Gynecological patient privacy protection method and system

Abstract

The invention relates to the technical field of medical information security, in particular to a gynecological patient privacy protection method and system.The method comprises the steps that a gynecological privacy information standard processing mechanism is established based on gynecological patient information, and the gynecological patient information is processed through the processing mechanism to obtain a gynecological patient privacy information base; constructing a privacy information chaos analysis and encryption protection model according to the gynecological patient privacy information base, and analyzing the chaos feature condition of the gynecological patient privacy information by using the analysis and encryption protection model; updating the initial key of the privacy information of the gynecological patient in combination with the chaos feature condition and a chaos analysis and encryption protection model so as to match new key information; and protecting the gynecological patient privacy information base according to the gynecological privacy multi-dimensional protection strategy and the new key information so as to ensure the security of the gynecological patient privacy information. The privacy information of the gynecological patient is protected, the privacy safety of the patient can be ensured, the medical service quality is improved, and the medical informatization development is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information security, and specifically provides a privacy protection method and system for gynecological patients. Background Art

[0002] Due to the privacy of gynecological patients' diseases, there is a high demand for privacy protection. However, with the wide application of digital medical information, network technology, and transmission and storage technology, the security risks of patients' privacy information have increased accordingly. Threats such as external hacker attacks and virus intrusions can lead to the leakage of gynecological patients' disease information. At the same time, problems such as poor information management within medical institutions, such as unreasonable permission settings and irregular employee operations, will further cause accidental leakage of gynecological patients' privacy information.

[0003] Therefore, it is urgent to design more advanced encryption technologies and gynecological patient information management methods to ensure the security and privacy of gynecological patients' privacy information during transmission and storage. In addition, a privacy protection management system for gynecological patients should be established and improved to enhance the protection of gynecological privacy information, effectively guarantee the security of gynecological privacy information, provide new ideas and technical means for the protection of gynecological privacy information, and thus promote the further development of the medical information security field. Summary of the Invention

[0004] In view of the deficiencies of existing methods and the requirements of practical applications, in order to protect the privacy information of gynecological patients, improve the security of patients' privacy information, enhance the quality and intelligent level of medical services, and further promote the construction of medical informatization. On the one hand, the present invention provides a privacy protection method for gynecological patients, and the method includes: establishing a standardized processing mechanism for gynecological privacy information based on gynecological patient information, and processing the gynecological patient information through the standardized processing mechanism for gynecological privacy information to obtain a gynecological patient privacy information database; constructing a chaos analysis and encryption protection model for privacy information according to the gynecological patient privacy information database, and using the chaos analysis and encryption protection model for privacy information to analyze the chaos characteristics of gynecological patient privacy information; updating the initial key of the gynecological patient privacy information in combination with the chaos characteristics and the chaos analysis and encryption protection model for privacy information to match the new key information of the gynecological patient privacy information; protecting the gynecological patient privacy information database according to the multi-dimensional protection strategy for gynecological privacy and the new key information to ensure the security of gynecological patient privacy information. The present invention establishes a standardized processing mechanism for gynecological privacy information based on gynecological patient information, and protects the gynecological patient privacy information through a series of technical means, which not only helps to protect patients' privacy, but also improves the quality and security of medical services, enhances data security, and promotes the intelligent development of medical information.

[0005] Optionally, the mechanism for standardizing the processing of gynecological privacy information based on gynecological patient information includes: setting a local similarity analysis model for patient privacy images and a normalization processing model for patient privacy images in the mechanism for standardizing the processing of gynecological privacy information according to the gynecological patient information. The local similarity analysis model for patient privacy images and the normalization processing model for patient privacy images set in the present invention help to improve the standardization effect of gynecological privacy information processing, can unify data standards, enhance data comparability, and provide support for the comprehensive protection of gynecological patient privacy information.

[0006] Optionally, the setting of the local similarity analysis model for patient privacy images and the normalization processing model for patient privacy images in the mechanism for standardizing the processing of gynecological privacy information according to the gynecological patient information includes: dividing the privacy images of different gynecological patients into multiple privacy image blocks based on the gynecological patient information; setting a local similarity analysis model for patient privacy images according to the multiple privacy image blocks; and analyzing the local similarity of the privacy images of different gynecological patients through the local similarity analysis model for patient privacy images. In the present invention, the privacy images of different gynecological patients are divided into multiple privacy image blocks, which can achieve refined analysis and protection of image content, adopt different protection measures according to the privacy image content, and further ensure the effective protection of privacy information.

[0007] Optionally, the local similarity analysis model for patient privacy images satisfies the following relationship: where represents the local similarity analysis result of the privacy images of different gynecological patients, represents the weight coefficient of the central pixel value of the patient privacy image, represents the fluctuation coefficient corresponding to the size of the nth image block, represents the image pixel value vector of the nth image block in the patient privacy image, represents the image pixel value vector at the central position of the patient privacy image, represents the smoothing parameter of the patient privacy image.

[0008] In the present invention, the similarity between the local image block and the central image block in the privacy images of different gynecological patients is calculated based on the analysis model, and thus the local similarity of the privacy images can be objectively and accurately evaluated, providing a basis for the operation of subsequent confidentiality mechanisms.

[0009] Optionally, the processing of gynecological patient information by the gynecological privacy information specification processing mechanism to obtain a gynecological patient privacy information database includes: setting a patient privacy image normalization processing model based on the local similarity; using the patient privacy image normalization processing model to process the gynecological patient information to obtain a gynecological patient privacy information database. The present invention uses the patient privacy image normalization processing model to process the gynecological patient information, which can ensure the security of the information during storage and transmission.

[0010] Optionally, the patient privacy image normalization processing model satisfies the following relationship: Among them, represents the normalization matrix of different gynecological patient privacy images, represents a diagonal matrix, represents the local similarity result of the first image block in the patient privacy image, represents the local similarity result of the second image block in the patient privacy image, represents the local similarity result of the nth image block in the patient privacy image, represents a real symmetric and positive semi-definite matrix.

[0011] The present invention uses the patient privacy image normalization processing model to process the patient privacy image, which helps to reduce the influence of noise and outliers in the data on the model performance and can more accurately compare the disease information of different gynecological patients.

[0012] Optionally, constructing a privacy information chaos analysis and encryption protection model according to the gynecological patient privacy information database, and using the privacy information chaos analysis and encryption protection model to analyze the chaos characteristics of gynecological patient privacy information includes: analyzing the probability characteristics of the gray value of the central pixel of the image and the average gray value of the pixels other than the center based on the gynecological patient privacy information database; setting a patient privacy image chaos degree prediction function in the privacy information chaos analysis and encryption protection model according to the probability characteristics; analyzing the chaos characteristics of the privacy information of different gynecological patients through the patient privacy image chaos degree prediction function. The present invention uses the model to analyze the chaos of gynecological patient privacy information, which can enhance the security of privacy information and further improve the intelligent level of privacy protection.

[0013] Optionally, the patient privacy image chaos degree prediction function satisfies the following relationship: Among them, represents the information entropy value of different gynecological patient privacy images, represents the maximum value obtained, represents The maximum value obtained represents the probability of occurrence in the privacy images of different gynecological patients The probability of occurrence represents the gray level of the central pixel of the patient's privacy image represents the average gray level of the remaining pixels after removing the central pixel in the patient's privacy image.

[0014] Based on the information entropy value, the present invention can objectively and quantitatively evaluate the degree of chaos of the privacy images of gynecological patients, and can effectively reflect the distribution and complexity of the patient's privacy images.

[0015] Optionally, updating the initial key of the gynecological patient privacy information by combining the chaos feature situation and the privacy information chaos analysis and encryption protection model to match the new key information of the gynecological patient privacy information includes: setting a key update mechanism for gynecological privacy information in the privacy information chaos analysis and encryption protection model; combining the key update mechanism for gynecological privacy information and the chaos feature situation to update the initial keys of different gynecological patient privacy information to match the new key information of different gynecological patient privacy information. The present invention dynamically adjusts the key generation and update strategy based on the chaos situation, making the key difficult to be predicted and cracked, significantly enhancing the security of the key, contributing to the protection of gynecological patient privacy information, and further improving the feasibility and effectiveness of the gynecological patient privacy protection method.

[0016] In a second aspect, to efficiently execute the gynecological patient privacy protection method provided by the present invention, the present invention also provides a gynecological patient privacy protection system, which includes an input device, a processor, an output device, and a memory. Among them, the input device, the processor, the output device, and the memory are interconnected. The memory includes the gynecological patient privacy protection method as described in the first aspect of the present invention. The memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions. The gynecological patient privacy protection system provided by the present invention has a compact structure and strong applicability, and greatly improves the operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the gynecological patient privacy protection method of the present invention; Figure 2 is a schematic diagram of the privacy image of a gynecological patient after being processed with different keys in the gynecological patient privacy protection method of the present invention; Figure 3 is a structural diagram of the gynecological patient privacy protection system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustrative purposes and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those of ordinary skill in the art that: the present invention does not have to be practiced with these specific details. In other instances, well-known circuits, software, or methods have not been described in detail in order to avoid obscuring the present invention.

[0019] Throughout the specification, references to "one embodiment", "an embodiment", "one example", or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the phrases "in one embodiment", "in an embodiment", "one example", or "an example" appearing throughout the specification do not necessarily all refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and / or sub-combination in one or more embodiments or examples. In addition, those of ordinary skill in the art should understand that the diagrams provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0020] Please refer to Figure 1 , in order to improve the security of the privacy information of gynecological patients and enhance the adaptability and flexibility of the privacy protection method for gynecological patients, the method realizes the effective protection of the privacy information of gynecological patients based on a comprehensive and systematic protection strategy. The present invention provides a privacy protection method for gynecological patients, and the above-mentioned privacy protection method for gynecological patients includes the following steps: S1. Establish a standardized processing mechanism for gynecological privacy information based on the information of gynecological patients, and process the information of gynecological patients through the above-mentioned standardized processing mechanism for gynecological privacy information to obtain a gynecological patient privacy information database. The specific setting steps and implementation contents are as follows: In order to realize the effective processing and analysis of the privacy information of gynecological patients, identify and keep confidential the key feature information in the patient privacy information, and encrypt or blur the key area as appropriate to provide a basis for the privacy protection method. In this embodiment, a local similarity analysis model for patient privacy images and a normalization processing model for patient privacy images are set in the standardized processing mechanism for gynecological privacy information according to the information of gynecological patients.

[0021] First, based on the information of gynecological patients, the privacy images of different gynecological patients are divided into multiple privacy image blocks, and a local similarity analysis model for patient privacy images is set according to the above-mentioned multiple privacy image blocks; the local similarity of the privacy images of different gynecological patients can be analyzed through the local similarity analysis model for patient privacy images.

[0022] In the process of processing the privacy information of gynecological patients, according to the information of gynecological patients, the privacy images of different patients are divided into multiple independent image blocks. Based on this, each local feature in the patient privacy images can be analyzed more carefully. When dividing the patient privacy images, it is necessary to denoise the patient privacy images, which can effectively remove the noise and interference in the patient privacy images, thereby improving the quality and effectiveness of the privacy information of gynecological patients.

[0023] In an optional embodiment, a denoising algorithm is used to analyze and process each pixel point in the patient privacy image. According to the feature conditions of each pixel point and other pixel points in its adjacent window, the noise components in the privacy image can be more accurately identified and removed. It can not only effectively retain the useful information in the image, but also significantly improve the denoising effect of the patient privacy image.

[0024] In addition, in the embodiment, targeted denoising processing of the patient privacy images is performed on each image block (i.e., each local position) in the privacy images of gynecological patients. Since different image blocks contain different information features and details, the adoption of targeted denoising strategies can better meet the specific requirements of each different image block, further improving the denoising effect and image quality, laying a foundation for the subsequent analysis of patient privacy images, and helping to ensure the accuracy and reliability of the privacy information of gynecological patients.

[0025] Based on the multiple privacy image blocks after division, the similarity degree between different image blocks (i.e., different local positions in the image) is calculated. In order to comprehensively analyze the feature information of each image block, a local similarity analysis model of patient privacy images is constructed in the embodiment, and this model can accurately reflect the similarity relationship between different image blocks.

[0026] The above local similarity analysis model of patient privacy images satisfies the following relationship: Among them, represents the local similarity analysis result of different gynecological patient privacy images, represents the weight coefficient of the central pixel value of the patient privacy image, represents the fluctuation coefficient corresponding to the size of the nth image block, represents the image pixel value vector of the nth image block in the patient privacy image, represents the image pixel value vector of the central position of the patient privacy image, represents the smoothing parameter of the patient privacy image.

[0027] Calculate the local similarity of the privacy images of different gynecological patients using the local similarity analysis model for patient privacy images. Based on this quantitative metric, the degree of similarity between different image patches and the central image patch in the patient privacy images can be measured. The larger the value of the above analysis result, the higher the similarity between the two image patches; conversely, it indicates a lower similarity.

[0028] The weight coefficient of the central pixel value of the patient privacy image is an adjustment factor used to adjust the influence of the central pixel value in the similarity calculation. The weight function can measure the contribution degree of different image patches to the central pixel value.

[0029] The fluctuation coefficients corresponding to different image patch sizes reflect the degree of influence of different image patch sizes on the similarity calculation results. Different image patch sizes will have different fluctuation coefficients to adapt to the characteristics of different-sized image patches in the similarity calculation. The image pixel value vectors of different image patches in the patient privacy image contain the gray values or color values of all pixels within different image patches. The above image pixel value vectors are the basic data for similarity calculation and directly reflect the information content contained in different image patches.

[0030] The image pixel value vector at the central position of the patient privacy image mainly contains the gray values or color values of all pixels within the central image patch. In the embodiment, the central position of the image is used as a reference point for comparison with other image patches, which is conducive to subsequent analysis of the similarity degree between different image patches and the reference image.

[0031] The smoothing parameter of the patient privacy image reflects the degree of smoothing in the similarity calculation. The size of the smoothing parameter will affect the smoothness and accuracy of the similarity analysis results.

[0032] In summary, the local similarity analysis model for patient privacy images comprehensively considers multiple factors such as the pixel values, sizes, weight coefficients, and smoothing parameters of image patches. Based on this, the local similarity between different image patches in the patient privacy images is calculated comprehensively, which can provide data support for subsequent image processing, privacy protection, and protection encryption measures.

[0033] The local similarity analysis model for patient privacy images can quantify the similarity between any two image patches and provide a numerical basis for subsequent image processing and analysis. At the same time, the above model can efficiently process a large amount of patient privacy image data, meet the requirements of practical applications, and provide technical support for the privacy protection method of gynecological patients.

[0034] Then, based on the above local similarity, a normalization processing model for patient privacy images is set up, and the normalization processing model for patient privacy images is used to standardize the information of gynecological patients to obtain a gynecological patient privacy information database.

[0035] A patient privacy image normalization model is constructed based on the local similarity analysis results of patient privacy images. This model fully considers the similarity relationship between different image blocks, as well as the relative importance and distribution characteristics in patient privacy images. Through the normalization model, the information of gynecological patients is standardized, which can ensure that the privacy information of each patient can be properly processed and protected.

[0036] The above patient privacy image normalization model satisfies the following relationship: Where, represents the normalization matrix of privacy images of different gynecological patients, represents a diagonal matrix, represents the local similarity result of the first image block in the patient privacy image, represents the local similarity result of the second image block in the patient privacy image, represents the local similarity result of the nth image block in the patient privacy image, represents a real symmetric and positive semi - definite matrix.

[0037] The normalization matrix of privacy images of different gynecological patients is calculated through the patient privacy image normalization model, which reflects a matrix representing different patient privacy images in the normalized space. Each row or column of the matrix represents the value of an image block or image feature after normalization. The above - mentioned normalization matrix provides the representation of patient privacy images in the normalized space, which is helpful for subsequent image analysis, processing, and application. Through normalization, the dimensional difference between different image blocks or different patient privacy images can be eliminated, enabling the privacy image information of different gynecological patients to be compared and analyzed on the same scale, further enhancing the comparability and accuracy of gynecological patient privacy information.

[0038] A diagonal matrix is a special form of matrix and has extensive applications in mathematics and linear algebra. A diagonal matrix refers to a matrix in which all elements outside the main diagonal are 0, that is, for an element (where i and j represent the row number and column number respectively), when , = 0; while when , can be any real number or complex number. The diagonal matrix can further represent the steady - state solutions or characteristic information of different patient privacy images.

[0039] It represents the local similarity results of different image patches in the patient privacy image. In the embodiment, the local similarity analysis model of the patient privacy image is used to analyze the local similarity results between different image patches in the patient privacy image and the reference point, which further reflects the similarity degree between different image patches and the image patch at the central position or the reference image patch, facilitating a better understanding and processing of the data information in the patient privacy image, and providing a basis for medical information protection and scientific classification management.

[0040] The elements in a real symmetric matrix are all real numbers, and the matrix is equal to its transpose matrix. That is, for a matrix B, if all elements of B are real numbers, and (where represents the transpose matrix of B), then B is called a real symmetric matrix, representing a real symmetric and positive semi - definite matrix. The above real symmetric matrix represents a weight relationship matrix, introducing additional information or constraints in the normalization process, which is beneficial to realizing the normalization processing and standardized management of patient privacy images.

[0041] In the processing process, the normalization processing model of patient privacy images is fully utilized to standardize the information of gynecological patients, which not only includes adjusting and optimizing the image pixel values, but also includes analyzing the similarity results between image patches. Based on this, a complete and standardized gynecological patient privacy information library can be obtained quickly and accurately.

[0042] In addition, it is also necessary to further establish a normalization matrix model of the patient privacy image based on the similarity results of different image patches in the patient privacy image. The matrix model takes image patches as units, calculates the similarity between different image patches, and constructs a matrix that can reflect the overall structure and characteristics of the image. It not only provides more intuitive and comprehensive image similarity information, but also provides technical support for subsequent image analysis, processing and applications.

[0043] Through the local similarity analysis model of the patient privacy image and the normalization matrix model of the patient privacy image, an optimized set of patient privacy images is obtained, realizing the comprehensive, standardized and efficient processing of gynecological patient privacy information, that is, obtaining the gynecological patient privacy information library of this embodiment. The above method not only standardizes the privacy information of gynecological patients, but also provides data resources and technical support for medical research, disease diagnosis and information management.

[0044] Furthermore, the method and implementation steps for obtaining the privacy information database of gynecological patients during the driving process in this embodiment are only an optional condition of the present invention. In one or some other embodiments, the method and implementation steps for obtaining the privacy information database of gynecological patients can be adjusted and optimized according to the protection requirements of the privacy information of gynecological patients and the actual situation of the information of gynecological patients. By adjusting and optimizing the obtaining method, the collection, storage, and use processes of the privacy information of gynecological patients can be more precisely controlled, thereby ensuring the effective implementation and practical application of the privacy protection method for gynecological patients.

[0045] S2. Construct a privacy information chaos analysis and encryption protection model based on the above-mentioned privacy information database of gynecological patients, and use the privacy information chaos analysis and encryption protection model to analyze the chaos characteristics of the privacy information of gynecological patients. The specific steps and implementation content are as follows: In image processing, the Gray code scrambling technology is mostly used. As a scrambling means, the Gray code can make only one code element (bit) different between adjacent binary codes, which is a common binary communication coding method. Its characteristic is that only one code element is different between any two adjacent binary codes.

[0046] The conversion from natural binary code to Gray code needs to satisfy the following relationship: Among them, represents the i-bit Gray code after conversion, represents the i-th bit of the natural binary code before conversion, represents the marker index variable of the Gray code, represents the total number of bits of the binary code, represents the exclusive OR operation.

[0047] When is the case, that is, the last bit of the Gray code is equal to the last bit of the binary code.

[0048] When is the case, G(i) = B(i+1) ⊕ B(i), that is, the -th bit of the Gray code is obtained by the exclusive OR of the -th bit and the -th bit of the binary code.

[0049] Furthermore, it satisfies the following relationship: Among them, represents the i-th bit of the natural binary code before conversion, represents the i-bit Gray code after conversion, represents the marker index variable of the Gray code, represents the total number of bits corresponding to the Gray code, represents the exclusive OR operation.

[0050] Based on this, Gray code conversion is performed on the pixel values of the image plane, and the relevant pixel values are correspondingly replaced. According to the size of the image, the number of iterative conversions of the Gray code for the first encryption can be obtained.

[0051] The number of iterative conversions of the above Gray code satisfies the following relationship: where, represents the number of iterative conversions of the Gray code for the first encryption, represents the length of the image, represents the width of the image, represents the corresponding constant during the iterative conversion of the Gray code.

[0052] In image processing is a constant, and based on this, the sum of the length and width of the image is calculated to obtain the analysis result of the corresponding conversion times. After the above processing, the position of each pixel in the image will change, showing an obvious difference compared with the original image. However, the Gray code scrambling technology has a defect, that is, it cannot meet the periodicity of image information. After the image undergoes a certain number of scrambling cycles, it will return to the original state, thus weakening the ability of image information to resist external interference. Therefore, in this embodiment, the Gray code scrambling technology is further optimized and adjusted.

[0053] In the embodiment, a privacy information chaos analysis and encryption protection model is constructed according to the privacy information database of gynecological patients.

[0054] In this embodiment, based on the local correlation of different gynecological patients, the chaos scrambling situation of each image block in the patient's privacy image is analyzed. Subsequently, according to the chaos scrambling degree of the patient's privacy image, different levels of encryption protection measures are matched and applied, which can more effectively capture the local image features of gynecological patients and exclude the interference of irrelevant information. The specific implementation steps and content are as follows: (1) Obtain the privacy images of different gynecological patients based on the privacy information database of gynecological patients, and then quickly obtain the corresponding privacy digital images of different gynecological patients. At the same time, read the size information of the privacy digital images of gynecological patients, where the width of the privacy digital image is set to a and the length is set to b; (2) Based on the historical privacy image information of the gynecological patient privacy protection system, the block size of the privacy digital image is preset in advance, and then the relevant information of the privacy image blocks of different gynecological patients is obtained according to the privacy information database of gynecological patients; (3) According to the relevant information of the privacy image blocks of different gynecological patients, the initial key of the patient's privacy digital image can be obtained (i.e., the initial key), and at the same time, a chaotic sequence is generated accordingly; (4) Analyze the chaotic sequences of the privacy digital images of different gynecological patients. Since the privacy digital images of different gynecological patients correspond to a unique multi-dimensional data structure, in the embodiment, the two-dimensional information entropy is combined to describe the degree of chaos of the image gray level and spatial information of the privacy digital images of different gynecological patients. The specific content is as follows: First, analyze the probability characteristics of the gray level of the central pixel of the image and the average gray level of the pixels other than the center according to the above-mentioned gynecological patient privacy information database.

[0055] Analyze the frequency of occurrence of the binary group in the privacy digital images of different gynecological patients, where represents the gray level value of the central pixel, and represents the average gray level around the central pixel (excluding the central pixel). It is necessary to count and calculate the probability of the occurrence of the above two combinations of gray level values in the privacy digital images, and further satisfy the following relationship: Among them, represents the probability of the occurrence of in the privacy images of different gynecological patients, represents the gray level of the central pixel of the patient's privacy image, represents the average gray level of the remaining pixels after removing the central pixel in the patient's privacy image, represents the binary group in the privacy digital images of different gynecological patients, represents the width of the privacy digital images of different gynecological patients, represents the length of the privacy digital images of different gynecological patients.

[0056] Then, according to the probability characteristics, a prediction function for the chaos degree of the patient's privacy image is set in the privacy information chaos analysis and encryption protection model.

[0057] In the process of processing the privacy images of different gynecological patients, through the frequency of occurrence of the binary group (the binary group composed of the gray level value of the central pixel and its surrounding average gray level) in the image, relevant information about the texture or structure of the privacy images of different gynecological patients is obtained. Furthermore, based on the probability of the occurrence of the binary group in the privacy digital images of different gynecological patients, the degree of chaos or complexity of the privacy images in terms of gray level and spatial information is further analyzed.

[0058] The above analysis process follows a certain relationship or rule, that is, it satisfies the prediction function for the chaos degree of the patient's privacy image in the embodiment, and satisfies the following relationship: Among them, The information entropy values representing the privacy images of different gynecological patients represent the maximum value obtained represent the maximum value obtained represent the probability of occurrence in the privacy images of different gynecological patients of occurrence represent the gray level of the central pixel of the patient's privacy image represent the average gray level of the remaining pixels after removing the central pixel in the patient's privacy image

[0059] The information entropy values of the privacy images of different gynecological patients are used to measure the uncertainty or degree of chaos of the patient's privacy information. In image processing, information entropy can be used to quantify the complexity and chaos of an image, where a higher information entropy value indicates that the privacy image contains more feature information or has a higher degree of chaos

[0060] In image processing, the image is divided into multiple small blocks, including but not limited to etc., and the gray level value of the central pixel of each small block is considered. The above gray level value is an integer between 0 and 255, which further reflects the brightness or color depth of the image pixels

[0061] The gray level value of the central pixel of the privacy image of different gynecological patients is an integer between 0 (black) and 255 (white). Therefore the maximum value of can be 255, which further represents the maximum value obtained by the gray level value of the central pixel

[0062] The average gray level of the remaining pixels after removing the central pixel in the privacy image of gynecological patients. Its gray level value is also an integer between 0 and 255, and further the maximum value can only be 255 The maximum value can be adjusted and set according to the gray level distribution of the privacy image

[0063] After considering the gray level value of the central pixel, calculate the average gray level around the pixel, such as the average gray level of the remaining pixels excluding the central pixel in a 3x3 small block. This average value reflects the average level of the brightness or color depth of the area around the central pixel

[0064] In the embodiment, based on the probability of occurrence and its logarithmic entropy, the degree of chaos and complexity of the privacy image of gynecological patients are measured, which has wide applications in the fields of image processing, computer vision, and machine learning, and helps to realize tasks such as texture analysis, feature extraction, and image classification of the privacy image of gynecological patients, and further facilitates the confidentiality processing of key information and the adjustment of the protection scheme by the privacy protection method for gynecological patients

[0065] Next, the chaotic characteristics of the privacy information of different gynecological patients are analyzed through the patient privacy image chaos prediction function.

[0066] Through the patient privacy image chaos prediction function, an in-depth analysis of the chaotic characteristics of the privacy information of different gynecological patients is carried out. This function uses a double summation method to traverse all possible combinations of gray values , where and respectively represent and 's maximum value range, which mainly depends on the gray level of the privacy image and the size of the selected analysis area. For each pair of gray value combinations represents the probability of its occurrence in the patient privacy digital image, and a logarithmic function is used in the function to calculate the logarithm of the probability.

[0067] In the embodiment, the information entropy value as a measure of entropy reflects the uncertainty or chaos degree of the gray level and spatial information of the privacy images of different gynecological patients. When the distribution of gray value combinations in the privacy image is more uniform, that is, the probability of each combination appearing is closer, the corresponding information entropy value is larger, indicating a higher degree of chaos of the privacy image; on the contrary, when the occurrence probability of the gray value combination is much higher than other combinations, the corresponding information entropy is smaller, indicating a lower degree of chaos of the privacy image of this gynecological patient.

[0068] In the process of processing the privacy digital images of different gynecological patients, not only the gray information of the pixels is considered, but also the spatial information is incorporated, which comprehensively reflects the position dimension information of the image pixels and the characteristics of the gray distribution within the pixel domain. The larger the information entropy value, the more chaotic the privacy image, which means that the encrypted patient privacy image needs to have higher security.

[0069] Furthermore, the analysis method of the chaotic characteristics of the privacy information in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the analysis method of the chaotic characteristics of the privacy information can be optimized according to the protection requirements of gynecological privacy information and the characteristic conditions of gynecological privacy information, which can not only capture and analyze the chaotic characteristics of the privacy information more accurately, but also further improve the effect and security of privacy protection, so as to provide guarantee for the effective protection of gynecological privacy information.

[0070] S3. Combine the chaotic characteristics and the privacy information chaos analysis and encryption protection model to update the initial key of the gynecological patient privacy information to match the new key information of the gynecological patient privacy information. The specific implementation content is as follows: In this embodiment, a key update mechanism for gynecological privacy information is set in the above-mentioned chaos analysis and encryption protection model for privacy information. At the same time, the initial keys of the privacy information of different gynecological patients are updated by combining the key update mechanism for gynecological privacy information and the chaos characteristics, so as to match the new key information of the privacy information of different gynecological patients.

[0071] Combining the chaos characteristics and the chaos analysis and encryption protection model for privacy information, the initial keys of the privacy information of gynecological patients are dynamically updated to ensure that the privacy information of gynecological patients matches the new key information. In this embodiment, a key update mechanism for gynecological privacy information is set in the chaos analysis and encryption protection model for privacy information, and the above mechanism can flexibly adjust the keys corresponding to different privacy information according to the protection requirements of gynecological privacy information.

[0072] In the specific implementation process, the initial keys of each gynecological patient are updated individually by combining the key update mechanism for gynecological privacy information and the chaos characteristics. During the update process, the chaos characteristics of the patient's privacy digital image need to be fully considered to ensure that the new key can accurately reflect the information entropy of the image, so as to generate a matching new key. 。

[0073] After obtaining the new key it is combined with the component matrices of the three channels of the pixel matrix R, G, B of the patient's privacy digital image, and the pixel matrix is updated according to a specific subscript sequence. During the update process, not only the redistribution of pixel values is involved, but also the scrambling of pixel positions, so as to obtain a new, scrambled pixel matrix.

[0074] Subsequently, the updated pixel matrix is separated again into the component matrices of the three channels of R, G, B for further processing. Through the above series of operations, a scrambled patient privacy digital image is finally generated. This image is visually very different from the original privacy information, so as to effectively protect the privacy information of different gynecological patients.

[0075] In the embodiment, the privacy digital images of gynecological patients after being processed based on different new keys, please refer to Figure 2 。

[0076] At the same time, the above encryption update process is reversible. The decryption algorithm uses the same key as the encryption algorithm, but the operation process is opposite. By using the algorithm to perform block chaos inverse scrambling on the scrambled image, the original privacy digital image of the gynecological patient can be restored, so as to ensure the integrity, availability and effectiveness of the gynecological patient privacy information database.

[0077] Furthermore, the update mechanism of the key information of gynecological patients in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the update mechanism of the key information of gynecological patients can be adjusted according to the actual requirements of privacy protection and the basic situation of privacy information, which can improve the pertinence and effectiveness of the key. Different gynecological patient privacy information has different sensitivities and protection levels. By adjusting the key update mechanism, corresponding levels of protection can be provided for different levels of privacy information, so as to achieve more refined privacy protection and scientific management.

[0078] S4. Protect the gynecological patient privacy information database according to the multi-dimensional protection strategy for gynecological privacy and the new key information to ensure the security of the gynecological patient privacy information. The specific implementation content is as follows: In an optional embodiment, in order to ensure the security of the gynecological patient privacy information, the gynecological information can be protected according to the multi-dimensional protection strategy for gynecological privacy and the new key information. The specific implementation content is as follows: The gynecological patient privacy protection method sets access permissions in the patient privacy image storage server to ensure that only authorized personnel can access the relevant privacy information of gynecological patients.

[0079] When transmitting the diagnostic images of gynecological patients, a secure transmission protocol (such as HTTPS) is used to prevent the data from being intercepted or tampered with during the transmission process. At the same time, the transmitted image data is preliminarily encrypted to ensure the confidentiality and security of the gynecological patient data during the transmission process.

[0080] When using the diagnostic images of different gynecological patients for medical diagnosis, teaching or research, it is necessary to ensure that the patient information in the images has been desensitized so that the identity information of gynecological patients cannot be recognized and obtained, preventing the leakage of patient identities. On the other hand, when sharing the diagnostic image data that needs to be shared, the consent of the gynecological patient must be obtained and relevant agreements must be signed. In addition, for the image data of gynecological patients that are no longer needed, they should be destroyed according to relevant regulations to ensure that the relevant data cannot be restored. At the same time, the destruction process should be recorded in detail and archived.

[0081] Through the above measures, the security and confidentiality of the gynecological patient privacy information are effectively improved, and the risk of leakage of the gynecological patient privacy information is reduced. At the same time, it helps to ensure the standardization and continuity of the gynecological patient privacy protection work.

[0082] The gynecological patient privacy protection method can systematically and standardize the processing of gynecological patient information through the gynecological privacy information standard processing mechanism, which can ensure the accuracy and integrity of the information and reduce the risk of information leakage at the same time; construct a privacy information chaos analysis and encryption protection model, and use the complexity of chaos theory to encrypt and protect the privacy information, which greatly increases the difficulty of information being cracked and improves the security of the information.

[0083] Furthermore, combining the chaotic characteristics of the privacy information of gynecological patients and the chaotic analysis and encryption protection model of privacy information, the initial key is dynamically updated to generate new key information, enabling the privacy protection method for gynecological patients to have better flexibility and adaptability, being able to effectively cope with potential security threats and attacks, and further enhancing the protection effect of privacy information.

[0084] In addition, the privacy information database of gynecological patients is protected according to the multi-dimensional protection strategy of gynecological privacy and the new key information, covering multiple links such as information storage, transmission, use, sharing, and destruction, which can ensure that the privacy information of gynecological patients is fully protected in each link, effectively preventing information leakage and abuse.

[0085] In summary, establishing a standardized processing mechanism for gynecological privacy information and constructing a chaotic analysis and encryption protection model for privacy information to comprehensively protect the privacy information of gynecological patients can not only improve the security of information, but also enhance the trust of patients, promote doctor-patient harmony, and provide a strong guarantee for the continuous development and improvement of medical services.

[0086] Please refer to Figure 3 , in an optional embodiment, in order to efficiently execute a privacy protection method for gynecological patients provided by the present invention, the present invention also provides a privacy protection system for gynecological patients, and the system includes a processor, an input device, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. Among them, the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute the specific steps of the privacy protection method for gynecological patients provided by the present invention and related embodiments. The privacy protection system for gynecological patients of the present invention has a complete structure, objectivity, and stability.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A privacy protection method for gynecological patients, characterized in that, The method includes: Establishing a standardized processing mechanism for gynecological privacy information based on gynecological patient information, and processing the gynecological patient information through the standardized processing mechanism for gynecological privacy information to obtain a gynecological patient privacy information database; Constructing a chaotic analysis and encryption protection model for privacy information according to the gynecological patient privacy information database, and analyzing the chaotic characteristics of the gynecological patient privacy information by using the chaotic analysis and encryption protection model for privacy information; Updating the initial key of the gynecological patient privacy information in combination with the chaotic characteristic situation and the chaotic analysis and encryption protection model for privacy information to match the new key information of the gynecological patient privacy information; Protecting the gynecological patient privacy information database according to the multi-dimensional protection strategy for gynecological privacy and the new key information to ensure the security of the gynecological patient privacy information.

2. The gynecological patient privacy protection method according to claim 1, wherein The establishing of the standardized processing mechanism for gynecological privacy information based on gynecological patient information includes: Setting a local similarity analysis model for patient privacy images and a normalization processing model for patient privacy images in the standardized processing mechanism for gynecological privacy information according to the gynecological patient information.

3. The gynecological patient privacy protection method according to claim 2, wherein The setting of the local similarity analysis model for patient privacy images and the normalization processing model for patient privacy images in the standardized processing mechanism for gynecological privacy information according to the gynecological patient information includes: Dividing the privacy images of different gynecological patients into multiple privacy image blocks based on the gynecological patient information; Setting a local similarity analysis model for patient privacy images according to the multiple privacy image blocks; Analyzing the local similarity of the privacy images of different gynecological patients through the local similarity analysis model for patient privacy images.

4. The gynecological patient privacy protection method according to claim 2, characterized in that, The local similarity analysis model for patient privacy images satisfies the following relationship: Among them, represents the local similarity analysis result of privacy images of different gynecological patients, represents the weight coefficient of the central pixel value of the patient's privacy image, represents the fluctuation coefficient corresponding to the size of the nth image block, represents the image pixel value vector of the nth image block in the patient's privacy image, represents the image pixel value vector at the central position of the patient's privacy image, represents the smoothing parameter of the patient's privacy image.

5. The gynecological patient privacy protection method according to claim 3, wherein The processing of the gynecological patient information through the standardized processing mechanism for gynecological privacy information to obtain a gynecological patient privacy information database includes: Setting a normalization processing model for patient privacy images based on the local similarity; Using the normalization processing model for patient privacy images to perform standardized processing on the gynecological patient information to obtain a gynecological patient privacy information database.

6. The gynecological patient privacy protection method according to claim 5, characterized in that, The normalization processing model for patient privacy images satisfies the following relationship: Among them, represents the normalized matrix of privacy images of different gynecological patients, represents the diagonal matrix, represents the local similarity result of the first image block in the patient privacy image, represents the local similarity result of the second image block in the patient privacy image, represents the local similarity result of the nth image block in the patient privacy image, represents a real symmetric and positive semi - definite matrix.

7. The gynecological patient privacy protection method according to claim 1, characterized in that, The constructing of the chaotic analysis and encryption protection model for privacy information according to the gynecological patient privacy information database and the analyzing of the chaotic characteristics of the gynecological patient privacy information by using the chaotic analysis and encryption protection model for privacy information include: Analyzing the probability characteristics of the gray scale of the central pixel of the image and the average gray scale of the pixels other than the center according to the gynecological patient privacy information database; Setting a prediction function for the chaos degree of patient privacy images in the chaotic analysis and encryption protection model for privacy information according to the probability characteristics; Analyzing the chaotic characteristics of the privacy information of different gynecological patients through the prediction function for the chaos degree of patient privacy images.

8. The gynecological patient privacy protection method according to claim 7, wherein, The prediction function for the chaos degree of patient privacy images satisfies the following relationship: Among them, represents the information entropy value of the privacy images of different gynecological patients, represents the maximum value obtained, represents the maximum value obtained, represents the probability of occurrence in the privacy images of different gynecological patients where represents the gray level of the central pixel of the patient's privacy image, represents the average gray level of the remaining pixels after removing the central pixel from the patient's privacy image.

9. The method for protecting the privacy of gynecological patients according to claim 1, wherein The updating of the initial key of the gynecological patient privacy information in combination with the chaotic characteristic situation and the chaotic analysis and encryption protection model for privacy information to match the new key information of the gynecological patient privacy information includes: Setting a key updating mechanism for gynecological privacy information in the chaotic analysis and encryption protection model for privacy information; Update the initial key of the privacy information of different gynecological patients in combination with the above-mentioned gynecological privacy information key update mechanism and the chaotic feature situation, so as to match the new key information of the privacy information of different gynecological patients.

10. A privacy protection system for gynecological patients, characterized in that, The system includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. Among them, the memory is used to store computer programs, and the computer programs include program instructions. The processor is configured to call the program instructions to execute the gynecological patient privacy protection method according to any one of claims 1-9.

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