Number privacy protection method

Abstract

The invention relates to the technical field of information security, and provides a number privacy protection method. The method comprises the following steps: receiving and analyzing a communication request of a first user to obtain communication context information; performing privacy demand degree prediction based on the communication context information to generate a first privacy demand degree index; triggering analysis of a privacy protection mode is carried out based on the first privacy demand degree index, and a first privacy protection triggering mode is generated; and based on the first privacy protection triggering mode, calling a privacy protection module to configure a virtual number and an additional protection decision for the first user and the first receiver, and establishing communication between the first user and the first receiver according to a configuration result. The technical problem that an existing privacy protection method cannot dynamically evaluate the privacy protection requirement according to the actual communication context is solved, and the technical effects that personalized privacy protection decision making is conducted according to the communication scene, the receiver identity and the historical communication behavior, and the flexibility and accuracy of number privacy protection are improved are achieved.

Description

Technical Field

[0001] The present application relates to the field of information security technology, and in particular to a method for protecting number privacy. Background Art

[0002] With the rapid development of information technology and communication networks, the privacy protection of personal information has become a focus of social attention. In daily life, users frequently use mobile devices to conduct various online activities, such as ordering takeout, calling online car-hailing, and social chatting. In these activities, users' personal sensitive information such as phone numbers are often passed to merchants, service providers or other communication objects, which easily leads to the risk of personal privacy leakage. Although traditional privacy protection methods can provide a certain degree of security, they are usually only static number encryption or virtual number use, which is difficult to cope with the ever-changing communication scenarios and dynamic privacy needs. In scenarios such as takeout, online car-hailing, and social chatting, users' privacy protection needs are not static, but will change according to different communication scenarios, recipient identities, and communication time and space. For example, when ordering takeout, users may be concerned about whether their phone numbers are exposed to merchants. When calling online car-hailing, users have more requirements for privacy protection because it may involve personal location and higher security risks. Traditional privacy protection methods lack flexibility for these actual scenarios and often adopt fixed privacy protection modes, and privacy protection needs in different scenarios are not fully considered. Especially for social chats and other activities with frequent communications and changing scenarios, existing technologies are often unable to adjust privacy protection strategies in real time and dynamically. Therefore, how to dynamically predict privacy protection needs based on actual communication scenarios and formulate appropriate protection measures accordingly has become an urgent problem to be solved. Summary of the invention

[0003] This application provides a number privacy protection method, aiming to solve the technical problem that existing privacy protection methods fail to dynamically evaluate privacy protection requirements according to actual communication contexts.

[0004] In view of the above problems, the present application provides a number privacy protection method.

[0005] The present application provides a number privacy protection method, the method comprising: receiving a communication request of a first user and parsing it to obtain communication context information, wherein the communication context information comprises a communication scenario, an identity of a first recipient, communication time and space information, and historical communication records; performing a privacy requirement prediction based on the communication scenario, the identity of the first recipient, the communication time and space information, and the historical communication records to generate a first privacy requirement index; performing a trigger analysis of a privacy protection mode based on the first privacy requirement index to generate a first privacy protection trigger mode; based on the first privacy protection trigger mode, calling a privacy protection module to configure a virtual number and an additional protection decision for the first user and the first recipient, and establishing communication between the first user and the first recipient according to the configuration result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above-mentioned number privacy protection method first receives and parses a communication request of a first user to obtain communication context information, wherein the communication context information includes a communication scenario, an identity of a first recipient, communication time and space information, and historical communication records; then, based on the communication scenario, the identity of the first recipient, the communication time and space information, and the historical communication records, a privacy demand degree prediction is performed to generate a first privacy demand degree index; further, based on the first privacy demand degree index, a trigger analysis of a privacy protection mode is performed to generate a first privacy protection trigger mode; finally, based on the first privacy protection trigger mode, a privacy protection module is called to configure a virtual number and an additional protection decision for the first user and the first recipient, and communication between the first user and the first recipient is established according to the configuration result, thereby solving the technical problem that the existing privacy protection method fails to dynamically evaluate the privacy protection demand according to the actual communication context, and realizing personalized privacy protection decisions according to the communication scenario, the identity of the recipient, and the historical communication behavior, and improving the flexibility and accuracy of number privacy protection.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 The figure is a flowchart of a number privacy protection method in an embodiment.

[0010] Figure 2 The present invention is a flow chart of generating a first privacy requirement indicator in a number privacy protection method in an embodiment. DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a number privacy protection method to solve the technical problem that the existing privacy protection method fails to dynamically evaluate the privacy protection requirements according to the actual communication context.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0014] Examples, such as Figure 1 As shown, the present application provides a number privacy protection method, the method comprising: A communication request from a first user is received and parsed to obtain communication context information, wherein the communication context information includes a communication scenario, an identity of the first recipient, communication time and space information, and historical communication records.

[0015] In an embodiment of the present application, when a first user initiates communication, a communication request sent by the first user is received, and communication context information of the current communication is parsed from the communication request of the first user, including a communication scenario, an identity of the first recipient, communication time and space information, and historical communication records, wherein the communication scenario refers to a specific situation or activity type in which the communication occurs. For example, the first user may be ordering takeout, calling an online car-hailing service, or engaging in social chatting, etc. Each communication scenario may have different privacy requirements. When ordering takeout, the first user usually wants to hide his or her personal number to avoid harassment from merchants or delivery personnel, so the demand for privacy protection is relatively high. During social chatting, since the conversation partners are usually acquaintances or friends, users may not pay much attention to privacy protection, and the privacy demand is relatively low. For online car-hailing services, since real-time location and security issues are involved, the privacy demand is high, and strict privacy protection is required. The identity of the first recipient refers to the identity category of the communication recipient, for example, a stranger (such as the other party in a social chat), a corporate service number (such as the customer service number of an online car-hailing company or a food delivery platform). The identity of the recipient will directly affect the privacy protection decision. For strangers, the first user may be more inclined to protect their privacy. The communication spatiotemporal information refers to the time and geographical location of the communication. For example, whether the communication request of the first user occurs at night, on holidays or in certain specific areas (such as privacy-sensitive places). The communication spatiotemporal information helps to assess potential privacy risks. If the communication occurs in certain high-risk areas (such as financial places or sensitive areas) or at night, it may be necessary to enhance the privacy protection strategy to prevent information leakage or abuse. The historical communication record refers to the past communication history between the first user and the recipient, including the communication frequency and interaction mode. For example, whether the first user has had multiple contacts with the same recipient, whether the contact interval is short, or whether there is no contact for a long time, etc. These historical data help to judge the privacy needs of the current communication. Frequent and long-term interactions may indicate that the recipient is a trustworthy communication object, so the privacy protection needs are relatively low, while short-term or occasional communications may require stricter privacy protection. The above four aspects of communication context information can provide a basis for the subsequent dynamic evaluation of the user's privacy needs, help make appropriate privacy protection decisions, and ensure the accuracy and flexibility of privacy protection.

[0016] A privacy requirement degree prediction is performed based on the communication scenario, the identity of the first recipient, the communication spatiotemporal information, and historical communication records to generate a first privacy requirement degree index.

[0017] In one embodiment, after parsing the communication scenario, the identity of the first recipient, the communication time and space information and the historical communication records from the communication context information, a privacy demand analysis is performed through a privacy demand classifier based on the communication scenario, the identity of the first recipient and the historical communication records, that is, to determine whether the current communication of the first user requires privacy protection, and generate an initial privacy demand degree based on the judgment result, and then merge this initial privacy demand degree with the risk indicator identified from the communication time and space information to generate a final first privacy demand degree indicator. This first privacy demand degree indicator quantifies the demand for privacy protection in the current communication. The higher the first privacy demand degree indicator, the stricter the requirement for privacy protection. According to the first privacy demand degree, the privacy protection strategy can be dynamically adjusted, such as triggering different privacy protection modes, to ensure that the privacy of the first user is accurately and effectively protected.

[0018] Further, if Figure 2 As shown, the present application provides a method for predicting the privacy requirement based on the communication scenario, the identity of the first recipient, the communication spatiotemporal information and the historical communication records, and generating a first privacy requirement index, including: Based on the historical communication records, the communication frequency characteristics between the first user and the first recipient are analyzed; a privacy requirement classifier is called to perform a privacy requirement analysis on the communication scenario, the identity of the first recipient and the communication frequency characteristics to generate an initial privacy requirement degree; communication risk identification is performed based on the communication spatiotemporal information to generate a first communication risk index; the initial privacy requirement degree is weighted by the first communication risk index to generate the first privacy requirement degree index.

[0019] Preferably, communication data related to the first user and the first recipient are retrieved from the historical communication records, and these data include the timestamp of the communication, the type of communication (such as phone calls, text messages, video calls, etc.), the duration of the communication, etc. In order to more accurately reflect the communication frequency characteristics, an analysis time window is defined. For example, the past week or month can be set as the time range, which is determined according to actual business needs; then, according to business accuracy requirements, multiple analysis time windows are set, and based on these analysis time windows, the communication data related to the first user and the first recipient in the historical communication records are divided, and the communication data is divided from the time of the last communication Starting from the beginning of the stamp, the communication data corresponding to multiple analysis time windows are divided forward. In each analysis time window, the number of window communications between the first user and the first recipient is counted. This window communication number can reflect the frequency of interaction between the two. The communication frequency characteristics between the first user and the first recipient are obtained by calculating the average of the window communication numbers of multiple analysis time windows, and then calculating the ratio of this average to the length of the analysis time window. The larger the communication frequency characteristic, the more likely the first user and the first recipient are acquaintances or have a strong trust relationship, and the lower the privacy protection demand. On the contrary, it means that the first user and the first recipient are relatively unfamiliar, and the privacy protection demand is high. The protection requirement is high; then, the communication frequency feature, the current communication scenario, and the identity of the first recipient are input into the privacy requirement classifier for privacy requirement analysis. The privacy requirement classifier maps the communication frequency feature, the communication scenario, and the identity of the first recipient to a plane according to the learned mapping relationship, and determines whether the current communication of the first user requires privacy protection. If the first user requires privacy protection, the privacy requirement classifier outputs a preset privacy requirement degree (such as 1, 0.5, etc.) as the initialization privacy requirement degree. If the first user does not need privacy protection, the privacy requirement classifier outputs 0 as the initialization privacy requirement degree; then, The communication spatiotemporal information is compared with the existing privacy risk label database to analyze whether the specific time and location of the communication is in a high-risk area or time period, such as at night or in a hospital. A first communication risk indicator is generated based on the analysis process to quantify the degree of privacy risk of the current communication. Finally, the generated first communication risk indicator is weighted and summed with the initial privacy requirement (the weights of the first communication risk indicator and the initial privacy requirement are determined based on historical experience and expert decisions) to calculate the first privacy requirement indicator, which can reflect the comprehensive privacy protection needs and provide a basis for triggering subsequent privacy protection modes.

[0020] The privacy requirement classifier can be built based on a support vector machine or other machine learning algorithms, such as decision trees, random forests, neural networks, etc. When using a support vector machine to build it, historical data containing features such as communication scenarios, recipient identities, historical communication records, and corresponding privacy protection situations are first collected. The historical communication records are processed in the same way as above and combined with the communication scenarios and recipient identities to obtain sample data. These sample data are identified according to the privacy protection situation, that is, whether these sample data are privacy protected. The identified sample data are then divided into a training set and a test set, usually 80% as a training set and 20% as a test set. Subsequently, a support vector machine (SVM) is selected as the classification model, and a radial basis kernel (RBF kernel) is used to process nonlinear features. The parameters of the support vector machine model are initialized, including the kernel function, setting C parameters and gamma parameters, etc. The initial parameters of the model are set by random initialization or preset values. Afterwards, the support vector machine model is tested using the training set. Training: During the training process, the feature data of the training set (including communication scenarios, recipient identities, and communication frequencies) is input, and the support vector machine model constructs an optimal hyperplane to classify the data and distinguish between two types of data that require privacy protection and those that do not. During the training process, the support vector machine optimization algorithm is also used to find the best classification boundary by maximizing the classification interval. Then, cross-validation is used to adjust the C parameter and gamma parameter to optimize the classification effect of the model to avoid overfitting and underfitting. After the training is completed, the trained model is evaluated using the test set. Evaluation indicators such as prediction accuracy, precision, recall rate, and F1 score are calculated to ensure that the model can correctly identify whether privacy protection is required. During the evaluation process, if the performance of the model on the test set does not meet expectations, the learning rate, C value, gamma value and other hyperparameters are adjusted to further optimize the model until the optimal effect is achieved. Finally, the trained support vector machine model is used as the privacy requirement classifier for output to perform subsequent privacy requirement analysis.

[0021] Further, the present application provides performing communication risk identification based on the communication spatiotemporal information to generate a first communication risk indicator, including: An existing privacy risk label database is called to compare the communication spatiotemporal information to generate a comparison result; and risk calculation is performed according to the comparison result to generate the first communication risk indicator.

[0022] Optionally, when identifying the communication risk of communication spatiotemporal information, first extract the time information (such as early morning, late night, weekdays or holidays) and geographic location information (such as the current location of the first recipient or the communication target address) from the communication spatiotemporal information; then, call a pre-built privacy risk label database, which stores privacy risk labels related to time and geographic location and corresponding risk scores, for example, high-risk time labels and time risk scores (late night period: 0.6 points), high-risk area labels and location risk scores (high-incidence areas of fraud: 0.9 points, sensitive geographic locations: 0.6 points), etc.; then, compare the extracted time information and geographic location information with the privacy risk label database, check whether it falls within the range of the high-risk time label based on the time when the communication occurs, and check whether it is located in the high-risk area label based on the geographic location information. Based on the generated comparison result, perform risk calculation on the communication spatiotemporal information of the first user, that is, perform weighted summation on the time risk score and location risk score in the comparison result to obtain the first communication risk indicator of the first user, which is used for subsequent evaluation of the degree of privacy protection demand to ensure the dynamic and accurate nature of privacy protection.

[0023] A trigger analysis of a privacy protection mode is performed based on the first privacy requirement indicator to generate a first privacy protection trigger mode.

[0024] In one embodiment, after calculating the first privacy requirement indicator, the first privacy requirement indicator is matched with a privacy protection mode triggering rule. The privacy protection mode triggering rule includes a privacy requirement range corresponding to each privacy protection mode (such as an original number communication mode and a privacy number communication mode). By comparing the first privacy requirement indicator with the privacy requirement range of each privacy protection mode, the privacy requirement range into which the first privacy requirement indicator falls is identified, and the corresponding privacy protection mode is triggered according to the privacy requirement range. The triggered privacy protection mode will be used as the first privacy protection triggering mode in subsequent communication processes to ensure the privacy security of the communication.

[0025] Furthermore, the present application provides that the privacy protection mode includes an original number communication mode and a privacy number communication mode, and the privacy number communication mode includes a first-level privacy mode and a second-level privacy mode; wherein the first-level privacy mode is a privacy mode that maps the original number to a virtual number, and the second-level privacy mode refers to a privacy mode that combines a virtual number with two-factor authentication.

[0026] Preferably, the privacy protection mode includes an original number communication mode and a privacy number communication mode. The original number communication mode is used when the privacy requirement of the first user is low, and allows the original number of the first user to be directly used to establish communication with the recipient. In this mode, due to the low privacy risk, no additional protection measures are required, and the basic needs of the first user can be met while ensuring the convenience of communication; the privacy number communication mode is used when the privacy requirement of the first user is relatively high, and is used to hide the original number of the first user and use a virtual number for protection. The privacy number communication mode is subdivided into a primary privacy mode and a secondary privacy mode. The primary privacy mode is used when the privacy requirement is medium, and the original number of the first user is replaced with a virtual number by a virtual number mapping method, and communication is established with the recipient through the virtual number. After the communication is completed, the virtual number will be automatically destroyed to ensure that the real number of the first user will not be leaked. The secondary privacy mode is used when the privacy requirement is high. On the basis of virtual number mapping, it is combined with two-factor authentication to further enhance the protection. In this mode, a one-time verification code will be generated and sent to the first recipient through the virtual number. The first recipient must verify successfully before communication can be established. This mode can increase the security of communication while protecting the privacy of the first user, and is suitable for scenarios with high privacy requirements.

[0027] Based on the first privacy protection trigger mode, the privacy protection module is called to configure virtual numbers and additional protection decisions for the first user and the first recipient, and communication between the first user and the first recipient is established according to the configuration result.

[0028] In one embodiment, after determining the first privacy protection trigger mode, the privacy protection policy corresponding to the mode will be obtained. If the first privacy protection trigger mode is the original number communication mode, the privacy protection module will be called to directly establish communication between the first user and the first recipient. If the first privacy protection trigger mode is the first-level privacy mode, the privacy protection module will be started to configure the virtual number to meet the basic privacy protection requirements, and the communication between the first user and the first recipient will be established based on the configured virtual number. If the first privacy protection trigger mode is the second-level privacy mode, the two-factor authentication module will be performed on the basis of the virtual number configuration, that is, a one-time verification code is generated and sent to the first recipient. Only after the first recipient completes the verification code verification, the communication between the first user and the first recipient will be established. Through the above process, the privacy needs of the first user can be dynamically adapted, and the virtual number and additional protection measures can be combined to provide users with safe and reliable communication services.

[0029] Further, the present application provides a method of calling a privacy protection module to configure a virtual number and an additional protection decision for the first user and the first recipient based on the first privacy protection trigger mode, and establishing communication between the first user and the first recipient according to the configuration result, including: If the first privacy protection trigger mode is the original number communication mode, communication is established using the original numbers of the first user and the first recipient.

[0030] Optionally, when the first privacy protection trigger mode is the original number communication mode, it means that the privacy requirements of the current communication scenario are low and no additional privacy protection measures are required. At this time, the privacy protection module will skip the virtual number mapping and additional protection configuration steps and directly create a communication channel for both parties through the real number provided by the first user. This mode is suitable for the first user to communicate with a trusted first recipient. In this way, the communication establishment process can be simplified to ensure the timeliness and convenience of communication.

[0031] Furthermore, the present application also includes: If the first privacy protection trigger mode is the first-level privacy mode in the privacy number communication mode, call the virtual number pool to perform virtual number mapping for the original numbers of the first user and the first recipient, and generate the first user virtual number and the first recipient virtual number; establish communication between the first user and the first recipient using the first user virtual number and the first recipient virtual number, and destroy the virtual number after the communication ends.

[0032] Optionally, when the first privacy protection trigger mode is the first level privacy mode in the privacy number communication mode, the privacy protection module will call the virtual number pool to assign virtual numbers to the original numbers of the first user and the first recipient to ensure that the real number is not leaked. Specifically, the privacy protection module will randomly extract two virtual numbers from the virtual number pool to map the original numbers of the first user and the first recipient, respectively as the first user virtual number and the first recipient virtual number. The virtual number is used as an intermediate identifier to replace the real number to participate in the communication, thereby protecting the privacy of both parties; then, the generated first user virtual number and the first recipient virtual number are used to establish a communication channel between the two parties. During the communication process, the two parties can only see each other's virtual number, but cannot obtain the real number, ensuring privacy security. When the communication is completed, the privacy protection module will immediately recycle and destroy the assigned virtual number. This process ensures the temporary and one-time use of the virtual number to prevent the information from being abused or stored. Through the above process, the privacy function of protecting the user's real number through the virtual number is realized in the first level privacy mode, and the privacy protection mechanism is simplified to meet the scenario of medium privacy needs.

[0033] Further, the present application provides that if the first privacy protection trigger mode is a secondary privacy mode in a privacy number communication mode, after generating a first user virtual number and a first recipient virtual number, the method further includes: Activate the two-factor authentication module, generate a one-time verification code, and send it to the first recipient through the first recipient's virtual number; verify the verification code received by the first recipient through the two-factor authentication module, if the verification is successful, establish communication between the first user and the first recipient using the first user's virtual number and the first recipient's virtual number, and destroy the virtual number after the communication ends.

[0034] Optionally, when the first privacy protection trigger mode is the secondary privacy mode in the privacy number communication mode, the privacy protection module will generate a first user virtual number and a first recipient virtual number in the same manner as described above. Based on the first user virtual number and the first recipient virtual number, the privacy protection module will activate the two-factor authentication module to further improve the security of communication by generating and verifying a one-time verification code. Specifically, the privacy protection module will generate a one-time verification code for the current communication based on the activated two-factor authentication module. The verification code is a temporary security code used to verify the identity of the first recipient and ensure the legitimacy of the communication. Subsequently, the privacy protection module will send the generated one-time verification code to the first recipient through the first recipient virtual number. The first recipient needs to receive and correctly enter the verification code to complete identity authentication. After receiving the verification code returned by the first recipient, the two-factor authentication module will compare it with the generated verification code. If the verification code is successfully verified, it means that the identity of the first recipient is legal, and the privacy protection module will allow communication to be established; after identity authentication is passed, the privacy protection module uses the first user's virtual number and the first recipient's virtual number to establish a communication channel between the two parties. During the entire communication process, the real numbers of both parties are hidden, and the communication is transferred through the virtual number to ensure privacy protection; when the communication is over, the privacy protection module recycles and destroys the used virtual number to ensure that the virtual number will not be reused or abused, thereby further improving the security of privacy protection. Through the above process, double privacy protection is achieved in the secondary privacy mode. Not only the real number is hidden through virtual number mapping, but also the legitimacy of the recipient's identity is ensured through two-factor authentication, effectively reducing privacy leakage and security risks.

[0035] Further, the present application provides that after establishing communication between the first user and the first recipient, the method further includes: Acquire any virtual number generated when establishing communication; introduce a behavior monitoring module to analyze abnormal usage behavior of any virtual number in real time; wherein, the behavior monitoring module is constructed based on historical abnormal call behavior, historical abnormal SMS behavior, historical abnormal communication pattern, and historical geographical and time abnormal pattern; and issue an abnormal reminder signal based on the abnormal usage behavior.

[0036] Optionally, when the first user establishes communication with the first recipient, the privacy protection module generates and allocates a virtual number, which is used to replace the real number to protect the privacy of both parties. The privacy protection module obtains any virtual number assigned to the communicating parties from the virtual number pool and binds it to the communication channel; then, a behavior monitoring module is introduced to monitor the usage behavior of the virtual number in real time. This module is a pre-trained model that continuously tracks the activities of the virtual number and analyzes whether it conforms to the normal usage pattern, thereby identifying abnormal usage behaviors, such as abnormal call behaviors (high-frequency calls, etc.) and abnormal SMS behaviors (repeated SMS, etc.); when constructing the behavior monitoring module, historical abnormal call behaviors, historical SMS behaviors, etc. are collected. Historical abnormal SMS behaviors, historical abnormal communication patterns, historical geographical and temporal abnormal patterns, and historical virtual number behavior activities corresponding to these anomalies. Among them, historical abnormal call behavior refers to the calling behavior of the virtual number in the historical records, including whether the number of calls per hour is abnormal and whether the call duration exceeds the normal range. For example, multiple calls in a short period of time or a single call for an extremely long time will be regarded as abnormal behavior. Historical abnormal SMS behavior refers to the SMS sending pattern of the virtual number in the historical records, including whether the frequency of sending SMS is too high and whether the SMS content is repeated. For example, if a large number of SMS with similar content (such as spam SMS) are sent in a short period of time, it may be regarded as abnormal behavior. Historical abnormal communication patterns refer to the number of virtual numbers in the historical records. The communication objects and pattern characteristics of the virtual number, for example, whether multiple repeated communications with the same number exceed the normal range. If there are frequent communications with a certain number in a short period of time, or the communication frequency is significantly higher than the average level, it may be regarded as abnormal behavior; historical geographic and temporal abnormal patterns refer to the location and time characteristics of the use of virtual numbers in historical records, for example, whether cross-regional behavior occurs at the same time as high-frequency communication. If the communication occurs in a scenario with drastic changes in geographical location (such as across cities), or the time period is concentrated in uncommon time periods such as late at night, it may be regarded as abnormal behavior; by combining the historical virtual number behavior activities and the corresponding anomalies, dividing them into training sets and verification sets, and then using the training set to train the constructed initial behavior monitoring module, the training After the training is completed, the validation set is used for verification to evaluate the abnormal detection performance of the initial behavior monitoring module. The initial behavior monitoring module can be constructed based on neural networks, support vector machines, random forests, etc. Taking the long short-term memory network (LSTM) in the neural network as an example, a structure of an initial behavior monitoring module is constructed, including an input layer, an LSTM hidden layer, a fully connected layer, and an output layer. During the training process, the training data is used for forward propagation, and the input virtual number behavior activities are passed through the LSTM layer layer by layer to generate the prediction results for each time step. The cross entropy loss function is then used to calculate the error between the prediction result and the actual label (abnormal behavior), and the gradient of the loss to the weight of each layer is calculated layer by layer through the back propagation algorithm;Afterwards, the Adam optimizer is used to optimize the weights to minimize the value of the loss function, and this process is repeated until the maximum number of iterations is reached or the loss function converges. After the training is completed, the initial behavior monitoring module is tested using the validation set to evaluate its detection accuracy for abnormal call behavior, SMS behavior, communication mode, and geographical and temporal anomalies. If the accuracy meets expectations, the current initial behavior monitoring module will be output as the final behavior monitoring module. Otherwise, hyperparameters such as the learning rate, the number of LSTM layers, and the number of hidden units are adjusted to further optimize the detection capability of the behavior monitoring module. Finally, the behavior activities of the virtual number collected in real time are input into the trained behavior monitoring module. The module will automatically detect whether the virtual number has abnormal behavior based on the input data, such as abnormal call behavior, abnormal SMS behavior, abnormal communication mode, etc. If there is an abnormality, an abnormal usage behavior will be generated, and an abnormal reminder signal will be triggered based on this abnormal usage behavior. This signal can send an alarm to the first user via SMS, email, or other notification methods to prompt him or her that there is a potential privacy risk and remind him or her to check or stop related communications. In this way, the use of virtual numbers can be monitored in real time, and potential security threats or abuses can be discovered and responded to in a timely manner, thereby further enhancing privacy protection during communications.

[0037] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application receives and parses the communication request of the first user, obtains the communication context information, wherein the communication context information includes the communication scenario, the identity of the first recipient, the communication time and space information and the historical communication records, and then predicts the privacy requirement based on the communication scenario, the identity of the first recipient, the communication time and space information and the historical communication records, and generates a first privacy requirement index; further, based on the first privacy requirement index, performs a trigger analysis of the privacy protection mode, and generates a first privacy protection trigger mode; finally, based on the first privacy protection trigger mode, calls the privacy protection module to configure the virtual number and additional protection decision for the first user and the first recipient, and establishes the communication between the first user and the first recipient according to the configuration result. These technical effects jointly solve the technical problem that the existing privacy protection method fails to dynamically evaluate the privacy protection requirements according to the actual communication context, and realizes the technical effect of making personalized privacy protection decisions according to the communication scenario, the identity of the recipient and the historical communication behavior, and improving the flexibility and accuracy of number privacy protection.

[0038] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0039] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0040] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A number privacy protection method, characterized in that: include: Receiving and parsing a communication request from a first user to obtain communication context information, wherein the communication context information includes a communication scenario, an identity of the first recipient, communication time and space information, and historical communication records; Predicting the privacy requirement based on the communication scenario, the identity of the first recipient, the communication spatiotemporal information, and historical communication records to generate a first privacy requirement index; Performing a trigger analysis of a privacy protection mode based on the first privacy requirement indicator to generate a first privacy protection trigger mode; Based on the first privacy protection trigger mode, the privacy protection module is called to configure virtual numbers and additional protection decisions for the first user and the first recipient, and communication between the first user and the first recipient is established according to the configuration result.

2. A number privacy protection method as claimed in claim 1, characterized in that: The privacy protection mode includes an original number communication mode and a privacy number communication mode, and the privacy number communication mode includes a primary privacy mode and a secondary privacy mode; The first-level privacy mode is a privacy mode that maps the original number to a virtual number, and the second-level privacy mode refers to a privacy mode that combines the virtual number with two-factor authentication.

3. A number privacy protection method as claimed in claim 1, characterized in that: Predicting the privacy requirement based on the communication scenario, the identity of the first recipient, the communication spatiotemporal information, and historical communication records to generate a first privacy requirement index includes: analyzing the communication frequency characteristics between the first user and the first recipient based on the historical communication records; Calling a privacy requirement classifier to perform a privacy requirement analysis on the communication scenario, the identity of the first recipient, and the communication frequency characteristics to generate an initial privacy requirement degree; Perform communication risk identification based on the communication spatiotemporal information to generate a first communication risk indicator; The initialization privacy requirement is weighted by the first communication risk indicator to generate the first privacy requirement indicator.

4. A number privacy protection method as claimed in claim 3, characterized in that: Performing communication risk identification based on the communication spatiotemporal information to generate a first communication risk indicator includes: Calling an existing privacy risk label database to compare the communication spatiotemporal information and generate a comparison result; Perform risk calculation according to the comparison result to generate the first communication risk indicator.

5. A number privacy protection method as claimed in claim 2, characterized in that: Based on the first privacy protection trigger mode, calling a privacy protection module to configure a virtual number and an additional protection decision for the first user and the first recipient, and establishing communication between the first user and the first recipient according to the configuration result, including: If the first privacy protection trigger mode is the original number communication mode, communication is established using the original numbers of the first user and the first recipient.

6. A number privacy protection method as claimed in claim 5, characterized in that: Also includes: If the first privacy protection trigger mode is the first level privacy mode in the privacy number communication mode, calling the virtual number pool to perform virtual number mapping for the original numbers of the first user and the first recipient, and generating a first user virtual number and a first recipient virtual number; The communication between the first user and the first recipient is established using the first user virtual number and the first recipient virtual number, and the virtual numbers are destroyed after the communication ends.

7. A number privacy protection method as claimed in claim 6, characterized in that: If the first privacy protection trigger mode is the secondary privacy mode in the privacy number communication mode, after generating the first user virtual number and the first recipient virtual number, the method further includes: activating a two-factor authentication module, generating a one-time verification code, and sending the code to the first recipient via the first recipient's virtual number; The verification code received by the first recipient is verified by the two-factor authentication module. If the verification is successful, communication between the first user and the first recipient is established using the first user virtual number and the first recipient virtual number, and the virtual number is destroyed after the communication ends.

8. A number privacy protection method as claimed in claim 7, characterized in that: After establishing communication between the first user and the first recipient, the method further includes: Obtain any virtual number generated by establishing communication; Introducing a behavior monitoring module to analyze abnormal usage behavior of any virtual number in real time; Wherein, the behavior monitoring module is constructed based on historical abnormal call behavior, historical abnormal SMS behavior, historical abnormal communication patterns, and historical geographical and temporal abnormal patterns; An abnormal reminder signal is issued based on the abnormal usage behavior.

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