is used as the encrypted historical session data.

3. The artificial intelligence conversation privacy protection method according to claim 1, characterized in that: After the step of displaying the decrypted history conversation list in the history list area, the following steps are included: When a preset selection event for the decrypted historical session list is detected, obtaining a first session index corresponding to the historical session selected by the selection event, wherein the historical session list includes at least one historical session, and one historical session corresponds to one historical session record; Reading a target historical session record corresponding to the first session index in the historical session data from the local storage space, and converting the target historical session record into a target historical session; The target historical conversation is filled into the conversation content area of ​​the user interface.

4. The artificial intelligence conversation privacy protection method according to claim 3, characterized in that: The step of filling the target historical conversation into the conversation content area of ​​the user interface comprises: If the privacy strength of the target historical session record corresponding to the target historical session is the privacy level, outputting a prompt message that a password is required; In response to a password input by a user, if the password is consistent with a preset key, filling the target historical session into the user interface; If the privacy strength of the target historical conversation is at a default level, the target historical conversation record is filled into the user interface, wherein the privacy strength of the privacy level is greater than the privacy strength of the default level.

5. The artificial intelligence conversation privacy protection method according to claim 4, characterized in that: If the privacy strength of the target historical session record corresponding to the target historical session is the privacy level, the step of outputting a prompt message indicating that a password is required includes: In response to a privacy setting instruction triggered by a user, obtaining a second session index in the privacy setting instruction; The privacy strength of the historical session record corresponding to the second session index is set to a privacy level, wherein the privacy strength of the historical session record in the historical session data that is not set to a privacy level is a default level.

6. The artificial intelligence conversation privacy protection method according to claim 1, characterized in that: The step of reading the historical session data from the local storage space includes: Generate a third conversation index, create a new historical conversation record using the third conversation index as a key, and insert the new historical conversation record into the historical conversation data, wherein the third conversation index is generated when the conversation content area of ​​the user interface is initialized; Receive a conversation message input by a user, and update the conversation message as user data into the new historical conversation record; A response message from the artificial intelligence software in response to the conversation message is obtained, and the response message is updated into the new historical conversation record as artificial intelligence data.

7. The artificial intelligence conversation privacy protection method according to claim 6, characterized in that: The step of encrypting all historical session records in the historical session data comprises: Traversing all historical session records in the historical session data; Deleting all historical session records that have a preset validity period and the validity period is earlier than the current time, to obtain updated historical session records; The updated historical session records are encrypted.

8. The artificial intelligence conversation privacy protection method according to claim 7, characterized in that: The step of deleting all historical session records with a preset validity period and the validity period being earlier than the current time includes: In response to an expiration date setting instruction triggered by a user, acquiring a fourth session index in the expiration date setting instruction; The validity period of the historical session corresponding to the fourth session index is set to the validity period in the validity period setting instruction.

9. An artificial intelligence conversation privacy protection device, characterized in that: Applied to a terminal device provided with artificial intelligence software, the terminal device includes a local storage space storing historical conversation data of a user's conversation record with the artificial intelligence software, and the artificial intelligence conversation privacy protection device includes: An encryption module reads the historical session data in the local storage space and encrypts all historical session records in the historical session data to obtain encrypted historical session data; A display module, converting the encrypted historical session data into an encrypted historical session list, and displaying the encrypted historical session list in a history list area of ​​a user interface of the terminal device; The decryption module, when detecting a preset decryption event in the history list area, decrypts part or all of the encrypted history session list, and displays the decrypted history session list in the history list area.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the artificial intelligence conversation privacy protection method according to any one of claims 1 to 8.

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