Mobile phone privacy protection method based on screen touch pressure induction

Abstract

The invention discloses a mobile phone privacy protection method based on screen touch pressure induction. The method comprises the following steps: acquiring original data during touch operation of a user in real time through a pressure sensor arranged in a mobile phone screen; preprocessing the collected original data, and extracting touch behavior characteristic parameters; based on the extracted touch behavior characteristic parameters, comparing the extracted touch behavior characteristic parameters with authorized user target touch behavior characteristic parameters pre-stored in the mobile phone; when the comparison result shows that the current touch behavior characteristics are not consistent with the authorized user behavior characteristics, triggering a common privacy protection mechanism; the common privacy protection mechanism comprises at least one of dynamic fuzzification of sensitive content on a screen, hiding of preset key data or function modules and limitation of access authority of a specific function; according to the invention, by dynamically comparing the touch behavior characteristics of the user, the unauthorized access can be intelligently judged and prevented, the privacy and data security of the user are effectively protected, and the security and flexibility of the use of the mobile phone are improved at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile phones, and in particular to a mobile phone privacy protection method based on screen touch pressure sensing. Background Art

[0002] With the widespread use of smartphones, user privacy and data security have become increasingly prominent concerns. Existing mobile privacy protection methods primarily include static authentication methods like passwords, fingerprints, and facial recognition, as well as traditional approaches such as software encryption or hiding sensitive data. While these technologies can prevent unauthorized access to a certain extent, they generally rely on a single biometric feature or a fixed verification process, making them difficult to adapt to users' dynamic privacy protection needs in complex scenarios.

[0003] However, these existing technologies face numerous application challenges. First, static authentication methods are susceptible to environmental constraints or external interference in practice, such as facial recognition failure in low light and fingerprint recognition anomalies when fingers are wet. Furthermore, traditional data encryption and hiding methods lack real-time and dynamic capabilities. Beyond user-initiated verification or settings, they struggle to automatically detect and adapt to varying privacy protection requirements. For example, if a user's phone is manipulated by an unauthorized person, the system struggles to respond promptly and effectively protect sensitive data.

[0004] Based on the above problems, the present 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: The pressure sensor built into the phone screen collects raw data of the user's touch operation in real time, including touch position, pressure distribution, force change and gesture trajectory; Preprocessing the collected raw data to extract touch behavior characteristic parameters, wherein the touch behavior characteristic parameters include touch pressure intensity, touch frequency, touch path data, and pressure change trend data; Based on the extracted touch behavior characteristic parameters, the extracted touch behavior characteristic parameters are compared with the target touch behavior characteristic parameters of the authorized user pre-stored in the mobile phone; When the comparison result shows that the current touch behavior characteristics do not match the authorized user 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; Limit access to specific features on your phone.

[0007] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: Providing a control option to the user, wherein the control option is used to allow the user to select a normal privacy protection mechanism or a covert privacy protection mechanism; If the user selects the normal privacy protection mechanism, the normal privacy protection mechanism will be triggered when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics; If the user selects a covert privacy protection mechanism, when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics, the covert privacy protection mechanism is triggered. 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.

[0008] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: When the angle of the phone changes beyond a preset threshold, or when sensors such as the gyroscope and accelerometer detect that the phone is suddenly moved, flipped, or picked up, the normal privacy protection mechanism or the covert privacy protection mechanism is triggered; Alternatively, when the mobile phone detects a sound in the environment and the direction of the sound source is inconsistent with the line of sight of the mobile phone user, the ordinary privacy protection mechanism or the hidden privacy protection mechanism is triggered.

[0009] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: Providing the user with personalized trigger condition setting options, wherein the personalized trigger condition options are used to trigger the general privacy protection mechanism or the hidden privacy protection mechanism, wherein the personalized touch setting options include long pressing a specific area of the screen, sliding with a specific pressure value, or tapping the screen multiple times in a predetermined time; When a user-defined personalized trigger condition is detected, the general privacy protection mechanism or the covert privacy protection mechanism is activated.

[0010] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing 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 the abnormal touch behavior belongs to a repetitive pattern; If the abnormal touch behavior belongs to a repetitive pattern, the facial feature data, fingerprint feature data and geographic location feature data of the current touch operation user are recorded, and the recorded feature data are sent to the authorized user.

[0011] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: Establishing a data synchronization connection between the mobile phone and the user's associated devices, wherein the associated devices include a smartwatch or tablet computer using the same account; the data synchronization connection includes sharing the authorized user's target touch behavior characteristic parameters pre-stored in the mobile phone with other devices; When any of the mobile phone or the user's associated devices enters the normal privacy protection mechanism or the hidden privacy protection mechanism, the other devices also enter the same privacy protection mechanism.

[0012] Furthermore, the pre-processing of the collected raw data to extract touch behavior characteristic parameters includes: Calculate the touch pressure intensity according to the following formula (1): ; in, is the touch pressure intensity; The total number of touch points on the phone; For mobile phone The pressure of each touch point; For mobile phone The pressure calculation weight value of each touch point is calculated according to the following formula (2): ; in, Adjust parameters for the preset spatial distribution; For the The distance between each touch point and the touch center of the phone is calculated according to the following formula (3): ; in,( ) is the The coordinates of the touch points; The coordinates of the touch center of the phone; The touch frequency is calculated according to the following formula (4): ; in, is the touch frequency; 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; is the frequency component The amplitude of is calculated according to the following formula (5): ; in, The discrete Fourier transform result provides the frequency components of the touch signal in the frequency domain and its complex representation; The touch path data is calculated according to the following formula (6): ; in, It is the touch path data, reflecting the average curvature of the path and indicating the complexity of the path; Indicates the number of touch points passed in the touch path; The first The coordinates of the touch points; The first The coordinates of the touch points; The first The coordinates of the touch points; is the total length of the touch path, calculated according to the following formula (7): ; The pressure change trend data is calculated according to the following formula (8): ; in, It is the pressure change trend data; is the total number of pressure time series of touch points on the phone; For the touch point The pressure value at each time point; For the touch point The pressure value at each time point; is the average pressure of the touch point at all time points; A decimal constant to prevent the denominator from being zero.

[0013] The beneficial effects of the technical solution provided by this application include: (1) The present invention collects raw data such as touch position, pressure distribution, force change and gesture trajectory, and combines it with touch behavior characteristic parameters (such as touch pressure intensity, touch frequency, touch path data and pressure change trend) to achieve dynamic comparison and identification of user operation behavior. Compared with traditional static authentication methods, it can more accurately identify unauthorized operations, trigger the privacy protection mechanism in time, and effectively prevent unauthorized access. (2) The present invention does not rely on additional hardware. By comparing touch behavior characteristic parameters in real time, when unauthorized operations are identified, the dynamic privacy protection mechanism is automatically triggered. The protection mechanism covers multiple modes such as dynamic blurring of screen content, hiding sensitive data and restricting function access. It can be flexibly adjusted according to scene requirements and is particularly suitable for complex environments such as public places or multiple people sharing equipment. (3) Through real-time data collection and comparison of built-in pressure sensors, the present invention can complete behavioral feature analysis at the moment of touch operation, ensuring that the privacy protection mechanism can be triggered in the first time. Compared with traditional solutions that rely on unlocking verification, this method significantly improves the real-time performance and reliability of protection. (4) While achieving intelligent privacy protection, the present invention reduces interference with normal user operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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 the present application. DETAILED DESCRIPTION

[0015] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of the present application. Therefore, the present application is not limited to the specific implementations disclosed below.

[0016] The first embodiment of this application provides a mobile phone privacy protection method based on screen touch pressure sensing. Figure 1 , which is a schematic diagram of the first embodiment of this application. Figure 1 The first embodiment of the present application provides a mobile phone privacy protection method based on screen touch pressure sensing, which is described in detail.

[0017] Step S101: using the pressure sensor built into the mobile phone screen to collect the original data of the user's touch operation in real time, the original data includes the touch position, pressure distribution, force change and gesture trajectory.

[0018] Step S101 primarily involves using the pressure sensors built into the phone's screen to collect raw data from user touch operations in real time. First, the phone's screen must integrate a pressure sensor array capable of sensing pressure. This array is typically located beneath the touchscreen or integrated with the touch layer, enabling real-time sensing of the pressure applied by the user's finger on the screen. Each sensor unit (or node) in the pressure sensor array can capture pressure, and a sampling circuit captures pressure change data from the user's touch.

[0019] When a user touches the screen, the pressure sensor array records changes in pressure in real time at a preset sampling frequency (e.g., 100 Hz or higher). The recorded raw data includes information from multiple dimensions: first, touch position data. The location of each touch point can be determined by the coordinates of the pressure sensor's activation node, typically expressed as two-dimensional (x, y) coordinates; second, pressure distribution data, representing the changes in pressure at different locations within the touch point or touch area, typically stored in matrix form; third, force change data, which refers to the dynamic change in pressure over time during the touch process, such as the increase or decrease in pressure during press and release operations; and fourth, gesture trajectory data, which records the path of the user's finger sliding across the screen through the continuous position changes of the touch point.

[0020] During the acquisition process, the pressure sensor's output signal is typically expressed as an analog voltage or current, which requires 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 collected raw data, such as eliminating sensor noise or compensating for pressure errors caused by local screen characteristics. This calibrated data is stored in the phone's cache in real time and serves as input for subsequent feature parameter extraction steps.

[0021] In practical implementations, the pressure sensor's sampling frequency and sensitivity can be dynamically adjusted based on the user's touch operation pattern. For example, during a quick swipe, the system might increase the sampling frequency to capture more subtle trajectory changes; during a long press, the system can focus on the stability of the pressure value. Furthermore, to conserve storage space, the system can employ an incremental storage strategy, recording only key points of pressure or trajectory changes.

[0022] Through the above process, step S101 ensures that the multi-dimensional raw data of the user's touch operation can be accurately obtained in real time. This data provides the basis for the subsequent feature extraction and behavior analysis steps, and can support the implementation of intelligent privacy protection functions.

[0023] Step S102: pre-processing the collected raw data to extract touch behavior characteristic parameters, where the touch behavior characteristic parameters include touch pressure intensity, touch frequency, touch path data, and pressure change trend data.

[0024] Step S102 involves pre-processing the raw data collected in step S101 and further extracting touch behavior feature parameters to support subsequent behavior comparison. The specific implementation of this step includes three parts: data cleaning, feature extraction, and structured storage.

[0025] First, the raw data is cleaned. Since the raw data collected in step S101 may contain noise or outliers (for example, data points caused by environmental interference, sensor errors, or unintentional user touch), the system needs to process them through filtering and correction techniques. For touch position data, a trajectory smoothing algorithm (such as moving average or Gaussian filtering) can be used to eliminate discontinuous offset points; for pressure distribution data, the data can be linearly or nonlinearly corrected using a sensor calibration matrix to ensure the accuracy of the pressure value. To improve system processing efficiency, a dynamic noise detection mechanism can be designed to eliminate or interpolate abnormal data points when they are detected (such as pressure values that clearly exceed the normal range or the trajectory is discontinuous).

[0026] After data cleaning, it enters the feature extraction stage. The touch behavior characteristic parameters extracted in this step include touch pressure intensity, touch frequency, touch path data and pressure change trend data. For touch pressure intensity, the user's pressure intensity can be comprehensively evaluated by calculating the weighted average or maximum value of the pressure value in the touch area; the touch frequency can be calculated based on the number of effective touches per unit time, and the touch signal is analyzed in the frequency domain by combining Fourier transform to extract the main frequency components. The touch path data generates complete trajectory information based on the time series coordinates of the touch point, which usually includes features such as path length, trajectory curvature and touch speed. For pressure change trends, the pressure value change curve over time can be analyzed to extract its change rate and fluctuation amplitude, thereby reflecting the dynamic characteristics of the touch behavior.

[0027] Finally, the extracted touch behavior characteristic parameters need to be stored in a structured manner to facilitate subsequent comparison and analysis. These parameters can be combined in the form of multidimensional feature vectors and stored in chronological order as a set of indexable data entries. To improve query efficiency, the data can be normalized during storage, for example, by 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 the characteristic parameters of the most recent touch operation in an efficient temporary storage area to support real-time analysis needs.

[0028] Through the above steps, the system completes the comprehensive preprocessing and extraction from raw data to behavioral feature parameters.

[0029] Furthermore, the pre-processing of the collected raw data to extract touch behavior characteristic parameters includes: Calculate the touch pressure intensity according to the following formula (1): ; in, is the touch pressure intensity; The total number of touch points on the phone; For mobile phone The pressure of each touch point; For mobile phone The pressure calculation weight value of each touch point is calculated according to the following formula (2): ; in, Adjust parameters for preset spatial distribution; For the The distance between each touch point and the touch center of the phone is calculated according to the following formula (3): ; in,( ) is the The coordinates of the touch points; The coordinates of the touch center of the phone; The touch frequency is calculated according to the following formula (4): ; in, is the touch frequency; 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; is the frequency component The amplitude of is calculated according to the following formula (5): ; in, The discrete Fourier transform result provides the frequency components of the touch signal in the frequency domain and its complex representation; The touch path data is calculated according to the following formula (6): ; in, It is the touch path data, reflecting the average curvature of the path and indicating the complexity of the path; Indicates the number of touch points passed in the touch path; The first The coordinates of the touch points; The first The coordinates of the touch points; The first The coordinates of the touch points; is the total length of the touch path; it is calculated according to the following formula (7): ; The pressure change trend data is calculated according to the following formula (8): ; in, It is the pressure change trend data; is the total number of pressure time series of touch points on the phone; For the touch point The pressure value at each time point; For the touch point The pressure value at each time point; is the average pressure of the touch point at all time points; A decimal constant to prevent the denominator from being zero.

[0030] In formula (1), It is The pressure value of each touch point is measured in Newtons (N). This is collected in real time by the built-in pressure sensor on the screen. The pressure sensor usually gives a standardized pressure value for each touch point.

[0031] It is The weight value of each touch point is used to represent the contribution of the point to the overall pressure intensity and is calculated according to formula (2).

[0032] In formula (2), This parameter 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.

[0033] In formula (3), ( ) is the The coordinates of each touch point, in pixels, are collected by the screen touch layer. The coordinates of the geometric center point of the screen, usually preset to half the length and width of the screen.

[0034] Touch frequency It represents the frequency characteristics of touch operation per unit time, obtained through frequency domain analysis and calculated using formula (4).

[0035] In formula (4), is the total number of frequency components of the touch signal, calculated by discrete Fourier transform (DFT). For the The unit of frequency is Hertz (Hz). for The amplitude of the 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 through discrete Fourier transform, the user's operation frequency characteristics can be accurately extracted. The recommended sampling frequency is 100 Hz to ensure the accuracy of the frequency domain components.

[0036] 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.

[0037] In formula (6), The total number of touch points in the touch path. The first The coordinates of the touch point in pixels. is the total length of the touch path, calculated according to formula (7).

[0038] Pressure change trend data To express the dynamic characteristics of pressure changing with time, formula (8) is used. In formula (8), is the total number of data points in the pressure time series. and For the and The pressure value at a time point. is the average value of the pressure value, calculated according to the following formula: ; To prevent decimal constants with zero denominators, the recommended value is .

[0039] Step S103: comparing the extracted touch behavior characteristic parameters with target touch behavior characteristic parameters of the authorized user pre-stored in the mobile phone.

[0040] Step S103 involves comparing the current touch behavior characteristic parameters extracted in step S102 with the authorized user's target touch behavior characteristic parameters pre-stored in the phone to determine whether the current operation meets the authorized user's behavior characteristics. This step requires multiple specific implementation processes, including characteristic parameter matching, similarity calculation, and judgment logic.

[0041] 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 trained or set using 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 multidimensional space to ensure that data from all dimensions is included in the matching range. For example, the current touch pressure intensity will be compared with the pressure intensity range or average value stored by the authorized user; the current touch path data needs to be analyzed for trajectory shape and curvature matching with the target path characteristics.

[0042] 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, a path similarity algorithm, 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 data sets in terms of pressure change rate and fluctuation pattern. Touch frequency can be determined by comparing frequency domain features to determine whether the dominant frequencies of the two are similar.

[0043] To comprehensively assess the similarity across all feature dimensions, the system combines the comparison results from multiple dimensions into an overall match score. For example, a weighted average can be used to normalize the similarity values across each dimension and calculate a single overall score. Weights can be adjusted based on the importance of each feature in the recognition process, for example, assigning higher weights to pressure intensity and path characteristics, while lower weights are assigned to frequency changes and trend characteristics.

[0044] Finally, the system compares the overall score with a preset similarity threshold. If the score is higher than the threshold, 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 enters the next step of the privacy protection mechanism triggering process.

[0045] Through the above process, step S103 realizes the accurate comparison between the current touch behavior and the target touch behavior of the authorized user.

[0046] Furthermore, the extracted touch behavior characteristic parameters are compared with target touch behavior characteristic parameters of authorized users pre-stored in the mobile phone, including: The joint vector U of touch behavior features is constructed according to the following formula (9): ; in, is the dynamic weight factor, which is calculated using the following formulas (10)-(13): ; ; ; ; in, Representation sequence The standard deviation of Representation sequence The mean of To prevent the denominator from being zero, a small constant; Representation sequence The standard deviation of Representation sequence The mean of Representation sequence The standard deviation of Representation sequence The mean of Representation sequence The standard deviation of Representation sequence The mean of The similarity is calculated according to the following formula (14): ; in, Similarity between the extracted touch behavior characteristic parameters and the pre-stored target touch behavior characteristic parameters of the authorized user; is the joint vector of the current touch behavior, created according to formula (9); is the joint vector of the authorized user’s touch behavior, created according to formula (9); Represents the joint vector No. Quantity Represents the joint vector No. Quantity For the Weight coefficients; when When it is greater than or equal to the specified threshold, it is determined that the current touch behavior characteristics are consistent with the authorized user target characteristics; when When the value is less than the specified threshold, the current touch behavior is determined to be abnormal and the privacy protection mechanism is triggered.

[0047] In formula (9), Indicates the touch pressure intensity, calculated according to the above formula (1), reflecting the average pressure level of the user during touch. is the touch frequency, which reflects the speed characteristics of the user's touch operation and is extracted from the time series data using discrete Fourier transform according to the above formula (4). It represents the average curvature of the touch path, reflecting the complexity of the user's operation trajectory. It is usually calculated through the geometric relationship of the spatial coordinates and can be calculated using formula (6). The dynamic characteristics describing the change of pressure over time are used to capture the continuity and fluctuation pattern of the user's pressure and can be calculated by formula (8).

[0048] Formula (10)-Formula (13), Represents specific feature parameters The standard deviation is used to measure the dispersion of the data. Represents specific feature parameters The mean reflects the central tendency of the data. Is a small constant to prevent the denominator from being zero. The recommended value is .

[0049] The weight 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 increasing the sensitivity of the comparison.

[0050] Similarity Calculate according to formula (14), where: is the joint vector of the authorized user’s target touch behavior, created by formula (9) based on the authorized user’s historical behavior feature data.

[0051] is the Euclidean distance of the joint vector, which is used to measure the overall difference between the two behavioral features.

[0052] is the joint vector The absolute difference of the components is used to further refine the contribution of each component.

[0053] It is The weight coefficient of each component is adjusted according to the feature importance, for example .

[0054] when If it is greater than or equal to the set threshold (recommended value is 0.8), the behavioral characteristics are judged to be consistent; if it is less than the threshold, the privacy protection mechanism is triggered.

[0055] This embodiment effectively integrates multiple touch feature parameters through the construction of a joint vector, dynamically adjusting weighting factors to accommodate the complexity of different user behaviors. Furthermore, by introducing a similarity calculation method that combines Euclidean distance and component difference, it provides a comprehensive quantitative analysis of behavioral characteristics.

[0056] Step S104: When the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics, a general privacy protection mechanism is triggered, and the general privacy protection mechanism includes at least one of the following: Dynamically blur sensitive content on the screen; Hide preset key data or functional modules; Limit access to specific features on your phone.

[0057] Step S104 involves triggering the general privacy protection mechanism when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics. The specific implementation of this step includes determining the activation conditions of the protection mechanism, selecting and executing specific protection measures, and managing the status of the protection mechanism.

[0058] When the system determines that the current touch behavior does not match the authorized user's target behavior characteristics, it immediately triggers the preset privacy protection mechanism. First, the system triggers the activation conditions of the protection mechanism by detecting an abnormality in the comparison results. For example, when the comprehensive match score falls below the set threshold, the system generates a protection trigger signal, which controls the dynamic adjustment of screen content and the protection strategy of sensitive data.

[0059] During the implementation of the protection mechanism, the system selects an appropriate protection method based on the user's protection settings. This typically includes dynamically blurring screen content, hiding key 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. This blurring can be done by reducing the transparency of these areas or using an algorithm to blur the image, ensuring that sensitive content cannot be identified while retaining basic functional displays in other areas of the screen, such as the time or battery level.

[0060] 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 layout of the application interface or directly freezing the display of sensitive functions, such as automatically hiding the payment application or important on-screen shortcuts in certain situations. Furthermore, for user message content, the system can choose to only display the source while hiding the specific content, thereby preventing unauthorized users from accessing critical information.

[0061] To restrict access to specific phone features, the system will temporarily restrict access to certain functions through the permissions management module. This can include disabling the camera, recording device, payment functions, or accessing areas marked as private by other users. Simultaneously, the system can run background protection logic to increase the scope or intensity of functional restrictions if abnormal behavior persists.

[0062] The status of standard privacy protection mechanisms is dynamically managed based on user actions. For example, when a user re-enters their authentication information (such as fingerprint, password, or facial recognition), the system disengages the protection mechanism, restoring normal display of screen content and functional modules. To prevent false triggering, the protection mechanism can be configured to allow users to verify their identity for a short period of time, while gradually increasing the protection level if verification is not performed.

[0063] Through the above process, step S104 implements the complete flow from abnormal behavior determination to protection mechanism triggering and execution, ensuring the security of user sensitive data and functions.

[0064] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: Providing a control option to the user, wherein the control option is used to allow the user to select a normal privacy protection mechanism or a covert privacy protection mechanism; If the user selects the normal privacy protection mechanism, the normal privacy protection mechanism will be triggered when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics; If the user selects a covert privacy protection mechanism, when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics, the covert privacy protection mechanism is triggered. 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.

[0065] 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 a general privacy protection mechanism or a hidden privacy protection mechanism according to actual needs, and dynamically trigger corresponding privacy protection strategies based on different touch behavior feature matching results.

[0066] 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 users as an initial step when first configuring privacy protection features. Users can select either a standard privacy protection mechanism or a covert privacy protection mechanism by selecting a single option or sliding. To improve the user-friendliness and understanding 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 covert privacy protection mechanism focuses on reducing the risk of information exposure without attracting attention.

[0067] After the user completes their selection, the system records the user's choice as a preference and dynamically invokes the corresponding protection mechanism when comparing the touch behavior characteristic parameters with the authorized user's target characteristics. If the user selects the standard privacy protection mechanism, and the comparison results show that the current touch behavior characteristics do not match those of the authorized user, the system immediately triggers the standard 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 to 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 through various methods, including reducing image clarity and applying a Gaussian blur algorithm. The system can also hide important shortcuts or application modules marked by the user, such as payment applications or personal folders, to prevent unauthorized access. Furthermore, the system can restrict access to certain functions that require advanced permissions by adjusting operating permissions, such as prohibiting unauthorized users from accessing the payment interface or system settings.

[0068] 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 noticeable to unauthorized users. One of the measures is to adjust the interface response speed. By artificially delaying the opening speed of the application or the operation response, unauthorized users will not be aware of the existence of the protection mechanism, but in practice it reduces the possibility of obtaining sensitive information. For example, when clicking on a sensitive application, the system can extend the loading time or display a prompt animation 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 the touch signal, such as slightly delaying the touch feedback after the user slides or clicks. This subtle change does not significantly affect the user experience of normal users, but increases the difficulty of operation for unauthorized users.

[0069] Reducing screen brightness is a core strategy for covert protection. When the system determines that user behavior is abnormal, it can gradually lower the screen brightness, making sensitive content difficult to discern. For example, in public places, the system can dynamically adjust the brightness based on input from the ambient light sensor, making it difficult for onlookers to obtain key information even if the screen content is within visible range. In addition, screen brightness adjustment can be combined with other operations, such as blurring or reducing the display range of sensitive areas, to further enhance protection.

[0070] The system also allows users to customize the specific parameters of the privacy protection mechanism, such as setting the minimum threshold for screen brightness, the delay time for interface response speed, and the delay amplitude for touch response. Through this customized setting, users can flexibly adjust the intensity of privacy protection based on their personal usage habits and security needs. In addition, 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, predicting possible usage scenarios, and dynamically adjusting the default privacy protection configuration.

[0071] The system also provides the ability to dynamically switch between standard and covert privacy protection mechanisms. For example, if the user doesn't explicitly specify a mode, the system can automatically recommend an appropriate protection mode based on environmental parameters such as light intensity, device posture, or frequency of operation. For example, if the phone is detected to be moved quickly or tilted at an abnormal angle, the covert protection mechanism can be enabled by default to maximize user privacy without interrupting normal operation.

[0072] This embodiment provides a new way to implement privacy protection for mobile phones through flexible control options, diverse protection measures, and dynamic adaptation of user behavior.

[0073] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: When the angle of the phone changes beyond a preset threshold, or when sensors such as the gyroscope and accelerometer detect that the phone is suddenly moved, flipped, or picked up, the normal privacy protection mechanism or the covert privacy protection mechanism is triggered; Alternatively, when the mobile phone detects a sound in the environment and the direction of the sound source is inconsistent with the line of sight of the mobile phone user, the ordinary privacy protection mechanism or the hidden privacy protection mechanism is triggered.

[0074] First, the phone uses its built-in gyroscope and accelerometer to monitor the device's posture and motion in real time. When the device's angle changes, the system calculates the difference between the current and previous angles. If the angle change exceeds a preset threshold, the device's posture is deemed abnormal. Specifically, the gyroscope outputs the device's rotation angles, such as pitch, roll, and yaw, at fixed time intervals. The rotation amplitude is calculated by taking the difference between two consecutive sampling points. If the rotation amplitude exceeds a set threshold (such as 30 degrees or another adaptive value), the privacy protection mechanism is triggered. Furthermore, combined with the accelerometer's output, the system can detect sudden movements or rapid flips of the device, such as if the user's phone is picked up or dropped while lying flat on a table. The system can detect abnormal movements by detecting rapid changes in gravitational acceleration (e.g., acceleration changes exceeding a certain range within a short period of time).

[0075] After the trigger conditions are met, the system will activate the normal privacy protection mechanism or the covert privacy protection mechanism based on the protection mode selected by the user. If the normal privacy protection mechanism is selected, the system will immediately take protective measures, such as dynamically blurring the screen content, hiding sensitive information, or locking device functions. If the covert protection mechanism is selected, the system may trigger protection by reducing the screen brightness or delaying the response, while avoiding causing significant interference to user operations. For example, when the device is turned over, the screen brightness can be gradually reduced instead of directly turning off the screen to create the illusion of normal operation.

[0076] On the other hand, this embodiment also expands the triggering conditions of the privacy protection mechanism, performing a multi-dimensional analysis by combining the direction of sound and the direction of the phone user's line of sight. When the device detects an abnormal sound in the environment, the system first collects the sound signal through the built-in microphone and uses the sound source localization algorithm to determine the direction of the sound source. Sound source localization can be achieved by analyzing the time difference (TDOA) of the sound arriving at the device. For example, in a multi-microphone array, the system can measure the time delay of the sound arriving at each microphone to calculate the azimuth of the sound source. At the same time, the system can estimate the user's line of sight by combining the phone's camera or front sensor, for example, by analyzing the orientation of the user's face through a facial detection algorithm.

[0077] If the direction of the detected sound source deviates significantly from the user's line of sight (for example, greater than 30 degrees or within a set tolerance range), the system will determine that there is a risk of unauthorized access or peeping in the current environment and trigger the corresponding privacy protection mechanism. This feature is particularly useful in public places. For example, when a user is using their phone on the subway or in a cafe, if someone suddenly makes a sound from behind or to the side, the system can immediately identify the sound and trigger a protection action.

[0078] When executing a protection action, the system responds differently based on the mode selected by the user. For example, a standard privacy protection mechanism might immediately lock the screen and hide sensitive content, while a stealth protection mechanism might delay screen content updates, reduce screen brightness, or gradually blur sensitive areas while allowing the user to continue basic operations. To avoid false triggers, the system can design 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.

[0079] To enhance adaptability, this embodiment also allows users to adjust the triggering conditions of the protection mechanism based on their specific needs. For example, users can set the triggering threshold for gyroscope angle change or acceleration change, or adjust the tolerance range for sound deviation based on the usage scenario. Furthermore, the system can dynamically adjust these parameters based on environmental data, for example, reducing sensitivity to sound direction deviation in noisy environments.

[0080] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: Providing the user with personalized trigger condition setting options, wherein the personalized trigger condition options are used to trigger the general privacy protection mechanism or the hidden privacy protection mechanism, wherein the personalized touch setting options include long pressing a specific area of the screen, sliding with a specific pressure value, or tapping the screen multiple times in a predetermined time; When a user-defined personalized trigger condition is detected, the general privacy protection mechanism or the covert privacy protection mechanism is activated.

[0081] This embodiment provides personalized trigger condition setting options, allowing users to customize the conditions for triggering the general privacy protection mechanism or the hidden privacy protection mechanism according to their own needs and operating habits, thereby protecting privacy while taking into account the flexibility and adaptability of operations.

[0082] The system first provides user interaction options in the privacy protection settings interface of the mobile phone for configuring personalized trigger conditions. These options can be presented in the form of clear text descriptions, graphical interactive interfaces or dynamic previews, so that users can intuitively understand the specific operations and effects of different trigger conditions. For example, the user can choose to trigger the privacy protection mechanism by long pressing a specific area of the screen. At this time, the system will guide the user to set the touch range of the screen and the long press time threshold. The long press area can be a fixed position on the screen (such as a corner area) or a dynamically specified position, and the long press time threshold can be adjusted through a slider, for example, set to two seconds or three seconds. The system can detect the stability and duration of the long press operation through a pressure sensor to ensure the accuracy of the trigger.

[0083] Another personalized trigger condition is achieved by sliding with a specific pressure value. In the settings interface, users can select the target pressure range and sliding path. For example, the sliding direction is specified to be from left to right, and the pressure applied during sliding must be kept within a certain range (such as a pressure sensing value of 200 grams to 300 grams). The system can determine whether the user operation complies with the set sliding path and pressure range by monitoring the touch pressure changes and trajectory data in real time. To avoid false triggers, the system can also combine sliding speed and trajectory smoothness as additional judgment conditions.

[0084] In addition, users can also choose to trigger the privacy protection mechanism by tapping the screen multiple times in a predetermined time. For example, the user can set a trigger condition to tap a certain area of the screen three times in a row within two seconds. The system uses a pressure sensor to detect whether the force of each touch is lower than the set threshold (such as 50 grams), and combines the time interval and the number of touches to determine whether the trigger condition is met. In the specific implementation, the system will record the timestamp of each touch and count the number of valid touches within the predetermined time window. The protection mechanism will only be triggered when all conditions are met at the same time. This method is particularly suitable for scenarios where privacy protection functions need to be activated quickly. For example, when a user suddenly perceives a privacy threat in a public place, protection measures can be triggered quickly.

[0085] When a user-defined personalized trigger condition is detected, the system will activate the corresponding privacy protection mechanism based on the protection mode selected by the user. If the user selects the normal privacy protection mechanism, the system will immediately perform the preset protection operation, such as dynamically blurring the screen content, hiding sensitive data, or restricting function access; if the user selects the covert privacy protection mechanism, the system may protect user privacy by gradually reducing the screen brightness, delaying touch response, or adjusting the interface loading speed. The system can also optimize the specific implementation of the protection measures based on the current environmental data (such as light intensity or device posture). For example, when it is detected that the phone is in a low-light environment, the system can prioritize reducing the screen brightness, and choose blurring under normal light conditions.

[0086] 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 that privacy protection can be triggered with a single swipe; while in a home environment, the sensitivity can be reduced, and the protection mechanism will only be activated by long pressing a specific area. The system can also automatically optimize the trigger condition settings by learning the user's daily operating habits and behavior patterns. For example, based on historical data analysis of the user's touch behavior characteristics in different time periods or locations, the trigger condition parameters can be dynamically adjusted to make the protection mechanism more in line with the user's actual needs.

[0087] This embodiment significantly enhances the flexibility of the privacy protection mechanism and user experience by providing personalized trigger condition setting options. At the same time, it improves the intelligence level of privacy protection by combining user-defined touch operations with comprehensive analysis of environmental parameters.

[0088] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing 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 the abnormal touch behavior belongs to a repetitive pattern; If the abnormal touch behavior belongs to a repetitive pattern, the facial feature data, fingerprint feature data and geographic location feature data of the current touch operation user are recorded, and the recorded feature data are sent to the authorized user.

[0089] When the normal privacy protection mechanism is triggered, the system will automatically start the data recording function for abnormal touch behavior. The recorded touch feature data include touch pressure intensity, touch frequency and touch path. These data are extracted from the real-time acquisition results of the pressure sensor and touch screen. Touch pressure intensity is the pressure value applied by the user in each touch operation. The system will record the pressure of each touch according to the time series to reflect the changes in the strength of the user's pressure. Touch frequency refers to the number of effective touches per unit time. It is usually obtained by counting the number of touch events of the user within a certain time window and is used to analyze the user's operation rhythm. Touch path data is recorded by recording the movement trajectory of the user's finger on the screen, including the coordinate changes of continuous touch points, thereby reflecting the user's operation direction and complexity. These feature data are structured and stored in the secure storage area of the device to support subsequent analysis.

[0090] After recording the data of abnormal touch behavior, the system will start the judgment logic to analyze whether the touch behavior belongs to a repeated pattern. The judgment of repeated patterns depends on the previously stored abnormal touch data. The system will compare the current behavior with the historical records, focusing on the range of changes in touch pressure intensity, the similarity of touch frequency, and the similarity of the touch path trajectory. For example, if the pressure intensity of multiple abnormal touch operations falls within a similar numerical range, and 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 repeated pattern. In order to improve the accuracy of the judgment, the system can introduce a fuzzy matching algorithm to tolerate deviations in the path data, and set a similarity threshold to ensure that only highly similar behavior patterns are identified as repeated.

[0091] Once it is determined that the abnormal touch behavior is a repetitive pattern, the system will further initiate the process of collecting extended data to help authorized users more comprehensively understand potential threats. These extended data include facial feature data, fingerprint feature data, and geographic location feature data of the current touch operating user. Facial feature data uses the front camera to capture the operating user's facial image in real time, and uses facial recognition algorithms to extract key feature points, such as the geometric relationship of the eyes, nose bridge, and mouth. These data will be encrypted and stored or transmitted. Fingerprint feature data can be collected through the fingerprint sensor under the screen to extract the fingerprint pattern information of the touch user for comparison with the fingerprint template of the authorized user. Geographic location feature data obtains the geographic location information of the current device through GPS or Wi-Fi signals, and is stored in combination with a timestamp to facilitate locating the location of potential threats.

[0092] The collected feature data is sent to the authorized user's device via a secure transmission protocol. For example, the system can send facial features, fingerprint characteristics, and geolocation information to the authorized user's mobile phone or pre-linked email address via an encrypted push message service. This information not only helps the authorized user identify potential unauthorized operations but also serves as the basis for security audits and, if necessary, takes further action, such as contacting law enforcement or strengthening the device's security settings.

[0093] By recording data on abnormal touch behaviors and analyzing whether they belong to a repetitive pattern, the embodiment can provide more detailed information to the user after the mobile phone is lost, which helps to find the lost mobile phone.

[0094] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: Establishing a data synchronization connection between the mobile phone and the user's associated devices, wherein the associated devices include a smartwatch or tablet computer using the same account; the data synchronization connection includes sharing the authorized user's target touch behavior characteristic parameters pre-stored in the mobile phone with other devices; When any of the mobile phone or the user's associated devices enters the normal privacy protection mechanism or the hidden privacy protection mechanism, the other devices also enter the same privacy protection mechanism.

[0095] This embodiment implements a cross-device collaborative privacy protection mechanism by establishing a data synchronization connection between the mobile phone and the user's associated devices. By sharing the authorized user's target touch behavior characteristic parameters pre-stored on the mobile phone with other associated devices, such as smartwatches or tablets, the privacy protection mechanism is uniformly triggered and executed. This approach enables linkage between multiple devices, effectively increasing the scope and depth of user privacy protection.

[0096] In practice, the synchronization connection between the 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 the synchronization process, the system verifies whether the associated device is authorized, for example, by confirming its identity through an account password, device pairing code, or biometric verification.

[0097] Once the connection is established, the system will share the authorized user's target touch behavior characteristic parameters stored in the mobile phone with the associated device. These characteristic parameters include but are not limited to touch pressure intensity, touch frequency, touch path data, and pressure change trends. Data sharing uses an encrypted transmission protocol, such as AES or TLS encryption, to prevent interception or tampering during transmission. The shared data will be stored in the secure storage area of the associated device for use by the associated device when analyzing touch behavior. This data synchronization ensures that all associated devices have the same behavioral feature recognition capabilities, without the need for users to set up each device separately.

[0098] When a mobile phone or any associated device detects abnormal touch behavior and enters the normal privacy protection mechanism or the covert privacy protection mechanism, other associated devices will receive the trigger signal through a synchronous connection and automatically enter the same privacy protection mechanism. For example, when a mobile phone detects touch behavior from an unauthorized user and triggers the normal privacy protection mechanism, the associated 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.

[0099] A significant advantage of this linkage mechanism is that privacy protection remains consistent even when users are using different devices. For example, when a user is processing sensitive data on a tablet, sensitive functions or notifications on the phone and smartwatch are also protected, thus preventing privacy leaks. This feature effectively prevents privacy vulnerabilities caused by separating devices when multiple devices are used simultaneously, such as when a user is presenting content on a tablet in a meeting while taking notes on a phone.

[0100] To further enhance the system's intelligence and adaptability, data synchronization connections can be dynamically adjusted based on the device's location or usage status. For example, when a phone and smartwatch are in close proximity, real-time synchronization can be maintained. However, when the devices are farther apart (for example, outside Bluetooth range or in different network environments), the protection mechanism can be triggered through cloud services. Furthermore, the system allows users to customize linkage rules, such as setting certain devices as "active devices" so that only when these devices enter the privacy protection mechanism will other devices initiate a linkage response.

[0101] During design, the synchronization of privacy protection mechanisms must also take into account device functional differences. For example, for a smartwatch, privacy protection might primarily involve turning off the screen or blocking notifications, while for a tablet, this might include hiding the displayed document or dynamically blurring the screen content. When triggering synchronization mechanisms, the system will select the most appropriate protection measure based on the device's characteristics.

[0102] By establishing a data synchronization connection and implementing cross-device linked privacy protection, this embodiment improves the scope and efficiency of privacy protection, and is particularly suitable for users' increasing multi-device usage scenarios.

[0103] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: Detect the number of users in front of the screen and their gaze direction through the front camera; When it is detected that the user's line of sight deviates from the screen or multiple line of sight targets appear in front of the screen, the general privacy protection mechanism is automatically triggered.

[0104] This embodiment proposes an intelligent detection mechanism based on the front camera to enhance mobile phone privacy protection. By real-time analysis of the number of users in front of the screen and their gaze direction, it can effectively identify whether the user is focused on the screen operation or whether there are other potential voyeurs. If the gaze direction is detected to be away from the screen or multiple viewing targets appear in front of the screen, the system can automatically trigger the standard privacy protection mechanism, thereby preventing sensitive information from being viewed by unauthorized users.

[0105] In implementation, the system uses the front-facing camera to capture real-time image data in front of the screen and uses a face detection algorithm to identify the user's facial information in the image. Specifically, the front-facing camera collects video frame data at a fixed interval (for example, 10 frames per second). This data is input into an embedded face detection module, which can quickly locate the face area in the image based on common deep learning models (such as Haar cascades or convolutional neural networks). For the detected face area, the system further extracts key facial features, such as the location of the eyes, nose, and mouth, and uses these features to calculate the user's gaze direction.

[0106] Gaze direction is calculated based on facial geometry, for example, by analyzing the relative positions of the eyes and nose bridge to estimate the user's head orientation. If the user's gaze direction is detected to be significantly away from the screen, such as toward the side or downward, the user is deemed not to be focused on the screen. To avoid false positives, the system can set a deviation threshold. Privacy protection mechanisms are only triggered when the gaze angle exceeds this threshold (for example, 30 degrees) for a sustained period of time.

[0107] The system also counts the number of faces in the image to determine whether there are multiple gaze targets in front of the screen. If the front camera detects multiple faces and all of these faces are looking toward the screen, the system can infer that the screen content may be being viewed by others. For example, in public places such as the subway or a cafe, while a user is operating their phone, a stranger nearby may be interested in the screen content. By combining analysis of the number of faces and gaze direction, the system can quickly trigger the standard privacy protection mechanism after detecting multiple gaze targets.

[0108] The implementation of general privacy protection mechanisms includes multiple strategies, and specific measures can be flexibly selected based on user settings and scenario requirements. For example, the system can immediately dynamically blur sensitive content on the screen, making it difficult to identify; or hide specific data modules, such as chat history or payment interfaces. In addition, the system can further protect privacy by locking the screen, prompting the user to adjust their gaze, or exiting the current application. For example, when the system detects that the user's gaze has strayed from the screen, it can display a reminder window, prompting the user to refocus on the screen to release the protection state.

[0109] 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 brightness of the current environment. If the ambient light is too dark, it may affect the camera's detection effect. 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 use a depth camera or infrared camera to further distinguish between users and background interference, for example, to eliminate false face detection results (such as posters or screen reflections).

[0110] The system also offers user-defined parameter configuration options. For example, users can set the angle threshold for straying from the screen or the trigger sensitivity for multiple gaze targets to suit different scenarios. For example, in a home environment, users may want to reduce sensitivity to straying gaze, while in public places, they may require stricter privacy protection settings.

[0111] 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.

[0112] Furthermore, the mobile phone privacy protection method based on screen touch pressure sensing also includes: The ambient light sensor collects the surrounding light intensity in real time to determine whether the light intensity is lower than the preset intensity threshold; When the light intensity falls 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 to reduce false triggering of the privacy protection mechanism when the user performs touch operations; After activating low-light privacy protection mode, the screen brightness is reduced to reduce the possibility of the screen content being visible to the outside world in low-light conditions; When the user confirms his or her identity through the preset verification method, the low-light privacy protection mode is disabled, including canceling the triggered normal privacy protection mechanism and adjusting the screen brightness to the default level.

[0113] This embodiment implements a dynamic privacy protection method in low-light environments by using an ambient light sensor to monitor light intensity in real time. This method can intelligently identify low-light scenarios and improve the adaptability and effectiveness of privacy protection through a series of optimization measures, while ensuring a normal user experience.

[0114] 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 light sensor data to obtain the current light intensity value. If the light intensity falls below a preset intensity threshold (for example, 5 lux, used to determine dim environments), the system determines that the current device is in a low-light environment and automatically activates low-light privacy protection mode. The preset intensity threshold can be adjusted to meet the needs of different scenarios. For example, for dimly lit indoor scenes and nighttime use, users can select a lower threshold to adapt to specific conditions.

[0115] After the low-light privacy protection mode is activated, the system will dynamically adjust the sensitivity of the touch behavior characteristic parameter comparison. This adjustment is to adapt to special operating scenarios in low-light environments and avoid false triggering of the privacy protection mechanism due to slight deviations in touch operations. For example, in low-light conditions, the user's operation may cause unstable trajectory or abnormal changes in touch pressure values due to insufficient light. The system ensures that the user's normal operation will not be misjudged as abnormal behavior by appropriately relaxing the tolerance range of characteristic parameter comparison, such as increasing the acceptable range of pressure value changes or reducing dependence on trajectory accuracy. In addition, the adjustment of comparison sensitivity can be achieved through dynamic weighting. For example, in low-light mode, the weight of the touch frequency parameter is reduced, and more reliance is placed on the pressure change trend and the stability of the path data.

[0116] At the same time, the system will also optimize the screen brightness to reduce the possibility of the screen content being perceived by the outside world in low-light environments. The brightness adjustment process is usually carried out gradually to avoid sudden visual impact on the user. For example, when the low-light privacy protection mode is activated, the system can gradually reduce the screen brightness to a default level of 30%, while retaining basic visibility to meet user operation needs. In order to further enhance the privacy protection effect, the system can also combine dynamic blur processing to blur only sensitive areas, such as covering message notifications or hiding payment information, rather than completely blurring the entire screen content. This partition protection strategy can not only effectively protect privacy, but also ensure the user's convenience in low-light environments.

[0117] The low-light privacy protection mode relies to a certain extent on user interaction feedback to ensure the accuracy of privacy protection measures. When the user completes the operation and confirms his identity through a preset verification method, such as entering a fingerprint, facial recognition or password, the system will automatically cancel the low-light privacy protection mode and restore the device to its normal state. The recovery process includes canceling the normal privacy protection mechanism, restoring the default sensitivity of the touch behavior characteristic parameter comparison, and gradually adjusting the screen brightness back to the user's original setting level. For example, if the user sets the screen brightness to 70% in normal mode, the system will restore the brightness to that level in a smooth transition after the identity authentication is completed.

[0118] To further enhance the practicality and user experience of the low-light privacy protection mode, the system can provide personalized setting options, allowing users to customize protection strategies in low-light environments. For example, users can adjust the light intensity threshold, the minimum level of screen brightness, and the range of comparison sensitivity adjustment according to their personal needs. Users can also choose whether to enable dynamic blur processing or only achieve privacy protection through brightness adjustment. In addition, the system can automatically optimize the parameter settings of the low-light mode based on the user's usage habits. For example, by analyzing the common operating patterns of users when using mobile phones at night, sensitive content related to the user's high-frequency use can be prioritized in low-light mode.

[0119] By introducing the low-light privacy protection mode, this embodiment solves the problem that traditional privacy protection mechanisms are easily mistriggered in low-light environments. At the same time, through screen brightness optimization and dynamic comparison adjustment, it realizes intelligent privacy protection and scene adaptability.

[0120] A second embodiment of the present application provides an electronic device, comprising: processor; The memory is used to store a program. When the program is read and executed by the processor, it executes a mobile phone privacy protection method based on screen touch pressure sensing provided in the first embodiment of the present application.

[0121] The third embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, a mobile phone privacy protection method based on screen touch pressure sensing provided in the first embodiment of the present application is executed.

[0122] Although the present application is disclosed as above with the preferred embodiments, it is not intended to limit the present application. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A mobile phone privacy protection method based on screen touch pressure sensing, characterized in that: include: The pressure sensor built into the phone screen collects raw data of the user's touch operation in real time, including touch position, pressure distribution, force change and gesture trajectory; Preprocessing the collected raw data to extract touch behavior characteristic parameters, wherein the touch behavior characteristic parameters include touch pressure intensity, touch frequency, touch path data, and pressure change trend data; Based on the extracted touch behavior characteristic parameters, the extracted touch behavior characteristic parameters are compared with the target touch behavior characteristic parameters of the authorized user pre-stored in the mobile phone; When the comparison result shows that the current touch behavior characteristics do not match the authorized user 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; Limit access to specific features on your phone.

2. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that: Also includes: Providing a control option to the user, wherein the control option is used to allow the user to select a normal privacy protection mechanism or a covert privacy protection mechanism; If the user selects the normal privacy protection mechanism, the normal privacy protection mechanism will be triggered when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics; If the user selects a covert privacy protection mechanism, when the comparison result shows that the current touch behavior characteristics do not match the authorized user behavior characteristics, the covert privacy protection mechanism is triggered. 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 such as the gyroscope and accelerometer detect that the phone is suddenly moved, flipped, or picked up, the normal privacy protection mechanism or the covert privacy protection mechanism is triggered; Alternatively, when the mobile phone detects a sound in the environment and the direction of the sound source is inconsistent with the line of sight of the mobile phone user, the ordinary privacy protection mechanism or the hidden privacy protection mechanism is triggered.

4. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that: Also includes: Providing the user with personalized trigger condition setting options, wherein the personalized trigger condition options are used to trigger the general privacy protection mechanism or the hidden privacy protection mechanism, wherein the personalized touch setting options include long pressing a specific area of the screen, sliding with a specific pressure value, or tapping the screen multiple times in a predetermined time; When a user-defined personalized trigger condition is detected, the general 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 the abnormal touch behavior belongs to a repetitive pattern; If the abnormal touch behavior belongs to a repetitive pattern, the facial feature data, fingerprint feature data and geographic location feature data of the current touch operation user are recorded, and the recorded feature data are 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: Establishing a data synchronization connection between the mobile phone and the user's associated devices, wherein the associated devices include a smartwatch or tablet computer using the same account; the data synchronization connection includes sharing the authorized user's target touch behavior characteristic parameters pre-stored in the mobile phone with other devices; When any of the mobile phone or the user's associated devices enters the normal privacy protection mechanism or the hidden privacy protection mechanism, the other devices 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: Detect the number of users in front of the screen and their gaze direction through the front camera; When it is detected that the user's line of sight deviates from the screen or multiple line of sight targets appear in front of the screen, the general privacy protection mechanism is automatically triggered.

8. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that: Also includes: The ambient light sensor collects the surrounding light intensity in real time to determine whether the light intensity is lower than the preset intensity threshold; When the light intensity falls 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 to reduce false triggering of the privacy protection mechanism when the user performs touch operations; After activating low-light privacy protection mode, the screen brightness is reduced to reduce the possibility of the screen content being visible to the outside world in low-light conditions; When the user confirms his or her identity through the preset verification method, the low-light privacy protection mode is disabled, including canceling the triggered normal privacy protection mechanism and adjusting the screen brightness to the default level.

9. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 1, characterized in that: The pre-processing of the collected raw data to extract touch behavior characteristic parameters includes: Calculate the touch pressure intensity according to the following formula (1): ； in, is the touch pressure intensity; The total number of touch points on the phone; For mobile phone The pressure of each touch point; For mobile phone The pressure calculation weight value of each touch point is calculated according to the following formula (2): ； in, Adjust parameters for preset spatial distribution; For the The distance between each touch point and the touch center of the phone is calculated according to the following formula (3): ； in,( ) is the The coordinates of the touch points; The coordinates of the touch center of the phone; The touch frequency is calculated according to the following formula (4): ； in, is the touch frequency; 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; is the frequency component The amplitude of is calculated according to the following formula (5): ； in, The discrete Fourier transform result provides the frequency components of the touch signal in the frequency domain and its complex representation; The touch path data is calculated according to the following formula (6): ； in, It is the touch path data, reflecting the average curvature of the path and indicating the complexity of the path; Indicates the number of touch points passed in the touch path; The first The coordinates of the touch points; The first The coordinates of the touch points; The first The coordinates of the touch points; is the total length of the touch path, calculated according to the following formula (7): ； The pressure change trend data is calculated according to the following formula (8): ； in, It is the pressure change trend data; is the total number of pressure time series of touch points on the phone; For the touch point The pressure value at each time point; For the touch point The pressure value at each time point; is the average pressure of the touch point at all time points; A decimal constant to prevent the denominator from being zero.

10. The mobile phone privacy protection method based on screen touch pressure sensing according to claim 9, characterized in that: The extracted touch behavior characteristic parameters are compared with target touch behavior characteristic parameters of the authorized user pre-stored in the mobile phone, including: The joint vector U of touch behavior features is constructed according to the following formula (9): ； in, is the dynamic weight factor, which is calculated using the following formulas (10)-(13): ； ； ； ； in, Representation sequence The standard deviation of Representation sequence The mean of To prevent the denominator from being zero, a small constant; Representation sequence The standard deviation of Representation sequence The mean of Representation sequence The standard deviation of Representation sequence The mean of Representation sequence The standard deviation of Representation sequence The mean of The similarity is calculated according to the following formula (14): ； in, Similarity between the extracted touch behavior characteristic parameters and the pre-stored target touch behavior characteristic parameters of the authorized user; is the joint vector of the current touch behavior, created according to formula (9); is the joint vector of the authorized user’s touch behavior, created according to formula (9); Represents the joint vector No. Quantity Represents the joint vector No. Quantity For the Weight coefficients; when When it is greater than or equal to the specified threshold, it is determined that the current touch behavior characteristics are consistent with the authorized user target characteristics; when When the value is less than the specified threshold, the current touch behavior is determined to be abnormal and the privacy protection mechanism is triggered.

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