A mobile phone privacy protection method based on screen touch pressure sensing

Abstract

The application discloses a mobile phone privacy protection method based on screen touch pressure sensing, which comprises the following steps: collecting original data in real time when a user performs a touch operation through a pressure sensor built in a mobile phone screen; pre-processing the collected original data and extracting touch behavior characteristic parameters; comparing the extracted touch behavior characteristic parameters with target touch behavior characteristic parameters of authorized users pre-stored in the mobile phone; and triggering a general privacy protection mechanism when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics; the general privacy protection mechanism comprises at least one of the following: dynamically blurring sensitive content on the screen, hiding preset key data or function modules, and limiting the access permission of specific functions; the application can intelligently judge and prevent unauthorized access by dynamically comparing user touch behavior characteristics, effectively protect the privacy and data security of the user, and improve the security and flexibility of the mobile phone use.

Description

Technical Field

[0001] This invention relates to the field of mobile phone technology, and in particular to a mobile phone privacy protection method based on screen touch pressure sensing. Background Technology

[0002] With the widespread use of smartphones, user privacy and data security have gradually become a focus of attention. Existing mobile privacy protection methods mainly include static authentication methods such as passwords, fingerprints, and facial recognition, as well as traditional methods of encrypting or hiding sensitive data through software. These technologies can prevent unauthorized access to a certain extent, but they generally rely on a single biometric feature or a fixed verification process, making it difficult to adapt to users' dynamic privacy protection needs in complex scenarios.

[0003] However, these existing technologies have many problems in application. First, static authentication methods are easily affected by environmental limitations or external interference in actual operation, such as facial recognition failure in low light or fingerprint recognition malfunction when fingers are wet. Furthermore, traditional data encryption and hiding methods lack real-time and dynamic capabilities, making it difficult to automatically detect and adapt to different privacy protection needs beyond user-initiated verification or settings. For example, when a user's phone is operated by unauthorized personnel, the system struggles to respond promptly and effectively protect sensitive data.

[0004] To address the aforementioned issues, this invention provides a mobile phone privacy protection method based on screen touch pressure sensing. Summary of the Invention

[0005] This application provides a mobile phone privacy protection method based on screen touch pressure sensing to improve the intelligence and accuracy of mobile phone privacy protection.

[0006] This application provides a mobile phone privacy protection method based on screen touch pressure sensing, including:

[0007] The system utilizes a pressure sensor built into the phone screen to collect raw data from the user's touch operation in real time. The raw data includes touch position, pressure distribution, force change, and gesture trajectory.

[0008] The collected raw data is preprocessed to extract touch behavior feature parameters, which include touch pressure intensity, touch frequency, touch path data and pressure change trend data.

[0009] Based on the extracted touch behavior feature parameters, compare them with the target touch behavior feature parameters of authorized users pre-stored in the mobile phone;

[0010] When the comparison result shows that the current touch behavior characteristics do not match the authorized user's behavior characteristics, a general privacy protection mechanism is triggered, which includes at least one of the following:

[0011] Dynamically blur sensitive content on the screen;

[0012] Hide preset key data or functional modules;

[0013] Restrict access to specific functions on the phone.

[0014] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0015] Provide users with control options that allow them to choose between a standard privacy protection mechanism and a hidden privacy protection mechanism;

[0016] If the user selects the normal privacy protection mechanism, the normal privacy protection mechanism will be triggered when the comparison results show that the current touch behavior characteristics do not match the authorized user's behavior characteristics.

[0017] If the user selects the covert privacy protection mechanism, the covert privacy protection mechanism will be triggered when the comparison result shows that the current touch behavior characteristics do not match the authorized user's behavior characteristics. The covert privacy protection mechanism includes at least one of adjusting the interface response speed, delaying the touch response time, and reducing the screen brightness.

[0018] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0019] When the angle of the phone changes beyond a preset threshold, or when sensors, including gyroscopes and accelerometers, detect that the phone has been suddenly moved, flipped, or picked up, the normal privacy protection mechanism or the covert privacy protection mechanism is triggered.

[0020] Alternatively, when the phone detects a sound in the environment, and the direction of the sound source is not in the direction of the user's line of sight, it triggers a normal privacy protection mechanism or a hidden privacy protection mechanism.

[0021] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0022] Provide users with personalized trigger condition settings options, which are used to trigger the ordinary privacy protection mechanism or the covert privacy protection mechanism. The personalized touch settings options include long-pressing a specific area of ​​the screen, performing a sliding operation with a specific pressure value, or repeatedly tapping the screen within a predetermined time.

[0023] When a user-defined personalized trigger condition is detected, either the normal privacy protection mechanism or the covert privacy protection mechanism is activated.

[0024] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0025] After the normal privacy protection mechanism is triggered, the characteristic data of abnormal touch behavior is recorded, including touch pressure intensity, touch frequency and touch path;

[0026] Determine whether abnormal touch behavior belongs to a repetitive pattern;

[0027] If the abnormal touch behavior is a repetitive pattern, the facial feature data, fingerprint feature data, and geolocation feature data of the current touch user are recorded, and the recorded feature data is sent to the authorized user.

[0028] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0029] Establish a data synchronization connection between the mobile phone and the user's associated devices, including smartwatches or tablets using the same account; the data synchronization connection includes sharing the target touch behavior feature parameters of the authorized user pre-stored in the mobile phone to other devices.

[0030] When any of the phone or any of the user's associated devices enters the normal privacy protection mechanism or the hidden privacy protection mechanism, the other devices will also enter the same privacy protection mechanism.

[0031] Furthermore, the preprocessing of the collected raw data to extract touch behavior feature parameters includes:

[0032] Calculate the touch pressure intensity according to the following formula (1):

[0033] ;

[0034] in, Touch pressure intensity; The total number of touch points on the mobile phone; For the first time on mobile phones Pressure on each touch point; For the first time on mobile phones The pressure weight of each touch point is calculated according to the following formula (2):

[0035] ;

[0036] in, The preset spatial distribution adjustment parameters; For the first The distance between each touch point and the touch center of the phone is calculated according to the following formula (3):

[0037] ;

[0038] in,( ) is the first The coordinates of each touch point; The coordinates of the phone's touch center;

[0039] The touch frequency is calculated according to the following formula (4):

[0040] ;

[0041] in, Touch frequency; This represents the total number of frequency components of the touch signal, reflecting the number of frequency components obtained after the Discrete Fourier Transform converts the touch signal from the time domain to the frequency domain. Frequency components The magnitude of the amplitude is calculated according to the following formula (5):

[0042] ;

[0043] in, The results of the Discrete Fourier Transform provide the frequency components of the touch signal in the frequency domain and their complex representations.

[0044] The touch path data is calculated according to the following formula (6):

[0045] ;

[0046] in, The touch path data reflects the average curvature of the path, indicating the complexity of the path. Indicates the number of touch points traversed in the touch path; The first step in the touch path The coordinates of each touch point; The first step in the touch path The coordinates of each touch point; The first step in the touch path The coordinates of each touch point; The total length of the touch path is calculated according to the following formula (7):

[0047] ;

[0048] The pressure change trend data is calculated according to the following formula (8):

[0049] ;

[0050] in, This data represents the trend of pressure changes. The total number of pressure time series of touch points on the mobile phone; For the touch point at the Pressure values ​​at specific points in time; For the touch point at the Pressure values ​​at specific points in time; The average pressure of the touch point at all points in time; To prevent small-valued constants with a denominator of zero.

[0051] The beneficial effects of the technical solution provided in this application include:

[0052] (1) This invention collects raw data such as touch position, pressure distribution, force change and gesture trajectory, and combines them with touch behavior feature parameters (such as touch pressure intensity, touch frequency, touch path data and pressure change trend) to achieve dynamic comparison and recognition of user operation behavior. Compared with the traditional static authentication method, it can more accurately identify unauthorized operations, trigger the privacy protection mechanism in time, and effectively prevent unauthorized access. (2) This invention does not rely on additional hardware. By comparing touch behavior feature parameters in real time, it automatically triggers the dynamic privacy protection mechanism when an unauthorized operation is identified. The protection mechanism covers multiple modes such as dynamically blurring screen content, hiding sensitive data and restricting function access. It can be flexibly adjusted according to the needs of the scenario and is particularly suitable for complex environments such as public places or multiple people sharing the device. (3) Through real-time data collection and comparison by the built-in pressure sensor, this invention can complete the behavior feature analysis at the moment of touch operation, ensuring that the privacy protection mechanism can be triggered at the first time. Compared with the traditional solution that relies on unlock verification, this method significantly improves the real-time performance and reliability of protection. (4) This invention reduces interference with normal user operation while achieving intelligent privacy protection. Attached Figure Description

[0053] Figure 1 This is a flowchart of a mobile phone privacy protection method based on screen touch pressure sensing provided in the first embodiment of this application. Detailed Implementation

[0054] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0055] The first embodiment of this application provides a mobile phone privacy protection method based on screen touch pressure sensing. Please refer to... Figure 1 This figure is a schematic diagram of the first embodiment of this application. The following is in conjunction with... Figure 1The first embodiment of this application provides a detailed description of a mobile phone privacy protection method based on screen touch pressure sensing.

[0056] Step S101: Use the pressure sensor built into the mobile phone screen to collect raw data of the user's touch operation in real time. The raw data includes touch position, pressure distribution, force change and gesture trajectory.

[0057] Step S101 mainly involves using a pressure sensor built into the phone screen to collect raw data from the user's touch operations in real time. First, the phone screen needs to integrate a pressure sensor array capable of sensing pressure. This array is typically located on the lower layer of the touch screen or integrated with the touch layer, and can sense the touch pressure information of the user's finger on the screen in real time. Each sensor unit (or node) in the pressure sensor array can collect the pressure magnitude and obtain pressure change data during user touch through a sampling circuit.

[0058] During screen touch, the pressure sensor array records pressure changes in real time at a preset sampling frequency (e.g., 100 Hz or higher). The recorded raw data includes information in multiple dimensions: first, touch position data, where the position of each touch point can be determined by the coordinates of the activated node of the pressure sensor, usually represented by two-dimensional coordinates (x, y); second, pressure distribution data, representing the pressure changes at different locations within the touch point or touch area, typically stored in matrix form; third, force variation data, indicating the dynamic changes in pressure over time during touch, such as the increasing or decreasing trend of pressure during pressing and releasing operations; and fourth, gesture trajectory data, recording the path of the user's finger sliding on the screen through continuous position changes of the touch point.

[0059] During data acquisition, the pressure sensor's output signal is typically represented as analog voltage or current, requiring conversion to a digital signal via an analog-to-digital converter (ADC) for subsequent processing. To ensure data accuracy, the system performs basic calibration on the acquired raw data, such as eliminating sensor noise or compensating for pressure errors caused by local screen characteristics. The calibrated data is stored in real-time in the phone's cache area and provides input for subsequent feature parameter extraction steps.

[0060] In practical implementation, the sampling frequency and sensitivity of the pressure sensor can be dynamically adjusted according to the user's touch operation mode. For example, when the user performs a rapid swipe operation, the system may increase the sampling frequency to capture finer trajectory changes; while during a long press operation, the focus can be on the stability of the pressure value. In addition, to save storage space, the system can adopt an incremental storage strategy, recording only the key points of pressure value or trajectory changes.

[0061] Through the above process, step S101 ensures that multi-dimensional raw data of user touch operations can be acquired accurately and in real time. This data provides the foundation for subsequent feature extraction and behavior analysis steps, and supports the implementation of intelligent privacy protection functions.

[0062] Step S102: Preprocess the collected raw data and extract touch behavior feature parameters, including touch pressure intensity, touch frequency, touch path data and pressure change trend data.

[0063] Step S102 involves preprocessing the raw data collected in step S101 and further extracting touch behavior feature parameters to support subsequent behavior comparison. This step specifically includes three parts: data cleaning, feature extraction, and structured storage.

[0064] First, the raw data is cleaned. Since the raw data collected in step S101 may contain noise or outliers (e.g., data points caused by environmental interference, sensor errors, or unintentional user touch), the system needs to process it using filtering and correction techniques. For touch position data, trajectory smoothing algorithms (such as moving averages or Gaussian filtering) can be used to eliminate discontinuous offset points; for pressure distribution data, linear or non-linear corrections can be performed using a sensor calibration matrix to ensure the accuracy of pressure values. To improve system processing efficiency, a dynamic noise detection mechanism can be designed to remove or interpolate abnormal data points (e.g., pressure values ​​significantly exceeding the normal range or discontinuous trajectories).

[0065] After data cleaning, the feature extraction stage begins. This step extracts touch behavior feature parameters including touch pressure intensity, touch frequency, touch path data, and pressure change trend data. For touch pressure intensity, the weighted average or maximum value of pressure values ​​within the touch area can be calculated to comprehensively assess the user's pressure intensity. Touch frequency can be calculated based on the number of effective touches per unit time, and Fourier transform can be used to perform frequency domain analysis of the touch signal to extract the main frequency components. Touch path data generates complete trajectory information based on the time-series coordinates of the touch point, typically including features such as path length, trajectory curvature, and touch speed. For pressure change trends, the rate of change and fluctuation amplitude can be extracted by analyzing the pressure value's curve over time, thus reflecting the dynamic characteristics of touch behavior.

[0066] Finally, the extracted touch behavior feature parameters need to be stored in a structured manner to facilitate subsequent comparison and analysis. These parameters can be combined in the form of multi-dimensional feature vectors and stored as a set of indexable data entries in chronological order. To improve query efficiency, the data can be normalized during storage, for example, mapping parameters such as pressure value, frequency, and path length to the same numerical range. In addition, a caching mechanism can be designed to prioritize storing the feature parameters of the most recent touch operation in an efficient temporary storage area to support the needs of real-time analysis.

[0067] Through the above steps, the system completes comprehensive preprocessing and extraction from raw data to behavioral feature parameters.

[0068] Furthermore, the preprocessing of the collected raw data to extract touch behavior feature parameters includes:

[0069] Calculate the touch pressure intensity according to the following formula (1):

[0070] ;

[0071] in, Touch pressure intensity; The total number of touch points on the mobile phone; For the first time on mobile phones Pressure on each touch point; For the first time on mobile phones The pressure weight of each touch point is calculated according to the following formula (2):

[0072] ;

[0073] in, The preset spatial distribution adjustment parameters; For the first The distance between each touch point and the touch center of the phone is calculated according to the following formula (3):

[0074] ;

[0075] in,( ) is the first The coordinates of each touch point; The coordinates of the phone's touch center;

[0076] The touch frequency is calculated according to the following formula (4):

[0077] ;

[0078] in, Touch frequency; This represents the total number of frequency components of the touch signal, reflecting the number of frequency components obtained after the Discrete Fourier Transform converts the touch signal from the time domain to the frequency domain. Frequency components The magnitude of the amplitude is calculated according to the following formula (5):

[0079] ;

[0080] in, The results of the Discrete Fourier Transform provide the frequency components of the touch signal in the frequency domain and their complex representations.

[0081] The touch path data is calculated according to the following formula (6):

[0082] ;

[0083] in, The touch path data reflects the average curvature of the path, indicating the complexity of the path. Indicates the number of touch points traversed in the touch path; The first step in the touch path The coordinates of each touch point; The first step in the touch path The coordinates of each touch point; The first step in the touch path The coordinates of each touch point; The total length of the touch path is calculated according to the following formula (7):

[0084] ;

[0085] The pressure change trend data is calculated according to the following formula (8):

[0086] ;

[0087] in, This data represents the trend of pressure changes. The total number of pressure time series of touch points on the mobile phone; For the touch point at the Pressure values ​​at specific points in time; For the touch point at the Pressure values ​​at specific points in time; The average pressure of the touch point at all points in time; To prevent small-valued constants with a denominator of zero.

[0088] In formula (1), It is the first The pressure value of each touch point is measured in Newtons (N). This pressure is collected in real time by a pressure sensor built into the screen, which typically assigns a standardized pressure value to each touch point.

[0089] It is the first The weight value of each touch point is used to represent the contribution of that point to the overall pressure intensity, and is calculated according to formula (2).

[0090] In formula (2), This is a parameter that adjusts the weight distribution; the recommended value is 1 / 10 of the screen diagonal length, expressed in pixels. It determines the contribution of touch points farther from the screen center to the overall pressure intensity.

[0091] In formula (3), ( ) is the first The coordinates of each touch point, in pixels, are collected by the screen touch layer. These are the coordinates of the screen's geometric center point, typically set to half the screen's length and width by default.

[0092] Touch frequency The frequency characteristics of touch operation per unit time are represented by frequency domain analysis and calculated using formula (4).

[0093] In formula (4), The total number of frequency components of the touch signal is calculated using the Discrete Fourier Transform (DFT). For the first Each frequency component is measured in Hertz (Hz). for The amplitude of each frequency component is calculated using formula (5). In formula (5), for The corresponding complex number represents the amplitude. By converting the touch signal from the time domain to the frequency domain using the Discrete Fourier Transform, the frequency characteristics of the user's operation can be accurately extracted. A sampling frequency of 100 Hz is recommended to ensure the accuracy of the frequency domain components.

[0094] Touch path data Formula (6) is used to reflect the complexity of the user's operation trajectory and represents the average curvature of the path.

[0095] In formula (6), This represents the total number of touch points in the touch path. For the first in the path The coordinates of each touch point are in pixels. The total length of the touch path is calculated according to formula (7).

[0096] Pressure change trend data The dynamic characteristics of pressure changing over time are represented by formula (8). In formula (8), This represents the total number of data points in the pressure time series. and For the first The and the first Pressure values ​​at specific points in time. The average pressure value is calculated using the following formula:

[0097] ;

[0098] To prevent small-valued constants with a denominator of zero, the recommended value is [value to be filled in]. .

[0099] Step S103: Based on the extracted touch behavior feature parameters, compare them with the target touch behavior feature parameters of authorized users pre-stored in the mobile phone.

[0100] Step S103 involves comparing the current touch behavior feature parameters extracted in step S102 with the authorized user target touch behavior feature parameters pre-stored in the mobile phone to determine whether the current operation conforms to the authorized user's behavior characteristics. This step requires several specific implementation processes, including feature parameter matching, similarity calculation, and judgment logic.

[0101] First, the current touch behavior characteristic parameters are matched with the authorized user's target characteristic parameters. The target characteristic parameters stored in the phone are obtained through training or setting based on the authorized user's historical touch data, and typically include features such as touch pressure intensity, touch frequency, touch path data, and pressure change trends. The system needs to match these two feature sets in a multi-dimensional space, ensuring that data from all dimensions are included in the matching range. For example, the current touch pressure intensity will be compared with the authorized user's stored pressure intensity range or average value; the current touch path data needs to be matched with the target path features for trajectory shape and curvature.

[0102] Next, the system needs to calculate the similarity between the current feature and the target feature. For each feature dimension, such as touch pressure intensity, the difference between the two can be calculated and compared with a preset tolerance range. For touch path data, path similarity algorithms, such as the Dynamic Time Warping (DTW) method for trajectory comparison, can be used to calculate the degree of matching between the two paths. Pressure change trends can be analyzed through time series analysis to determine the consistency of the two sets of data in terms of pressure value change rate and fluctuation pattern. Touch frequency can be determined by comparing frequency domain features to determine whether the main frequencies of the two are similar.

[0103] To comprehensively evaluate the similarity across all feature dimensions, the system combines the comparison results from multiple dimensions into a single overall matching score. For example, a weighted average can be used to normalize the similarity values ​​of each dimension and then calculate a comprehensive score. The weights can be adjusted based on the importance of each feature in the identification process; for instance, higher weights can be assigned to stress intensity and path features, while lower weights can be assigned to frequency variation and trend features.

[0104] Finally, the system compares the overall score with a preset similarity threshold. If the score is higher than the threshold, it means that the current touch behavior matches the authorized user's target touch behavior, and the system allows normal operation; if the score is lower than the threshold, the system determines that the current touch behavior is abnormal and proceeds to the next step of the privacy protection mechanism.

[0105] Through the above process, step S103 achieves an accurate comparison between the current touch behavior and the authorized user's target touch behavior.

[0106] Furthermore, the comparison of the extracted touch behavior feature parameters with the target touch behavior feature parameters of authorized users pre-stored in the mobile phone includes:

[0107] Construct the joint vector U of touch behavior features according to the following formula (9):

[0108] ;

[0109] in, For dynamic weighting factors, the following formulas (10)-(13) are used for calculation:

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] in, Represents a sequence Standard deviation; Represents a sequence The mean; To prevent small constants with a denominator of zero; Represents a sequence Standard deviation; Represents a sequence The mean; Represents a sequence Standard deviation; Represents a sequence The mean; Represents a sequence Standard deviation; Represents a sequence The mean;

[0115] The similarity is calculated according to the following formula (14):

[0116] ;

[0117] in, The similarity between the extracted touch behavior feature parameters and the pre-stored target touch behavior feature parameters of authorized users; The joint vector of the current touch behavior is created according to formula (9); The joint vector for authorized user touch behavior is created according to formula (9); Represents the joint vector The One component; The joint vector is represented by the symbol. The One component; For the first Each weighting coefficient;

[0118] when When the current touch behavior characteristic is greater than or equal to the specified threshold, it is determined that the current touch behavior characteristic is consistent with the target characteristic of the authorized user; when If the touch behavior is below the specified threshold, the current touch behavior is determined to be abnormal, triggering the privacy protection mechanism.

[0119] In formula (9), The touch pressure intensity is calculated according to the aforementioned formula (1) and reflects the average pressure level when the user touches the screen. The touch frequency reflects the speed characteristics of the user's touch operation. It is extracted from the time series data using the aforementioned formula (4) and the discrete Fourier transform. The average curvature of the touch path reflects the complexity of the user's operation trajectory. It is usually calculated through the geometric relationship of spatial coordinates and can be calculated using formula (6). The dynamic characteristics of pressure change over time are described to capture the continuity and fluctuation patterns of user-applied pressure, and can be calculated using formula (8).

[0120] Formula (10)-Formula (13), Represents specific feature parameters The standard deviation is used to measure the dispersion of data. Represents specific feature parameters The mean reflects the central trend of the data. It is a small constant to prevent the denominator from being zero; the recommended value is [value missing]. .

[0121] The weighting factor is dynamically adjusted by the ratio of the standard deviation to the mean to ensure that higher weights are given to feature parameters with greater volatility, thereby improving the sensitivity of the comparison.

[0122] Similarity Calculated according to formula (14), in formula (14):

[0123] It is a joint vector of the target touch behavior of the authorized user, created by formula (9), based on the historical behavior feature data of the authorized user.

[0124] It is the Euclidean distance of the joint vector, used to measure the overall difference between two behavioral features.

[0125] It is the joint vector of the th The absolute difference of each component is used to further refine the contribution of each component.

[0126] It is the first The weight coefficients of each component are adjusted according to the importance of the features, for example... .

[0127] when If the value is greater than or equal to the set threshold (recommended value is 0.8), the behavioral characteristics are considered consistent; if it is less than the threshold, the privacy protection mechanism is triggered.

[0128] This embodiment effectively integrates multiple touch feature parameters by constructing joint vectors, and adapts to the complexity of different user behaviors by dynamically adjusting weighting factors. Simultaneously, by introducing a similarity calculation method combining Euclidean distance and component differences, it provides a comprehensive quantitative analysis of behavioral features.

[0129] Step S104: When the comparison result shows that the current touch behavior characteristics do not match the authorized user's behavior characteristics, a normal privacy protection mechanism is triggered, wherein the normal privacy protection mechanism includes at least one of the following:

[0130] Dynamically blur sensitive content on the screen;

[0131] Hide preset key data or functional modules;

[0132] Restrict access to specific functions on the phone.

[0133] Step S104 involves triggering a standard privacy protection mechanism when the comparison results show that the current touch behavior characteristics do not match the authorized user's behavior characteristics. The specific implementation of this step includes determining the activation conditions of the protection mechanism, selecting and implementing specific protection measures, and managing the status of the protection mechanism.

[0134] When the system determines that the current touch behavior does not match the target behavior characteristics of the authorized user, it immediately triggers a preset privacy protection mechanism. First, the system triggers the activation conditions of the protection mechanism through an anomaly judgment signal of the comparison result. For example, when the overall matching score is lower than a set threshold, the system will generate a protection trigger signal, which controls the dynamic adjustment of screen content and the protection strategy for sensitive data.

[0135] During the execution of the protection mechanism, the system selects an appropriate protection method based on the user's protection settings. These typically include dynamically blurring screen content, hiding critical data or functional modules, and restricting access to specific functions. For dynamic blurring of screen content, the system blurs content marked as sensitive (such as message notifications, payment information, or important documents) in the screen display area. Blurring can be achieved by reducing the transparency of these areas or using algorithms to blur the images, ensuring that sensitive content is unidentifiable while preserving basic functional displays in other areas of the screen, such as time or battery level.

[0136] When hiding critical data or functional modules, the system uses software control to conceal application modules, folders, or data content marked as sensitive. This can be achieved by adjusting the application interface layout or directly freezing the display of sensitive functions, such as automatically hiding payment applications or important shortcuts on the screen in certain situations. Furthermore, for user message content, the system can choose to display only the source while hiding the specific content, thereby preventing unauthorized users from obtaining critical information.

[0137] To restrict access to specific phone functions, the system will temporarily limit access to certain functions through the permission management module. This may include prohibiting access to the camera, recording device, payment functions, or other function areas marked as private by the user. Simultaneously, the system can run protection logic in the background to increase the scope or intensity of function restrictions if abnormal behavior persists.

[0138] The status of standard privacy protection mechanisms is dynamically managed based on user actions. For example, when a user re-enters authentication information (such as fingerprint, password, or facial recognition), the system will deactivate the protection mechanism and restore the normal display of screen content and functional modules. To prevent accidental triggering, the protection mechanism can be set to allow the user to verify their identity for a short period of time, while gradually increasing the protection strength if no verification is performed.

[0139] Through the above process, step S104 realizes the complete process from abnormal behavior judgment to protection mechanism triggering and execution, ensuring the security of user sensitive data and functions.

[0140] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0141] Provide users with control options that allow them to choose between a standard privacy protection mechanism and a hidden privacy protection mechanism;

[0142] If the user selects the normal privacy protection mechanism, the normal privacy protection mechanism will be triggered when the comparison results show that the current touch behavior characteristics do not match the authorized user's behavior characteristics.

[0143] If the user selects the covert privacy protection mechanism, the covert privacy protection mechanism will be triggered when the comparison result shows that the current touch behavior characteristics do not match the authorized user's behavior characteristics. The covert privacy protection mechanism includes at least one of adjusting the interface response speed, delaying the touch response time, and reducing the screen brightness.

[0144] This embodiment provides a mobile phone privacy protection method based on screen touch pressure sensing. By providing users with flexible control options, users can choose between ordinary privacy protection mechanisms or covert privacy protection mechanisms according to their actual needs, and dynamically trigger corresponding privacy protection strategies based on different touch behavior characteristics matching results.

[0145] In practical applications, the system first provides users with the ability to select control options through a user interface. This interface can be integrated into the phone's privacy settings module or presented to the user as an initial step when configuring privacy protection functions for the first time. Users can select between a standard privacy protection mechanism and a hidden privacy protection mechanism through radio buttons or swipe gestures. To improve the user-friendliness and comprehensibility of the interaction, the system can provide a brief description or simulated animation for each mechanism. For example, the standard privacy protection mechanism emphasizes direct protection of sensitive content, while the hidden privacy protection mechanism focuses on reducing the risk of information exposure without attracting attention.

[0146] After the user completes their selection, the system records the user's choice as a preference setting and dynamically invokes the corresponding protection mechanism during the comparison of touch behavior characteristic parameters with the authorized user's target characteristics. When the user selects the normal privacy protection mechanism, if the comparison result shows that the current touch behavior characteristics do not match the authorized user's behavior characteristics, the system will immediately trigger the normal privacy protection mechanism. This mechanism includes, but is not limited to, dynamically blurring sensitive content on the screen, hiding preset key data or functional modules, and restricting access permissions for specific functions. For example, when abnormal touch behavior is detected, the system can quickly blur sensitive content displayed on the screen, such as chat messages or payment data. Blurring can be achieved in various ways, including reducing image sharpness and applying Gaussian blur algorithms. At the same time, the system can hide important shortcuts or application modules marked by the user, such as payment applications or personal folders, to prevent unauthorized access. In addition, the system can also restrict access to certain functions that require advanced permissions by adjusting operation permissions, such as prohibiting unauthorized users from entering the payment interface or accessing system settings.

[0147] For users who choose the covert privacy protection mechanism, the system will trigger the covert protection strategy when it detects abnormal touch behavior, making the protection process less likely to be noticed by unauthorized users. One measure is to adjust the interface response speed. By artificially delaying the opening speed of applications or the response time of operations, unauthorized users will not be aware of the existence of the protection mechanism, but in practice, the possibility of obtaining sensitive information is reduced. For example, the system can extend the loading time or display a prompt animation when clicking on a sensitive application to divert attention. At the same time, delaying the touch response time is an effective means. The system can introduce a small delay when processing touch signals, such as slightly delaying the touch feedback after the user swipes or clicks. This subtle change will not significantly affect the user experience of normal users, but will increase the difficulty of operation for unauthorized users.

[0148] Reducing screen brightness is a core strategy for covert protection. When the system detects abnormal user behavior, it can gradually lower the screen brightness, making sensitive content difficult to discern. For example, in public places, the system can dynamically adjust brightness based on ambient light sensor input, making it difficult for bystanders to obtain key information even when the screen content is within their view. Furthermore, screen brightness adjustment can be combined with other operations, such as blurring or reducing the display area of ​​sensitive regions, to further enhance the protection effect.

[0149] The system also allows users to customize specific parameters of the privacy protection mechanism, such as setting the minimum threshold for screen brightness, the latency of interface response speed, and the latency of touch response. Through these customizations, users can flexibly adjust the strength of privacy protection according to their personal usage habits and security needs. Furthermore, the system can automatically optimize these parameters based on the user's historical behavior records. For example, by analyzing the user's touch frequency and pressure change patterns, it can predict possible usage scenarios and dynamically adjust the default privacy protection configuration.

[0150] The system also provides a dynamic switching capability between the standard privacy protection mechanism and the covert privacy protection mechanism. For example, when the user does not explicitly specify a mode, the system can automatically recommend a suitable protection mode based on environmental parameters (such as light intensity, device posture, or operation frequency). For example, when the system detects that the phone has been moved quickly or tilted at an abnormal angle, it can enable the covert protection mechanism by default to protect user privacy to the greatest extent without interrupting normal operation.

[0151] This embodiment provides a new way to protect mobile phone privacy through flexible control options, diverse protection measures, and dynamic adaptation to user behavior.

[0152] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0153] When the angle of the phone changes beyond a preset threshold, or when sensors, including gyroscopes and accelerometers, detect that the phone has been suddenly moved, flipped, or picked up, the normal privacy protection mechanism or the covert privacy protection mechanism is triggered.

[0154] Alternatively, when the phone detects a sound in the environment, and the direction of the sound source is not in the direction of the user's line of sight, it triggers a normal privacy protection mechanism or a hidden privacy protection mechanism.

[0155] First, the phone monitors the device's attitude and motion in real time using its built-in gyroscope and accelerometer. When the device's angle changes, the system calculates the difference between the current angle and the previous angle. If the angle change exceeds a preset threshold, the device's attitude is deemed abnormal. Specifically, the gyroscope outputs the device's rotation angle at fixed time intervals, such as pitch, roll, and yaw. The rotation amplitude is calculated by the difference between two consecutive sampling points. If the rotation amplitude exceeds a set threshold (e.g., 30 degrees or other suitable values), a privacy protection mechanism is triggered. Furthermore, combined with the accelerometer's output, the system can detect if the device has been suddenly moved or rapidly flipped. For example, if a user's phone is picked up or dropped while lying flat on a table, the system can detect rapid changes in gravitational acceleration (e.g., acceleration changes exceeding a certain range within a short period) to identify abnormal actions.

[0156] Once the triggering conditions are met, the system will activate either a standard privacy protection mechanism or a covert privacy protection mechanism based on the user's selected protection mode. If the standard privacy protection mechanism is selected, the system will immediately take protective measures, such as dynamically blurring screen content, hiding sensitive information, or locking device functions. If the covert protection mechanism is selected, the system may trigger protection by reducing screen brightness or delaying response, while avoiding significant interference with user operations. For example, when the device is flipped, the screen brightness may be gradually reduced instead of being turned off abruptly, creating the illusion of normal operation.

[0157] On the other hand, this embodiment also expands the triggering conditions of the privacy protection mechanism by combining the direction of sound and the user's gaze direction for multi-dimensional analysis. When the device detects abnormal sounds in the environment, the system first collects sound signals through the built-in microphone and determines the direction of the sound source using a sound source localization algorithm. Sound source localization can be achieved by analyzing the time difference of arrival (TDOA) of sound. For example, in a multi-microphone array, the system can measure the time delay of sound reaching each microphone to calculate the azimuth angle of the sound source. At the same time, the system can estimate the user's gaze direction by combining the phone's camera or front-facing sensor, for example, by analyzing the orientation of the user's face using a facial detection algorithm.

[0158] If the detected sound source direction deviates significantly from the user's line of sight (e.g., greater than 30 degrees or within the set tolerance range), the system will determine that there may be a risk of unauthorized personnel approaching or spying in the current environment, triggering the corresponding privacy protection mechanism. This function is particularly useful in public places, such as when a user is using their mobile phone in a subway or coffee shop and someone suddenly makes a sound behind or to their side; the system can promptly identify the sound and trigger the protection action.

[0159] When performing protection actions, the system will respond differently depending on the mode selected by the user. For example, a normal privacy protection mechanism may immediately lock the screen and hide sensitive content, while a covert protection mechanism may delay screen content updates, reduce screen brightness, or gradually blur sensitive areas, while allowing the user to continue performing basic operations. To avoid accidental triggering, the system can be designed with a protection delay window, meaning that after a sound signal is detected, protection will only be triggered if it persists for a certain period of time or if the sound direction deviation persists.

[0160] To enhance adaptability, this embodiment also allows users to adjust the triggering conditions of the protection mechanism according to their own usage needs. For example, users can set a threshold for gyroscope angle changes or acceleration changes, and can also adjust the tolerance range of sound deviation according to the usage scenario. In addition, the system can dynamically adjust these parameters based on environmental data, such as reducing the sensitivity to sound direction deviation in noisy environments.

[0161] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0162] Provide users with personalized trigger condition settings options, which are used to trigger the ordinary privacy protection mechanism or the covert privacy protection mechanism. The personalized touch settings options include long-pressing a specific area of ​​the screen, performing a sliding operation with a specific pressure value, or repeatedly tapping the screen within a predetermined time.

[0163] When a user-defined personalized trigger condition is detected, either the normal privacy protection mechanism or the covert privacy protection mechanism is activated.

[0164] This embodiment provides personalized trigger condition settings, allowing users to customize the conditions for triggering ordinary privacy protection mechanisms or covert privacy protection mechanisms according to their own needs and operating habits, thereby protecting privacy while taking into account the flexibility and adaptability of operation.

[0165] The system first provides user interaction options in the phone's privacy settings interface for configuring personalized trigger conditions. These options can be presented in the form of clear text descriptions, graphical interfaces, or dynamic previews, allowing users to intuitively understand the specific operations and effects of different trigger conditions. For example, users can choose to trigger the privacy protection mechanism by long-pressing a specific area of ​​the screen. The system will then guide the user to set the touch area of ​​the screen and the threshold for the long-press duration. The long-press area can be a fixed location on the screen (such as a corner) or a dynamically specified location, while the long-press duration threshold can be adjusted via a slider, for example, set to two or three seconds. The system uses a pressure sensor to detect the stability and duration of the long-press operation to ensure the accuracy of the trigger.

[0166] Another personalized trigger condition is achieved through a swipe operation with a specific pressure value. Users can select the target pressure range and swipe path in the settings interface, for example, specifying the swipe direction as left to right, and requiring the applied pressure to remain within a certain range (e.g., a pressure sensitivity value of 200 to 300 grams). The system can determine whether the user's operation conforms to the set swipe path and pressure range by monitoring changes in touch pressure and trajectory data in real time. To avoid false triggers, the system can also combine swipe speed and trajectory smoothness as additional judgment conditions.

[0167] In addition, users can choose to trigger the privacy protection mechanism by repeatedly tapping the screen multiple times within a predetermined time period. For example, a user can set three consecutive taps on a specific area of ​​the screen within two seconds as the trigger condition. The system uses a pressure sensor to detect whether the force of each touch is below a set threshold (e.g., 50 grams), and combines the time interval and the number of touches to determine whether the trigger condition is met. In practice, the system records the timestamp of each touch and counts the number of valid touches within a predetermined time window. The protection mechanism is only triggered when all conditions are met simultaneously. This approach is particularly suitable for scenarios requiring rapid activation of privacy protection functions, such as when a user suddenly perceives a privacy threat in a public place, allowing for quick activation of protective measures.

[0168] When a user-defined personalized trigger condition is detected, the system activates the corresponding privacy protection mechanism based on the user's selected protection mode. If the user selects the standard privacy protection mechanism, the system will immediately execute preset protection operations, such as dynamically blurring screen content, hiding sensitive data, or restricting function access. If the user selects a covert privacy protection mechanism, the system may protect user privacy through covert methods such as gradually reducing screen brightness, delaying touch response, or adjusting interface loading speed. The system can also optimize the specific execution of protection measures based on current environmental data (such as light intensity or device orientation). For example, when the phone is detected in a low-light environment, the system can prioritize reducing screen brightness, while blurring the screen under normal lighting conditions.

[0169] To further enhance the practicality of personalized trigger conditions, the system allows users to save multiple sets of trigger conditions and set priorities for different scenarios. For example, users can set a higher trigger sensitivity for public places, such as triggering privacy protection with a single swipe; while in a home environment, the sensitivity can be reduced, activating the protection mechanism only when a specific area is long-pressed. The system can also automatically optimize trigger condition settings by learning users' daily operating habits and behavioral patterns. For instance, based on historical data analysis of users' touch behavior characteristics at different times or locations, the system dynamically adjusts the parameters of the trigger conditions, making the protection mechanism more closely aligned with users' actual needs.

[0170] This embodiment significantly enhances the flexibility of the privacy protection mechanism and the user experience by providing personalized trigger condition settings. At the same time, it improves the intelligence level of privacy protection by combining user-defined touch operations with comprehensive analysis of environmental parameters.

[0171] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0172] After the normal privacy protection mechanism is triggered, the characteristic data of abnormal touch behavior is recorded, including touch pressure intensity, touch frequency and touch path;

[0173] Determine whether abnormal touch behavior belongs to a repetitive pattern;

[0174] If the abnormal touch behavior is a repetitive pattern, the facial feature data, fingerprint feature data, and geolocation feature data of the current touch user are recorded, and the recorded feature data is sent to the authorized user.

[0175] When the standard privacy protection mechanism is triggered, the system automatically initiates data logging for abnormal touch behavior. The recorded touch characteristic data includes touch pressure intensity, touch frequency, and touch path, all extracted from real-time data collected by pressure sensors and the touchscreen. Touch pressure intensity is the pressure value applied by the user in each touch operation; the system records the pressure magnitude of each touch over time to reflect changes in the strength of the user's pressure. Touch frequency refers to the number of effective touches per unit of time, typically calculated by counting the number of touch events within a certain time window, used to analyze the user's operational rhythm. Touch path data records the movement trajectory of the user's finger on the screen, including coordinate changes of consecutive touch points, reflecting the direction and complexity of the user's operations. This characteristic data is structured and stored in the device's secure storage area to support subsequent analysis.

[0176] After recording abnormal touch behavior data, the system initiates a judgment logic to analyze whether the touch behavior belongs to a repetitive pattern. The determination of a repetitive pattern relies on previously stored abnormal touch data. The system compares the current behavior with historical records, primarily focusing on the range of touch pressure intensity variation, the similarity of touch frequency, and the similarity of touch path trajectories. For example, if the pressure intensity of multiple abnormal touch operations falls within a similar numerical range, the touch frequency remains consistent, and the shape and direction of the touch path on the screen are similar, it can be determined as a repetitive pattern. To improve the accuracy of the judgment, the system can introduce a fuzzy matching algorithm to tolerate deviations in path data, while setting a similarity threshold to ensure that only highly similar behavior patterns are identified as repetitive.

[0177] Once the abnormal touch behavior is determined to be a recurring pattern, the system will further initiate an extended data collection process to help authorized users gain a more comprehensive understanding of potential threats. This extended data includes the current touch user's facial feature data, fingerprint feature data, and geolocation feature data. Facial feature data is obtained by capturing the user's facial image in real time through the front-facing camera and extracting key feature points, such as the geometric relationships of the eyes, bridge of the nose, and mouth, using facial recognition algorithms. This data is stored or transmitted after encryption. Fingerprint feature data is collected through the fingerprint sensor under the screen, extracting the touch user's fingerprint ridge information for comparison with the fingerprint template of authorized users. Geolocation feature data obtains the current device's geographical location information through GPS or Wi-Fi signals and stores it in conjunction with a timestamp to facilitate locating the location of potential threats.

[0178] The collected feature data will be sent to the authorized user's device via a secure transmission protocol. For example, the system can send facial features, fingerprint features, and geolocation information to the authorized user's mobile phone or pre-linked email address via encrypted push notification service. This information can not only help authorized users identify potential unauthorized operations, but also serve as a basis for security audits, allowing for further action when necessary, such as contacting law enforcement agencies or strengthening device security settings.

[0179] By recording data on abnormal touch behavior and analyzing whether it belongs to a recurring pattern, the embodiment can provide users with more detailed information after a phone is lost, which helps in finding the lost phone.

[0180] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0181] Establish a data synchronization connection between the mobile phone and the user's associated devices, including smartwatches or tablets using the same account; the data synchronization connection includes sharing the target touch behavior feature parameters of the authorized user pre-stored in the mobile phone to other devices.

[0182] When any of the phone or any of the user's associated devices enters the normal privacy protection mechanism or the hidden privacy protection mechanism, the other devices will also enter the same privacy protection mechanism.

[0183] This embodiment establishes a data synchronization connection between the mobile phone and the user's associated devices, realizing a cross-device collaborative privacy protection mechanism. By sharing the authorized user's target touch behavior characteristic parameters pre-stored in the mobile phone with other associated devices, such as smartwatches or tablets, the unified triggering and execution of the privacy protection mechanism is achieved. This method can create linkage between multiple devices, effectively improving the scope and depth of user privacy protection.

[0184] In implementation, the synchronization connection between the mobile phone and associated devices is established through device authentication using the same account. Associated devices can include smartwatches, tablets, or other smart devices bound to the same cloud service account. The synchronization connection can be established using wireless networks (such as Wi-Fi or cellular data), Bluetooth, or Ultra-Wideband (UWB) technology to ensure stable and secure data transmission between devices. During synchronization, the system verifies whether the associated device is authorized, for example, by confirming its identity through account passwords, device pairing codes, or biometric verification.

[0185] Once the connection is established, the system shares the authorized user's target touch behavior characteristics stored on the phone with the associated device. These characteristics include, but are not limited to, touch pressure intensity, touch frequency, touch path data, and pressure change trends. Data sharing employs encrypted transmission protocols, such as AES or TLS, to prevent interception or tampering during transmission. The shared data is stored in a secure storage area on the associated device for use in touch behavior analysis. This data synchronization ensures that all associated devices have the same behavior characteristic recognition capabilities, eliminating the need for individual settings on each device by the user.

[0186] When a mobile phone or any connected device detects abnormal touch behavior and enters either the normal privacy protection mechanism or the covert privacy protection mechanism, other connected devices will receive the trigger signal through synchronous connection and automatically enter the same privacy protection mechanism. For example, when a mobile phone detects unauthorized user touch behavior and triggers the normal privacy protection mechanism, the connected tablet can simultaneously enter a protected state, hiding sensitive content on the screen or restricting access to specific functions. Similarly, if a smartwatch detects abnormal operation and triggers the covert privacy protection mechanism, the mobile phone will also respond by entering the same covert protection state, such as reducing screen brightness or delaying touch response.

[0187] A significant advantage of this interconnected mechanism is that privacy protection remains consistent even when users are using different devices. For example, when a user processes sensitive data on a tablet, sensitive functions or notifications on the phone and smartwatch will also receive the same protection, thus preventing privacy leaks. In scenarios where multiple devices are used simultaneously, such as when a user is presenting content on a tablet while taking notes on their phone during a meeting, this feature effectively prevents privacy vulnerabilities caused by device separation.

[0188] To further enhance the intelligence and adaptability of the system, the data synchronization connection can be dynamically adjusted according to the geographical location or usage status of the device. For example, when the mobile phone and the smart watch are in close proximity, real-time synchronization can be maintained; while when the devices are far apart (such as beyond the Bluetooth range or in different network environments), the protection mechanism can be triggered through cloud service relay. In addition, the system can also allow users to customize linkage rules, such as setting certain devices as "active devices", and only when these devices enter the privacy protection mechanism, other devices will respond in a linked manner.

[0189] During design, the synchronous execution of the privacy protection mechanism also needs to consider the functional differences of devices. For example, for a smart watch, privacy protection may mainly manifest as the screen going off or blocking message notifications, while for a tablet, it can include hiding the currently displayed documents or dynamically blurring the screen content. When the system triggers the synchronization mechanism, it will select the most suitable protection measure according to the characteristics of the device.

[0190] By establishing a data synchronization connection and implementing cross-device linked privacy protection, this embodiment improves the scope and efficiency of privacy protection, especially suitable for the increasing multi-device usage scenarios of users.

[0191] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing further includes:

[0192] Detecting the number of users and the line of sight direction in front of the screen through the front camera;

[0193] When it is detected that the line of sight direction of the user deviates from the screen or multiple line of sight targets appear in front of the screen, the general privacy protection mechanism is automatically triggered.

[0194] This embodiment proposes an intelligent detection mechanism based on the front camera to enhance the mobile phone privacy protection function. By real-time analyzing the number of users in front of the screen and the line of sight direction, it can effectively identify whether the user is focused on screen operations or whether there are other potential peepers. When it is detected that the line of sight direction deviates from the screen or multiple line of sight targets appear in front of the screen, the system can automatically trigger the general privacy protection mechanism, thus preventing sensitive information from being snooped by unauthorized users.

[0195] In its implementation, the system utilizes a front-facing camera to capture image data in front of the screen in real time and identifies the user's facial information in the image using a face detection algorithm. Specifically, the front-facing camera collects video frame data at fixed time intervals (e.g., 10 frames per second). This data is input into an embedded face detection module, which can quickly locate facial regions in the image based on common deep learning models (e.g., Haar cascades or convolutional neural networks). For detected facial regions, the system further extracts key facial feature points, such as the positions of the eyes, nose, and mouth, and calculates the user's gaze direction using these feature points.

[0196] The calculation of gaze direction is based on facial geometry, such as estimating the user's head orientation by analyzing the relative positions of the two eyes and the bridge of the nose. If the system detects that the user's gaze direction deviates significantly from the screen, such as looking to the side or downwards, it determines that the user is not currently focused on the screen. To avoid false positives, the system can set a deviation threshold; the privacy protection mechanism will only be triggered when the gaze angle exceeds this threshold (e.g., 30 degrees) for a sustained period of time.

[0197] Furthermore, the system also determines whether there are multiple targets looking at the screen by counting the number of faces in the image. If the front-facing camera detects multiple faces and all of these faces are looking towards the screen, the system can infer that the screen content may be being viewed by others. For example, in public places such as subways or cafes, strangers next to a user may be interested in the screen content while the user is using their phone. By combining the analysis of the number of faces and the direction of gaze, the system can quickly trigger normal privacy protection mechanisms after detecting multiple targets looking at the screen.

[0198] The implementation of common privacy protection mechanisms includes various strategies, and specific measures can be flexibly selected based on user settings and scenario needs. For example, the system can immediately and dynamically blur sensitive content on the screen, making it difficult to identify; or it can hide specific data modules, such as chat logs or payment interfaces. Furthermore, the system can further protect privacy by locking the screen, prompting the user to adjust their gaze, or exiting the current application. For instance, when the system detects that the user's gaze has deviated from the screen, it can display a reminder window, prompting the user to refocus on the screen to deactivate the protection.

[0199] To improve detection accuracy and user experience, the system can also combine data from other sensors for joint analysis. For example, a light sensor can detect the current ambient brightness. If the ambient light is too dim, it may affect the camera's detection performance. In this case, the system can reduce the detection frequency or temporarily disable privacy protection features. For the detection of multiple line-of-sight targets, the system can further distinguish users from background interference using a depth camera or infrared camera, such as eliminating false face detection results (e.g., posters or screen reflections).

[0200] In addition, the system offers user-defined parameter configuration options. For example, users can set the angle threshold for line-of-sight deviation from the screen or the trigger sensitivity for multiple line-of-sight targets to suit different needs in different scenarios. For instance, in a home environment, users may want to reduce sensitivity to line-of-sight deviations, while in public places they may require stricter privacy protection settings.

[0201] Through the above mechanism, this embodiment can effectively enhance the privacy protection capabilities of mobile phones, especially in public places or multi-person environments. This technical solution not only enables intelligent monitoring of user operation status but also dynamically adjusts privacy protection measures based on real-time detection results, thereby ensuring the security of sensitive information.

[0202] Furthermore, the aforementioned mobile phone privacy protection method based on screen touch pressure sensing also includes:

[0203] The ambient light sensor collects the ambient light intensity in real time and determines whether the light intensity is lower than a preset intensity threshold.

[0204] When the light intensity is below the intensity threshold, the low-light privacy protection mode is activated;

[0205] In low-light privacy protection mode, the sensitivity of touch behavior feature parameter comparison is reduced, thereby reducing the false triggering of the privacy protection mechanism when the user performs touch operations.

[0206] After activating the low-light privacy protection mode, the screen brightness is reduced to decrease the possibility that the screen content will be visible to the outside world in low-light conditions.

[0207] When a user confirms their identity through a preset verification method, the low-light privacy protection mode is deactivated, including canceling the triggered normal privacy protection mechanism and adjusting the screen brightness to the default level.

[0208] This embodiment implements a dynamic privacy protection method in low-light environments by monitoring real-time light intensity using an ambient light sensor. This method can intelligently identify low-light scenes and improve the adaptability and effectiveness of privacy protection through a series of optimization measures, while ensuring a normal user experience.

[0209] The ambient light sensor monitors the light intensity around the phone in real time and converts it into a digital signal. The system periodically reads the sensor's data to obtain the current light intensity value. If the light intensity is lower than a preset threshold (e.g., 5 lux, used to determine dim environments), the system determines that the device is in a low-light environment and automatically activates low-light privacy protection mode. The preset threshold can be adjusted according to different scenario needs. For example, for dimly lit indoor scenarios and nighttime use, users can choose a lower threshold to suit specific conditions.

[0210] Once the low-light privacy protection mode is activated, the system dynamically adjusts the sensitivity of touch behavior feature parameter comparison. This adjustment is designed to adapt to special operating scenarios in low-light environments, preventing accidental triggering of the privacy protection mechanism due to minor deviations in touch operations. For example, in low-light conditions, user operations may result in unstable trajectories or abnormal changes in touch pressure values ​​due to insufficient light. The system ensures that normal user operations are not misjudged as abnormal behavior by appropriately widening the tolerance range of feature parameter comparison, such as increasing the acceptable range of pressure value changes or reducing the reliance on trajectory accuracy. Furthermore, the adjustment of comparison sensitivity can be achieved through dynamic weighting, for example, reducing the weight of touch frequency parameters in low-light mode and relying more on the stability of pressure change trends and path data.

[0211] Meanwhile, the system also optimizes screen brightness to reduce the likelihood of screen content being visible in low-light environments. The brightness adjustment is typically gradual to avoid sudden visual shock to the user. For example, when low-light privacy protection mode is activated, the system can gradually reduce the screen brightness to the default 30% level while maintaining basic visibility to meet user operation needs. To further enhance privacy protection, the system can also incorporate dynamic blurring, blurring only sensitive areas, such as obscuring message notifications or hiding payment information, rather than completely blurring the entire screen content. This zoned protection strategy effectively protects privacy while ensuring user convenience in low-light environments.

[0212] Low-light privacy protection mode relies to some extent on user interaction to ensure the accuracy of privacy protection measures. When the user completes the operation and confirms their identity through a preset verification method, such as fingerprint, facial recognition, or password, the system automatically deactivates low-light privacy protection mode and restores the device to normal operation. The restoration process includes canceling the normal privacy protection mechanism, restoring the default sensitivity for touch behavior feature parameter comparison, and gradually adjusting the screen brightness back to the user's original settings. For example, if the user sets the screen brightness to 70% in normal mode, the system will smoothly restore the brightness to that level after authentication.

[0213] To further enhance the practicality and user experience of the low-light privacy protection mode, the system offers personalized settings options, allowing users to customize protection strategies in low-light environments. For example, users can adjust the light intensity threshold, the minimum screen brightness level, and the range of comparison sensitivity adjustments according to their individual needs. Users can also choose whether to enable dynamic blurring or achieve privacy protection solely through brightness adjustment. Furthermore, the system can automatically optimize low-light mode parameter settings based on user habits. For instance, by analyzing common user operating patterns when using the phone at night, it prioritizes protecting sensitive content frequently used by the user in low-light mode.

[0214] By introducing a low-light privacy protection mode, this embodiment solves the problem that traditional privacy protection mechanisms are prone to accidental triggering in low-light environments. At the same time, through screen brightness optimization and dynamic comparison adjustment, it achieves intelligent privacy protection and scene adaptability.

[0215] A second embodiment of this application provides an electronic device, the electronic device comprising:

[0216] processor;

[0217] The memory is used to store a program, which, when read and executed by the processor, performs a mobile phone privacy protection method based on screen touch pressure sensing provided in the first embodiment of this application.

[0218] The third embodiment of this application provides a computer-readable storage medium storing a computer program thereon. When the program is executed by a processor, it performs a mobile phone privacy protection method based on screen touch pressure sensing provided in the first embodiment of this application.

[0219] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

Claims

1. A mobile phone privacy protection method based on screen touch pressure sensing, characterized in that, include: The system utilizes a pressure sensor built into the phone screen to collect raw data from the user's touch operation in real time. The raw data includes touch position, pressure distribution, force change, and gesture trajectory. The collected raw data is preprocessed to extract touch behavior feature parameters, which include touch pressure intensity, touch frequency, touch path data and pressure change trend data. Based on the extracted touch behavior feature parameters, compare them with the target touch behavior feature parameters of authorized users pre-stored in the mobile phone; When the comparison result shows that the current touch behavior characteristics do not match the authorized user's behavior characteristics, a general privacy protection mechanism is triggered, which includes at least one of the following: Dynamically blur sensitive content on the screen; Hide preset key data or functional modules; Restrict access to specific phone functions; The ambient light sensor collects the ambient light intensity in real time and determines whether the light intensity is lower than a preset intensity threshold. When the light intensity is below the intensity threshold, the low-light privacy protection mode is activated; In low-light privacy protection mode, the sensitivity of touch behavior feature parameter comparison is reduced, thereby reducing the false triggering of the privacy protection mechanism when the user performs touch operations. After activating the low-light privacy protection mode, the screen brightness is reduced to decrease the possibility that the screen content will be visible to the outside world in low-light conditions. When a user confirms their identity through a preset verification method, the low-light privacy protection mode is deactivated, including canceling the triggered normal privacy protection mechanism and adjusting the screen brightness to the default level.

2. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that, Also includes: Provide users with control options that allow them to choose between a standard privacy protection mechanism and a hidden privacy protection mechanism; If the user selects the normal privacy protection mechanism, the normal privacy protection mechanism will be triggered when the comparison results show that the current touch behavior characteristics do not match the authorized user's behavior characteristics. If the user selects the covert privacy protection mechanism, the covert privacy protection mechanism will be triggered when the comparison result shows that the current touch behavior characteristics do not match the authorized user's behavior characteristics. The covert privacy protection mechanism includes at least one of adjusting the interface response speed, delaying the touch response time, and reducing the screen brightness.

3. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 2, characterized in that, Also includes: When the angle of the phone changes beyond a preset threshold, or when sensors, including gyroscopes and accelerometers, detect that the phone has been suddenly moved, flipped, or picked up, the normal privacy protection mechanism or the covert privacy protection mechanism is triggered. Alternatively, when the phone detects a sound in the environment, and the direction of the sound source is not in the direction of the user's line of sight, it triggers a normal privacy protection mechanism or a hidden privacy protection mechanism.

4. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that, Also includes: Provide users with personalized trigger condition settings options, which are used to trigger the ordinary privacy protection mechanism or the covert privacy protection mechanism. The personalized touch settings options include long-pressing a specific area of ​​the screen, performing a sliding operation with a specific pressure value, or repeatedly tapping the screen within a predetermined time. When a user-defined personalized trigger condition is detected, either the normal privacy protection mechanism or the covert privacy protection mechanism is activated.

5. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that, Also includes: After the normal privacy protection mechanism is triggered, the characteristic data of abnormal touch behavior is recorded, including touch pressure intensity, touch frequency and touch path; Determine whether abnormal touch behavior belongs to a repetitive pattern; If the abnormal touch behavior is a repetitive pattern, the facial feature data, fingerprint feature data, and geolocation feature data of the current touch user are recorded, and the recorded feature data is sent to the authorized user.

6. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 2, characterized in that, Also includes: Establish a data synchronization connection between the mobile phone and the user's associated devices, including smartwatches or tablets using the same account; the data synchronization connection includes sharing the target touch behavior feature parameters of the authorized user pre-stored in the mobile phone to other devices. When any of the phone or any of the user's associated devices enters the normal privacy protection mechanism or the hidden privacy protection mechanism, the other devices will also enter the same privacy protection mechanism.

7. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that, Also includes: The front-facing camera is used to detect the number of users in front of the screen and the direction of their gaze. When the system detects that the user's gaze is off-center from the screen or that multiple objects are visible in front of the screen, the system automatically triggers the standard privacy protection mechanism.

8. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that, The preprocessing of the collected raw data to extract touch behavior feature parameters includes: Calculate the touch pressure intensity according to the following formula (1): ； in, Touch pressure intensity; The total number of touch points on the mobile phone; For the first time on mobile phones Pressure on each touch point; For the first time on mobile phones The pressure weight of each touch point is calculated according to the following formula (2): ； in, The preset spatial distribution adjustment parameters; For the first The distance between each touch point and the touch center of the phone is calculated according to the following formula (3): ； in,( ) is the first The coordinates of each touch point; The coordinates of the phone's touch center; The touch frequency is calculated according to the following formula (4): ； in, Touch frequency; This represents the total number of frequency components of the touch signal, reflecting the number of frequency components obtained after the Discrete Fourier Transform converts the touch signal from the time domain to the frequency domain. Frequency components The magnitude of the amplitude is calculated according to the following formula (5): ； in, The results of the Discrete Fourier Transform provide the frequency components of the touch signal in the frequency domain and their complex representations. The touch path data is calculated according to the following formula (6): ； in, The touch path data reflects the average curvature of the path, indicating the complexity of the path. Indicates the number of touch points traversed in the touch path; The first step in the touch path The coordinates of each touch point; The first step in the touch path The coordinates of each touch point; The first step in the touch path The coordinates of each touch point; The total length of the touch path is calculated according to the following formula (7): ； The pressure change trend data is calculated according to the following formula (8): ； in, This data represents the trend of pressure changes. The total number of pressure time series of touch points on the mobile phone; For the touch point at the Pressure values ​​at specific points in time; For the touch point at the Pressure values ​​at specific points in time; The average pressure of the touch point at all points in time; To prevent small-valued constants with a denominator of zero.

9. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 8, characterized in that, The comparison of the extracted touch behavior feature parameters with the target touch behavior feature parameters of authorized users pre-stored in the mobile phone includes: Construct the joint vector U of touch behavior features according to the following formula (9): ； in, For dynamic weighting factors, the following formulas (10)-(13) are used for calculation: ； ； ； ； in, Represents a sequence Standard deviation; Represents a sequence The mean; To prevent small constants with a denominator of zero; Represents a sequence Standard deviation; Represents a sequence The mean; Represents a sequence Standard deviation; Represents a sequence The mean; Represents a sequence Standard deviation; Represents a sequence The mean; The similarity is calculated according to the following formula (14): ； in, The similarity between the extracted touch behavior feature parameters and the pre-stored target touch behavior feature parameters of authorized users; The joint vector of the current touch behavior is created according to formula (9); The joint vector for authorized user touch behavior is created according to formula (9); Represents the joint vector The One component; The joint vector is represented by the symbol. The One component; For the first Each weighting coefficient; when When the current touch behavior characteristic is greater than or equal to the specified threshold, it is determined that the current touch behavior characteristic is consistent with the target characteristic of the authorized user; when If the touch behavior is below the specified threshold, the current touch behavior is determined to be abnormal, triggering the privacy protection mechanism.

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