Sensitive data real-time grading rendering method and system based on multistage visual desensitization engine, terminal and storage medium
By dynamically calculating the sensitivity level of sensitive data through a multi-level visual desensitization engine, configuring desensitization parameters, and building a secure rendering channel, a hierarchical and progressive protection of sensitive data is achieved. This solves the problems of low readability and insufficient adaptability to privacy levels in existing technologies, ensuring data security and privacy protection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-04-03
AI Technical Summary
Existing sensitive data anonymization schemes have low readability and cannot adapt to the privacy level requirements of different scenarios, resulting in insufficient data security and privacy protection.
The method adopts a multi-level visual desensitization engine to collect sensitive data, identify sensitive types, calculate data levels using a privacy level dynamic calculation model, configure desensitization parameters, build a secure rendering channel, and perform off-screen rendering and secure composite display to achieve hierarchical and progressive data protection.
The data display scheme is dynamically adjusted according to the privacy level to adapt to the privacy needs of different scenarios, prevent the leakage of sensitive data, and ensure data security and readability.
Smart Images
Figure CN121786876A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mobile Internet technology, and in particular to a method, system, terminal, and storage medium for real-time hierarchical rendering of sensitive data based on a multi-level visual desensitization engine. Background Technology
[0002] In modern enterprises, with the deepening of digital transformation, data has become a core asset for businesses and institutions. Whether it's users' personal information (such as ID numbers, mobile phone numbers, and consumption records), corporate business data (such as customer lists and transaction secrets), or sensitive information in the government sector (such as population files and medical records), all contain enormous value. This sensitive information can be used for data analysis, business optimization, product development, and decision support. However, at the same time, the sensitivity and vulnerability of data are becoming increasingly prominent. Once sensitive data is leaked, it may lead to the infringement of personal privacy, damage to corporate business interests (such as core data being obtained by competitors), and even trigger a crisis of social trust. Therefore, data anonymization to protect data security is an increasingly important need, an inevitable choice for enterprises and institutions to ensure data security and maintain trust, and the foundation for sustainable development in the digital age.
[0003] In existing sensitive data anonymization solutions, a full masking method is generally used to hide sensitive information. This full masking method will destroy data readability, and static rules cannot adapt to the privacy level requirements of different scenarios (e.g., internal audit scenarios, customer display scenarios).
[0004] Therefore, this technology still needs improvement. Summary of the Invention
[0005] The technical problem to be solved by the present invention is to provide a real-time hierarchical rendering method, system, terminal and storage medium for sensitive data based on a multi-level visual desensitization engine, in order to address the shortcomings of existing sensitive data desensitization schemes, which have low readability and cannot adapt to the privacy level requirements of different scenarios.
[0006] The technical solution adopted by this invention to solve the technical problem is as follows: In a first aspect, the present invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, comprising: Collect sensitive data and identify the sensitivity type corresponding to the sensitive data; Based on the identified sensitive types, the privacy level of the sensitive data is calculated using a dynamic privacy level calculation model. Based on the privacy level, de-identification rules are matched, and de-identification parameters corresponding to the sensitive data are configured; A secure rendering channel is built based on the matching desensitization rules and the configured desensitization parameters; The sensitive data is rendered off-screen and displayed securely using the secure rendering channel.
[0007] In one implementation, the step of collecting sensitive data and identifying the sensitivity type corresponding to the sensitive data includes: The sensitive data is obtained by acquiring user information tables, transaction records, and privacy information through API interfaces or database connections; Based on the regular expression library, the system matches ID card number, mobile phone number, and name information to obtain sensitive fields in the sensitive data. Then, it combines NLP semantic analysis to identify implicit sensitive data in the sensitive data and generate a list of sensitive data types.
[0008] In one implementation, calculating the privacy level of the sensitive data using a dynamic privacy level calculation model based on the identified sensitivity type includes: Based on the aforementioned privacy level dynamic calculation model, the user role weights corresponding to the sensitive data are assigned values, and the corresponding device environment coefficients and content sensitivity benchmark scores are determined. The privacy level of the sensitive data is calculated based on the user role weight, the device environment coefficient, and the content sensitivity benchmark score.
[0009] In one implementation, the step of performing de-identification rule matching based on the privacy level and configuring the de-identification parameters corresponding to the sensitive data includes: Based on the privacy level, de-identification rules are matched to generate a privacy level-rule mapping relationship, and the de-identification parameters corresponding to the sensitive data are configured according to the real-time adjusted level style parameters. The mapping relationship between the privacy level and the rule is as follows: L0: Directly output the sensitive data; L1: Display the sensitive data using a layered style; L2: Adds a transition display effect based on L1; L3: Mask the sensitive data with text and set a hover decryption trigger condition; L4: Overlay a semi-transparent mask on top of L3; L5: Apply a dynamic blur filter to the sensitive data and overlay a dynamic watermark.
[0010] In one implementation, the step of building a secure rendering channel based on the matched desensitization rules and configured desensitization parameters includes: Initialize the main canvas layer and the off-screen canvas layer, and enable the WebGL protocol to build an independent rendering environment for obtaining the sensitive data; Based on the techniques of de-document flow and keyboard attribute hiding, the main canvas layer and the off-screen canvas layer are configured for DOM isolation in sequence, and an anti-screenshot strategy is loaded to obtain the secure rendering channel.
[0011] In one implementation, the off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel includes: The sensitive data is converted into a pixel matrix, loaded into the off-screen canvas layer through WebGL texture, and the highest level sensitive data is subjected to a blur filter shader calculation based on the Gaussian blur algorithm to obtain the off-screen preprocessed pixel data. The pixel data is extracted using the readPixels method, and watermarked RGBA image data is generated on the off-screen canvas layer. The original content in the main canvas layer is cleared, and the off-screen canvas layer is drawn on the main canvas layer using an image drawing method for secure composite display.
[0012] In one implementation, the step of performing off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel includes: The system triggers an authorization request based on the user's hovering action, obtains the biometric or password verification result corresponding to the user, and dynamically modifies the pixels in the corresponding area based on the biometric or password verification result to achieve hovering decryption. Real-time monitoring of screenshot shortcut key events; interception of abnormal interactive behaviors based on the screenshot shortcut key events; and automatic enhancement of the blurring effect of corresponding sensitive data. Obtain compliance testing information and identification efficiency statistics, and optimize the de-identification rules based on the compliance testing information and the identification efficiency statistics.
[0013] Secondly, the present invention provides a real-time hierarchical rendering system for sensitive data based on a multi-level visual desensitization engine, comprising: A sensitive data acquisition module is used to acquire sensitive data and identify the sensitive type corresponding to the sensitive data; The privacy level calculation module is used to calculate the privacy level of the sensitive data based on the identified sensitive type using a dynamic privacy level calculation model. The desensitization rule matching and visual processing module is used to perform desensitization rule matching based on the privacy level and configure the desensitization parameters corresponding to the sensitive data; The secure rendering channel building module is used to build a secure rendering channel based on the matching de-identification rules and the configured de-identification parameters. The off-screen rendering and secure display module is used to perform off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel.
[0014] Thirdly, the present invention provides a terminal, comprising: a processor and a memory, wherein the memory stores a real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine, and the real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine, when executed by the processor, is used to implement the operation of the real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine as described in the first aspect.
[0015] Fourthly, the present invention also provides a computer-readable storage medium storing a real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine. When executed by a processor, the real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine is used to implement the operation of the real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine as described in the first aspect.
[0016] The present invention, by employing the above technical solution, has the following effects: This invention collects sensitive data and identifies the corresponding sensitive types. Based on the identified sensitive types, it calculates the privacy level of the sensitive data using a dynamic privacy level calculation model. Then, based on the privacy level, it matches de-identification rules and configures de-identification parameters for the sensitive data. A secure rendering channel is built based on the matched de-identification rules and configured parameters, and this secure rendering channel is used to perform off-screen rendering and secure composite display of the sensitive data. This invention dynamically calculates the sensitivity intensity based on the privacy level, performs graded progressive de-identification of the data, and then renders it through a secure rendering channel. This allows for data protection according to the needs of different privacy scenarios, preventing the leakage of sensitive data. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.
[0018] Figure 1 This is a flowchart of the real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine in this invention.
[0019] Figure 2 This is the overall system flowchart of the present invention.
[0020] Figure 3 This is a functional schematic diagram of the terminal in one implementation of the present invention.
[0021] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0022] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0023] Exemplary methods In modern enterprises, with the deepening of digital transformation, data has become a core asset for businesses and institutions. Whether it's users' personal information (such as ID numbers, mobile phone numbers, and consumption records), corporate business data (such as customer lists and transaction secrets), or sensitive information in the government sector (such as population files and medical records), all contain enormous value. This sensitive information can be used for data analysis, business optimization, product development, and decision support. However, at the same time, the sensitivity and vulnerability of data are becoming increasingly prominent. Once sensitive data is leaked, it may lead to the infringement of personal privacy, damage to corporate business interests (such as core data being obtained by competitors), and even trigger a crisis of social trust. Therefore, data anonymization to protect data security is an increasingly important need, an inevitable choice for enterprises and institutions to ensure data security and maintain trust, and the foundation for sustainable development in the digital age.
[0024] In existing sensitive data anonymization solutions, a full masking method is generally used to hide sensitive information. This full masking method will destroy data readability, and static rules cannot adapt to the privacy level requirements of different scenarios (e.g., internal audit scenarios, customer display scenarios).
[0025] To address the above technical problems, this invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine. The method includes: collecting sensitive data and identifying the sensitivity type corresponding to the sensitive data; calculating the privacy level of the sensitive data using a dynamic privacy level calculation model based on the identified sensitivity type; matching desensitization rules based on the privacy level and configuring desensitization parameters corresponding to the sensitive data; building a secure rendering channel based on the matched desensitization rules and configured desensitization parameters; and performing off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel. This invention dynamically calculates sensitivity intensity based on privacy levels, performs hierarchical progressive desensitization of data, and then renders it through a secure rendering channel. This allows for data protection according to the needs of different privacy scenarios, preventing sensitive data leakage.
[0026] like Figure 1 As shown, this embodiment of the invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, comprising the following steps: Step S100: Collect sensitive data and identify the sensitive type corresponding to the sensitive data.
[0027] In this embodiment, a multi-level visual desensitization engine for sensitive data is provided, along with a real-time hierarchical rendering method for sensitive data based on the multi-level visual desensitization engine. This method can dynamically calculate the sensitivity intensity according to the privacy level, desensitize the data progressively by level, and then render it through a secure rendering channel to prevent the leakage of sensitive data.
[0028] Specifically, in one implementation of this embodiment, step S100 includes the following steps: Step S101: Obtain the user information table, transaction records, and privacy information through API interface or database connection to obtain the sensitive data; Step S102: Based on the regular expression library, match the ID card number, mobile phone number and name information to obtain the sensitive fields in the sensitive data, and combine NLP semantic analysis to identify the implicit sensitive data in the sensitive data to generate a list of sensitive data types.
[0029] like Figure 2 As shown, in the overall system flow of this embodiment, sensitive data is first collected. This process mainly involves accessing multi-source data in the system to identify sensitive information such as ID card / name.
[0030] In this embodiment, the specific methods for data collection and sensitive type identification are as follows: 1) Full data access: Obtain target data (such as user information tables, transaction records, and sensitive data such as privacy information) through API interfaces / database connections; during the collection of sensitive data, it supports the collection of structured data (JSON / CSV) and semi-structured data (HTML table fragments).
[0031] 2) Automatic identification of sensitive fields: This process is divided into two stages. First, based on the regular expression library, it matches information such as ID card number (e.g., 18 digits), mobile phone number (e.g., 11 digits), and name (e.g., 2-4 Chinese characters). Then, it combines NLP (Natural Language Processing) model to perform semantic analysis, identify implicit sensitive data (e.g., house number in an address), and generate a list of sensitive data types. The list of sensitive data types includes: sensitive data, sensitive fields, and corresponding sensitive data types.
[0032] In this embodiment, after identifying sensitive data types, these sensitive data also need to be preprocessed. This process mainly involves cleaning up invalid values (e.g., empty strings / abnormal formats) and standardizing field formats (e.g., names are standardized as "surname + given name") to provide a unified input format for subsequent desensitization processing.
[0033] like Figure 1 As shown, this embodiment of the invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, comprising the following steps: Step S200: Based on the identified sensitive type, calculate the privacy level of the sensitive data using a privacy level dynamic calculation model.
[0034] like Figure 2 As shown, in the overall system flow of this embodiment, after collecting sensitive data, the privacy level is dynamically determined. This process requires a comprehensive judgment based on information such as user role, device environment, and content sensitivity, and outputs a desensitization strength of 0-5 levels.
[0035] Specifically, in one implementation of this embodiment, step S200 includes the following steps: Step S201: Based on the privacy level dynamic calculation model, assign values to the user role weights corresponding to the sensitive data, and determine the corresponding device environment coefficients and content sensitivity benchmark scores. Step S202: Calculate the privacy level of the sensitive data based on the user role weight, the device environment coefficient, and the content sensitivity benchmark score.
[0036] In this embodiment, a dynamic privacy level calculation model is executed to calculate the privacy level of the sensitive data. Specifically, the method is as follows: 1) Three-dimensional parameter quantization: First, assign weights to user roles: the default base score for administrators is 1 (0-5 correspond to 0-100 points, and level 1 is 20 points), and the base score for ordinary users is 3 (i.e. 60 points). Then, calculate the device environment coefficient: in an intranet environment, the device environment coefficient is reduced by 0.5 (i.e., reduced by 10 points); in a public network environment, the public WiFi coefficient can be increased by 0.5 (i.e., increased by 10 points). Finally, calculate the content sensitivity baseline score: if the sensitive content is an ID number, the content sensitivity baseline score is 5 (i.e., 100 points); if the sensitive content is a name, the content sensitivity baseline score is 3 (i.e., 60 points); if the sensitive content is an email address, the content sensitivity baseline score is 2 (i.e., 40 points).
[0037] 2) Calculate the final privacy level according to the level calculation formula: Final level = ROUND [(user role score + device environment score + content sensitivity score) / 20], and map the result to level 0-5 (using rounding method for mapping).
[0038] like Figure 1 As shown, this embodiment of the invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, comprising the following steps: Step S300: Perform de-identification rule matching based on the privacy level, and configure the de-identification parameters corresponding to the sensitive data.
[0039] like Figure 2 As shown, in the overall system flow of this embodiment, after the privacy level is dynamically determined, a graded visual desensitization process is performed according to the determined privacy level. This process requires fading the text and using partial masking + hover secondary authorization dynamic blurring + watermarking for desensitization.
[0040] Specifically, in one implementation of this embodiment, step S300 includes the following steps: Step S301: Based on the privacy level, perform desensitization rule matching to generate a privacy level-rule mapping relationship, and configure the desensitization parameters corresponding to the sensitive data according to the real-time adjusted level style parameters.
[0041] In this embodiment, the mapping relationship between the privacy level and the rule is as follows: L0: Directly output the sensitive data; L1: Display the sensitive data using a layered style; L2: Adds a transition display effect based on L1; L3: Mask the sensitive data with text and set a hover decryption trigger condition; L4: Overlay a semi-transparent mask on top of L3; L5: Apply a dynamic blur filter to the sensitive data and overlay a dynamic watermark.
[0042] In this embodiment, the specific method of desensitization rule matching and visual processing is as follows: 1) Level-Rule Mapping: L0: Output plaintext directly (i.e., no processing); L1: Apply CSS styles (Cascading Style Sheets, a computer language used to describe the style of HTML or XML documents, which can control the presentation of web page elements through rules composed of selectors and declarations), specifically: color: #999; opacity: 0.7, to achieve font fading; L2: Adds `letter-spacing: 1px` (transition level) to L1. The `letter-spacing` property controls the spacing between characters (letters, numbers, symbols). A value of 1px means increasing the spacing by 1 pixel.
[0043] L3: Text mask: Names are displayed as "surname + first name" (e.g., "Zhang San"), and mobile phone numbers are displayed by retaining the first 3 and last 4 digits (e.g., "138****5678"). Hover decryption trigger: The element bound to the Canvas (canvas layer) has an onmouseover event (onmouseover is one of the mouse events in JavaScript, which is triggered when the mouse pointer moves to a specified HTML element). When the onmouseover event is triggered, a pop-up window requests secondary authorization for hover decryption. The secondary authorization supports verification methods such as password / fingerprint verification. L4: Overlay a semi-transparent mask on top of L3 (background: rgba(255,255,255,0.3), transition level); L5: Apply a dynamic blur filter (filter: blur(8px, supports automatic adjustment of blur radius based on device performance), and overlay a dynamic watermark (text "internal data|username@time", tilt 30, transparency 20%, slight displacement every 3 seconds to prevent watermark removal from screenshots).
[0044] 2) In addition to the above-mentioned method of matching desensitization rules, the desensitization style can also be set by dynamically configuring desensitization parameters: This means that the style parameters of each level (such as L5 blur radius and L1 transparency) can be adjusted through the back-end management system, and the configuration changes take effect in real time (without needing to restart the service).
[0045] like Figure 1 As shown, this embodiment of the invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, comprising the following steps: Step S400: Build a secure rendering channel based on the matched desensitization rules and configured desensitization parameters.
[0046] Specifically, in one implementation of this embodiment, step S400 includes the following steps: Step S401: Initialize the main canvas layer and the off-screen canvas layer, and enable the WebGL protocol to build an independent rendering environment for obtaining the sensitive data; Step S402: Based on the de-document flow technology and keyboard attribute hiding technology, DOM isolation configuration is performed on the main canvas layer and the off-screen canvas layer in sequence, and anti-screenshot strategy is loaded to obtain the secure rendering channel.
[0047] In this embodiment, the secure rendering channel is initialized using Canvas / WebGL (Web Graphics Library, a 3D graphics protocol). Specifically: 1) Setting up an independent rendering environment: First, initialize two Canvas layers: a main Canvas (configured to be visible, with the same size as the container) and an off-screen Canvas (configured to be invisible, with the same size). Then, enable the WebGL up / down (canvas.getContext('webgl',{preserveDrawingBuffer:false})) directive to disable default buffer saving; 2) DOM (Document Object Model) isolation configuration: First, configure the main Canvas to be removed from the document flow by setting `position: absolute` (a positioning method in CSS), and set the parent container to `pointer-events: none` (an event handler for mouse elements that prevents user interaction), to prevent direct manipulation of the DOM; Then, hide the Canvas's tabindex property (TabIndex is a property used in computer programming to define the tab key navigation order of form controls) to prevent content from being stolen via keyboard event focus.
[0048] Finally, an anti-screenshot strategy is implemented: a custom WebGL shader is injected to add random pixel noise during off-screen rendering (visible only to screenshot tools, not to the human eye) to achieve the function of hiding screenshots.
[0049] like Figure 1 As shown, this embodiment of the invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, comprising the following steps: Step S500: Perform off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel.
[0050] like Figure 2 As shown, in the overall system flow of this embodiment, after performing hierarchical visual desensitization processing, secure rendering is performed. This process mainly involves using an independent Canvas layer to draw sensitive data, which is then displayed securely after off-screen processing.
[0051] Specifically, in one implementation of this embodiment, step S500 includes the following steps: Step S501: The sensitive data is converted into a pixel matrix, loaded onto the off-screen canvas layer through WebGL texture, and a blur filter shader is calculated on the highest level sensitive data based on the Gaussian blur algorithm to obtain the off-screen preprocessed pixel data. Step S502: Extract the pixel data using the readPixels method and generate watermarked RGBA image data on the off-screen canvas layer; Step S503: Clear the original content in the main canvas layer, and draw the off-screen canvas layer in the main canvas layer using an image drawing method for secure composite display.
[0052] In this embodiment, based on the established secure rendering channel, off-screen rendering and compositing of de-identified data are performed. The specific method is as follows: 1) Off-screen preprocessing: First, the anonymized data (text / table) is converted into a pixel matrix and loaded onto an off-screen Canvas using WebGL texture; Then, an additional blur filter shader calculation is performed on the L5 level (i.e., the highest level) data (based on the Gaussian blur algorithm, taking <2ms). Finally, the desensitization layer is generated: the off-screen Canvas extracts the processed pixel data through the readPixels method (the readPixels method is a core function in OpenGLES and its managed bindings, used to read pixel data from the frame buffer) to generate watermarked RGBA image data.
[0053] 2) Secure synthetic display: The main Canvas is cleared of its original content, and the off-screen desensitization layer is drawn onto the main Canvas using the drawImage method. Executing gl.flush() forces a WebGL context refresh, ensuring that the rendering results are displayed immediately (total rendering latency <5ms); glFlush is a core function of the OpenGL graphics API, used to force a flush of the command buffer, ensuring that submitted drawing commands are sent to the underlying rendering engine for execution.
[0054] This invention provides a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, which further includes the following steps: Step S600: Trigger an authorization request based on the user's hover operation, obtain the biometric result or password verification result corresponding to the user, and dynamically modify the pixels in the corresponding area based on the biometric result or password verification result to achieve hover decryption; Step S700: Monitor screenshot shortcut key events in real time, intercept abnormal interactive behaviors based on the screenshot shortcut key events, and automatically enhance the blurring effect of the corresponding sensitive data. Step S800: Obtain compliance detection information and recognition efficiency statistics, and optimize the de-identification rules based on the compliance detection information and the recognition efficiency statistics.
[0055] like Figure 2 As shown, in the overall system flow of this embodiment, after performing secure rendering, effect monitoring and optimization are carried out. This process mainly adopts the method of compliance detection + recognition efficiency statistics to continuously optimize the desensitization rules of the entire process.
[0056] In this embodiment, after off-screen rendering and secure composite display of the sensitive data, the interactive response performance and permissions of the entire process can be verified. Specifically, the method is as follows: 1) Hover decryption process (Level 3): The authorization request is triggered based on the user's hover action. The front end calls navigator.credentials.get() to obtain the biometric / password verification result. After successful verification, the corresponding area pixels are dynamically modified through WebGL (for example, the mask is replaced with plaintext). After decryption for 3 seconds, the mask is automatically restored.
[0057] 2) Abnormal interaction interception: Listen for mousedown+keydown combination events (suspected screenshot shortcuts), and automatically perform temporary blurring processing on L5 level data (for example, increase the pixel radius to 12px). This process is displayed for 1 second.
[0058] Furthermore, in this embodiment, while verifying the interactive response performance and permissions of the entire process, performance and compliance monitoring can also be performed. Specifically, the following methods are used: 1) Real-time performance metrics collection: First, record the rendering time of each frame using performance.now(), and trigger an alarm when it exceeds 5ms (e.g., console output + log report); then, count the rendering frame rate (FPS) of the thousand-node table, and automatically enable rendering caching when it is below 60 frames (for reusing off-screen textures for duplicate data).
[0059] 2) Compliance verification: First, simulate DOM theft attacks periodically (e.g., every 5 minutes) to verify that sensitive data has not been leaked; then, record the anonymization level change log (e.g., user role / device / time / operator) to support audit traceability.
[0060] 3) Performance optimization and iteration: Based on feedback from business personnel on recognition efficiency (e.g., collecting "recognition time" through data tracking), dynamically adjust the L3 level masking rules (e.g., optimize the name mask from "Zhang San" to "Zhang" to improve recognition speed).
[0061] The multi-level visual desensitization engine and real-time hierarchical rendering method for sensitive data provided in this embodiment, compared with traditional desensitization schemes, can dynamically adjust the data display scheme according to factors such as the level of sensitive data and the on-site environment. Thus, while meeting the security requirements of sensitive data display, it can adapt to the privacy level requirements of different scenarios and continuously update the desensitization rules based on subsequent feedback.
[0062] This embodiment achieves the following technical effects through the above technical solution: This embodiment collects sensitive data and identifies the corresponding sensitive types. Based on the identified sensitive types, it calculates the privacy level of the sensitive data using a dynamic privacy level calculation model. It then performs de-identification rule matching based on the privacy level and configures the corresponding de-identification parameters for the sensitive data. A secure rendering channel is built based on the matched de-identification rules and configured parameters, and this secure rendering channel is used to perform off-screen rendering and secure composite display of the sensitive data. This embodiment dynamically calculates the sensitivity intensity based on the privacy level, performs graded progressive de-identification of the data, and then renders it through a secure rendering channel. This allows for data protection according to the needs of different privacy scenarios, preventing the leakage of sensitive data.
[0063] Exemplary device Based on the above embodiments, the present invention also provides a real-time hierarchical rendering system for sensitive data based on a multi-level visual desensitization engine, comprising: A sensitive data acquisition module is used to acquire sensitive data and identify the sensitive type corresponding to the sensitive data; The privacy level calculation module is used to calculate the privacy level of the sensitive data based on the identified sensitive type using a dynamic privacy level calculation model. The desensitization rule matching and visual processing module is used to perform desensitization rule matching based on the privacy level and configure the desensitization parameters corresponding to the sensitive data; The secure rendering channel building module is used to build a secure rendering channel based on the matching de-identification rules and the configured de-identification parameters. The off-screen rendering and secure display module is used to perform off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel.
[0064] This embodiment achieves the following technical effects through the above technical solution: This embodiment collects sensitive data and identifies the corresponding sensitive types. Based on the identified sensitive types, it calculates the privacy level of the sensitive data using a dynamic privacy level calculation model. It then performs de-identification rule matching based on the privacy level and configures the corresponding de-identification parameters for the sensitive data. A secure rendering channel is built based on the matched de-identification rules and configured parameters, and this secure rendering channel is used to perform off-screen rendering and secure composite display of the sensitive data. This embodiment dynamically calculates the sensitivity intensity based on the privacy level, performs graded progressive de-identification of the data, and then renders it through a secure rendering channel. This allows for data protection according to the needs of different privacy scenarios, preventing the leakage of sensitive data.
[0065] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 3 As shown.
[0066] The terminal includes: a processor, a memory, an interface, a display screen, and a communication module connected via a system bus; wherein, the processor of the terminal provides computing and control capabilities; the memory of the terminal includes a computer-readable storage medium and internal memory; the computer-readable storage medium stores an operating system and computer programs; the internal memory provides an environment for the operation of the operating system and computer programs in the computer-readable storage medium; the interface is used to connect to external devices; the display screen is used to display relevant information; and the communication module is used to communicate with a cloud server or other devices.
[0067] When executed by the processor, this computer program is used to implement a real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine.
[0068] It will be understood by those skilled in the art that Figure 3 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0069] In one embodiment, a terminal is provided, comprising: a processor and a memory, the memory storing a real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine, the real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine being executed by the processor to implement the operation of the above-described real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine.
[0070] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine, which, when executed by a processor, is used to implement the operation of the above-described real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine.
[0071] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include both non-volatile and volatile memory.
[0072] In summary, this invention provides a method, system, terminal, and storage medium for real-time hierarchical rendering of sensitive data based on a multi-level visual desensitization engine. The method includes: collecting sensitive data and identifying the sensitivity type corresponding to the sensitive data; calculating the privacy level of the sensitive data using a dynamic privacy level calculation model based on the identified sensitivity type; matching desensitization rules based on the privacy level and configuring desensitization parameters corresponding to the sensitive data; building a secure rendering channel based on the matched desensitization rules and configured desensitization parameters; and performing off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel. This invention dynamically calculates sensitivity intensity based on privacy levels, performs hierarchical progressive desensitization of data, and then renders the data through a secure rendering channel. This allows for data protection according to the needs of different privacy scenarios, preventing the leakage of sensitive data.
[0073] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.
Claims
1. A real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine, characterized in that, include: Collect sensitive data and identify the sensitivity type corresponding to the sensitive data; Based on the identified sensitive types, the privacy level of the sensitive data is calculated using a dynamic privacy level calculation model. Based on the privacy level, de-identification rules are matched, and de-identification parameters corresponding to the sensitive data are configured; A secure rendering channel is built based on the matching desensitization rules and the configured desensitization parameters; The sensitive data is rendered off-screen and displayed securely using the secure rendering channel.
2. The real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine according to claim 1, characterized in that, The process of collecting sensitive data and identifying the sensitive type corresponding to the sensitive data includes: The sensitive data is obtained by acquiring user information tables, transaction records, and privacy information through API interfaces or database connections; Based on the regular expression library, the system matches ID card number, mobile phone number, and name information to obtain sensitive fields in the sensitive data. Then, it combines NLP semantic analysis to identify implicit sensitive data in the sensitive data and generate a list of sensitive data types.
3. The real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine according to claim 1, characterized in that, The step of calculating the privacy level of the sensitive data using a dynamic privacy level calculation model based on the identified sensitive type includes: Based on the aforementioned privacy level dynamic calculation model, the user role weights corresponding to the sensitive data are assigned values, and the corresponding device environment coefficients and content sensitivity benchmark scores are determined. The privacy level of the sensitive data is calculated based on the user role weight, the device environment coefficient, and the content sensitivity benchmark score.
4. The real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine according to claim 1, characterized in that, The step of matching de-identification rules based on the privacy level and configuring the de-identification parameters corresponding to the sensitive data includes: Based on the privacy level, de-identification rules are matched to generate a privacy level-rule mapping relationship, and the de-identification parameters corresponding to the sensitive data are configured according to the real-time adjusted level style parameters. The mapping relationship between the privacy level and the rule is as follows: L0: Directly output the sensitive data; L1: Display the sensitive data using a layered style; L2: Adds a transition display effect based on L1; L3: Mask the sensitive data with text and set a hover decryption trigger condition; L4: Overlay a semi-transparent mask on top of L3; L5: Apply a dynamic blur filter to the sensitive data and overlay a dynamic watermark.
5. The real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine according to claim 1, characterized in that, The process of building a secure rendering channel based on the matched de-identification rules and configured de-identification parameters includes: Initialize the main canvas layer and the off-screen canvas layer, and enable the WebGL protocol to build an independent rendering environment for obtaining the sensitive data; Based on the techniques of de-document flow and keyboard attribute hiding, the main canvas layer and the off-screen canvas layer are configured for DOM isolation in sequence, and an anti-screenshot strategy is loaded to obtain the secure rendering channel.
6. The real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine according to claim 1, characterized in that, The off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel includes: The sensitive data is converted into a pixel matrix, loaded into the off-screen canvas layer through WebGL texture, and the highest level sensitive data is subjected to a blur filter shader calculation based on the Gaussian blur algorithm to obtain the off-screen preprocessed pixel data. The pixel data is extracted using the readPixels method, and watermarked RGBA image data is generated on the off-screen canvas layer. The original content in the main canvas layer is cleared, and the off-screen canvas layer is drawn on the main canvas layer using an image drawing method for secure composite display.
7. The real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine according to claim 1, characterized in that, The process of off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel then includes: The system triggers an authorization request based on the user's hovering action, obtains the biometric or password verification result corresponding to the user, and dynamically modifies the pixels in the corresponding area based on the biometric or password verification result to achieve hovering decryption. Real-time monitoring of screenshot shortcut key events; interception of abnormal interactive behaviors based on the screenshot shortcut key events; and automatic enhancement of the blurring effect of corresponding sensitive data. Obtain compliance testing information and identification efficiency statistics, and optimize the de-identification rules based on the compliance testing information and the identification efficiency statistics.
8. A real-time hierarchical rendering system for sensitive data based on a multi-level visual desensitization engine, characterized in that, include: A sensitive data acquisition module is used to acquire sensitive data and identify the sensitive type corresponding to the sensitive data; The privacy level calculation module is used to calculate the privacy level of the sensitive data based on the identified sensitive type using a dynamic privacy level calculation model. The desensitization rule matching and visual processing module is used to perform desensitization rule matching based on the privacy level and configure the desensitization parameters corresponding to the sensitive data; The secure rendering channel building module is used to build a secure rendering channel based on the matching de-identification rules and the configured de-identification parameters. The off-screen rendering and secure display module is used to perform off-screen rendering and secure composite display of the sensitive data based on the secure rendering channel.
9. A terminal, characterized in that, include: The processor and memory, wherein the memory stores a real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine, and the real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine, when executed by the processor, is used to implement the operation of the real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine. When executed by a processor, the real-time hierarchical rendering program for sensitive data based on a multi-level visual desensitization engine is used to implement the operation of the real-time hierarchical rendering method for sensitive data based on a multi-level visual desensitization engine as described in any one of claims 1-7.