HPV detection information management method and system based on privacy protection

Abstract

The invention relates to the technical field of medical message management, in particular to an HPV detection information management method and system based on privacy protection, and the method comprises the steps: obtaining HPV detection information of a target subject, carrying out the feature extraction of the HPV detection information, and obtaining personal basic information and detection sample information; performing encryption processing on the detection sample information to obtain HPV detection encryption data, and performing privacy feature recognition on the HPV detection encryption data to obtain a privacy feature set; performing privacy risk assessment on the privacy feature set to obtain a privacy risk index; acquiring a whole-process information security parameter of the HPV detection information of the target subject; based on the whole-process information security parameters, performing privacy leakage influence quantitative evaluation on the privacy risk index to obtain a privacy leakage risk evaluation index; calling a corresponding privacy leakage risk threshold value based on the personal basic information, and performing leakage risk early warning judgment on the privacy leakage risk assessment index according to the corresponding privacy leakage risk threshold value; the threat identification precision can be improved, and risk early warning is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical message management, and in particular to a privacy protection-based HPV detection information management method and system. Background Art

[0002] HPV testing is an important means of early screening for malignant tumors such as cervical cancer. The testing process involves the patient's sensitive personal information. With the popularization of HPV testing technology in disease prevention and screening, the need for privacy protection in the testing information management system is becoming increasingly urgent. Once private information is leaked or abused, it will pose a serious threat to the privacy, reputation and even personal safety of the person being tested.

[0003] Existing HPV test information management methods mostly use basic data storage and access control strategies, only perform simple encryption processing on some key information, and lack systematic privacy protection for the entire life cycle of test information; for example, in the information processing process, the relationship between data characteristics and privacy risks is not fully considered, making it difficult to accurately identify potential privacy threats. In the information storage and transmission links, the monitoring and evaluation of information security parameters are insufficient, and it is impossible to effectively resist the risk of data leakage, resulting in the inability to timely and accurately warn of privacy leakage risks. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a privacy-protected HPV detection information management method and system that can improve threat identification accuracy and achieve early risk warning.

[0005] In a first aspect, the present invention provides a method for managing HPV test information based on privacy protection, the method comprising:

[0006] Obtain the HPV test information of the target subject and perform feature extraction to obtain basic personal information and test sample information;

[0007] Encrypting the test sample information to obtain HPV test encrypted data, and performing privacy feature identification on the data to obtain a privacy feature set;

[0008] Performing a privacy risk assessment on the privacy feature set to obtain a privacy risk index;

[0009] Obtain the full-process information security parameters of the target examinee's HPV test information;

[0010] Based on the full-process information security parameters, quantitatively assess the privacy leakage impact of the privacy risk index to obtain a privacy leakage risk assessment index;

[0011] The corresponding privacy leakage risk threshold is retrieved based on the personal basic information, and a leakage risk warning judgment is performed on the privacy leakage risk assessment index accordingly.

[0012] Furthermore, the basic personal information includes at least an anonymous identifier, age and gender;

[0013] The test sample information includes at least the sample collection time, collection site and viral load measurement value.

[0014] Furthermore, the full-process information security parameters include storage environment security parameters, transmission process security parameters and information usage security parameters.

[0015] Furthermore, the storage environment security parameter adopts any one of the access control authority level and the data encryption storage mode quantified value, or a weighted comprehensive value of the two parameters;

[0016] The transmission process security parameter adopts any one of the transmission protocol security level and the encryption channel state quantization value, or a weighted comprehensive value of the two parameters;

[0017] The information usage security parameter adopts any one of the parameters of the number of access personnel, the number of access times, the access time and the quantitative value of the operation content, or a weighted comprehensive value of the two parameters.

[0018] Furthermore, the method for obtaining the privacy feature set includes:

[0019] Use symmetric encryption algorithm to encrypt the test sample information;

[0020] A unique session key is assigned to each piece of test sample information. The session key is derived from the master key, which is stored in the hardware security module.

[0021] According to the session key corresponding to the test sample information, the test sample information such as the sample collection time, collection site and viral load measurement value is encrypted field by field, and after the encryption is completed, the HPV test encrypted data is generated;

[0022] The encrypted HPV test data is input into the pre-trained privacy feature recognition model for in-depth analysis to identify potential privacy features and form a privacy feature set.

[0023] Furthermore, the formula for calculating the privacy risk index is:

[0024]

[0025] Among them, R represents the privacy risk index, n represents the number of features in the privacy feature set, S i represents the risk score of the i-th feature, W irepresents the weight of the i-th feature.

[0026] Furthermore, based on the full-process information security parameters, a privacy leakage impact quantitative assessment is performed on the privacy risk index to obtain a privacy leakage risk assessment index:

[0027] Based on medical information security practices and expert experience, weights are assigned to storage environment security parameters, transmission process security parameters, and information use security parameters;

[0028] Mapping the quantitative values ​​of each security parameter in the whole process information security parameters into an impact coefficient;

[0029] According to the influence coefficient of each security parameter, the privacy risk index is corrected to obtain the corrected privacy risk index;

[0030] Taking the revised privacy risk index as the basis and combining it with the impact of security parameters throughout the entire process, the privacy leakage risk assessment index is calculated.

[0031] On the other hand, the present application also provides an HPV detection information management system based on privacy protection, the system comprising:

[0032] The data collection and feature extraction module obtains the HPV test information of the target subject and performs feature extraction to obtain personal basic information and test sample information;

[0033] An information encryption and privacy feature identification module encrypts the test sample information to obtain HPV test encrypted data, and identifies privacy features on the data to obtain a privacy feature set;

[0034] a privacy risk assessment module, performing a privacy risk assessment on the privacy feature set to obtain a privacy risk index;

[0035] The full-process security parameter collection module obtains the full-process information security parameters of the target examinee's HPV test information;

[0036] A quantitative assessment module, which performs a quantitative assessment of the privacy leakage impact of the privacy risk index based on the full-process information security parameters to obtain a privacy leakage risk assessment index;

[0037] The leakage risk warning judgment module retrieves the corresponding privacy leakage risk threshold based on the personal basic information, and performs leakage risk warning judgment on the privacy leakage risk assessment index accordingly.

[0038] In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program implements the steps of any one of the above methods when executed by the processor.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in any one of the above methods when executed by a processor.

[0040] Compared with the existing technology, the advantages of the present invention are as follows: the method covers the entire process of data collection, processing, storage, transmission, and use, breaking through the traditional "single-point encryption" model; in the collection stage, basic personal information and test sample information are separated by feature extraction to reduce the exposure of sensitive data; full-process information security parameters are introduced in the storage and transmission links to achieve quantitative monitoring of the security status of each link, avoiding the overall risk caused by vulnerabilities in a single link; through privacy feature identification and privacy risk index calculation, high-risk data subsets are accurately located; combined with real-time security parameters in the storage, transmission, and use links, the actual impact of privacy leakage is dynamically evaluated, changing the limitations of traditional encryption management;

[0041] While encrypting the test sample information, the data sensitivity level is analyzed through a set of privacy features to avoid the resource waste or insufficient protection caused by traditional encryption. Based on personal basic information, personalized risk thresholds are retrieved and combined with real-time calculated risk assessment indexes to achieve accurate early warning. Compared with traditional methods that rely solely on fixed security policies, this method can perceive abnormal access behavior in real time and trigger timely warnings through quantitative assessment, shortening the risk response cycle.

[0042] Standardize and quantify security parameters in storage, transmission, and use, and support weighted comprehensive calculations to transform abstract security status into computable and comparable numerical indicators, facilitating automated system analysis and decision-making. Replace traditional real-name information with anonymous identifiers to protect patient privacy while retaining the basic identifiers required for data traceability and management, avoiding confusion in the testing process due to excessive anonymization. Decouple the encryption of test sample information from feature recognition to ensure that encrypted data can still be used for medical statistics and research, supporting the rational use of medical data while protecting privacy.

[0043] In the storage phase, the dual protection of access control and encrypted storage reduces the risk of unauthorized access by internal personnel or database leaks. In the transmission phase, real-time monitoring based on security protocols and encrypted channels prevents detection information leakage caused by network attacks. In the use phase, by recording access personnel, time, operation content and quantitative evaluation, it deters abuse and facilitates post-audit tracing.

[0044] This method fills the gap in systematic protection in traditional HPV testing information management through four core capabilities: full-process coverage, precise assessment, dynamic early warning, and quantitative security management. It not only improves the technical accuracy of privacy protection, but also takes into account the actual needs of medical services. It can improve the accuracy of threat identification and achieve early warning of risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of the present invention;

[0046] Figure 2 This is a structural diagram of an HPV testing information management method and system based on privacy protection. DETAILED DESCRIPTION

[0047] The present application is described below in conjunction with the accompanying drawings.

[0048] Example 1: Figure 1 As shown, the privacy protection-based HPV detection information management method and system of the present invention specifically include the following steps:

[0049] S1. Obtaining HPV test information of the target subject and performing feature extraction to obtain basic personal information and test sample information; the basic personal information includes at least an anonymized identifier, age, and gender; the test sample information includes at least sample collection time, collection site, and viral load measurement value;

[0050] Directly connect to hospital information systems, laboratory information management systems, or HPV testing equipment through standardized medical data interfaces. After sample analysis is complete, the testing equipment can transmit the raw test data to the management system in real time through the standardized medical data interfaces. Alternatively, the system can retrieve the entered patient information from the laboratory information management system to ensure real-time and accurate data collection.

[0051] The collected raw data may have problems such as inconsistent formats, missing values, or outliers. The system cleans the raw data using regular expressions and data validation rules, removes invalid characters, fills in default values, and parses unstructured data into structured fields to facilitate subsequent feature extraction.

[0052] An irreversible hash algorithm or differential privacy technology is used to anonymize the real identity information of the examinee to generate a unique anonymous identifier. The name and ID number are combined and converted into a fixed-length string through a hash algorithm to ensure the uniqueness of the identifiers of different examinees, and at the same time, the real identity cannot be reversely parsed.

[0053] Separate basic personal information such as age and gender from the original data and desensitize some sensitive fields; replace the date of birth with an age range to retain statistical value while reducing the risk of privacy leakage;

[0054] Standardize sample collection times by time zone and format to ensure that timestamps can be used for subsequent process traceability; map collection site descriptions to standardized medical terms to facilitate data classification and statistical analysis;

[0055] For viral load values, unit unification, outlier detection, and data normalization are performed to ensure the accuracy and comparability of the measured values, providing a reliable data basis for subsequent risk assessment;

[0056] After feature extraction is completed, the system verifies the data quality through integrity verification and consistency verification; the data that passes the verification will be encrypted and stored in a medical-grade database, and a data traceability log will be recorded.

[0057] In this step, through the medical-grade data interface, it is directly connected to the hospital information system, laboratory information management system and testing equipment to achieve "real-time collection of the source" of the original test data, avoiding delays and transcription errors caused by manual entry; it supports real-time synchronization of new test data, and can also retrieve historical archived information in batches to ensure that all examinee data are included in the privacy protection scope and eliminate the protection blind spots caused by "data islands"; through regular expressions and data verification rules, invalid characters are removed, outliers are corrected, and default identifiers are filled in to lay the foundation for subsequent privacy feature extraction; the test report text is automatically parsed into structured fields, which reduces manual processing costs while making the data directly available for algorithm analysis; the hash algorithm is used to perform one-way encryption on the real identity information to generate a unique anonymous identifier, ensuring that the identifier is traceable but cannot be reversely decrypted to restore the identity; the date of birth is converted into age ranges, hidden addresses, etc. Privacy information, while retaining its medical statistical value, will reduce the risk of direct identity association; unify the collection time format to ensure the consistency of timestamps in cross-regional medical collaboration, which can be used to analyze risk scenarios such as "abnormal visits at night" and "high-frequency operations in a short period of time after detection"; map the collection site to the internationally accepted medical terminology standard code to eliminate natural language ambiguity, and facilitate the statistics of privacy leakage hotspots by anatomical site; unify the unit of viral load, detect outliers and normalize it to provide reliable input for the subsequent calculation of privacy risk index, and avoid risk misjudgment due to data noise; through integrity verification and consistency verification, ensure that the data entering the database complies with medical regulations and improve the data quality pass rate; the data that passes the verification is encrypted and stored in a medical-grade database, and records the traceability logs such as data collection time, interface source, and operator to meet the requirements of storage security and audit traceability, and can quickly respond to regulatory query needs.

[0058] S2. Encrypt the test sample information to obtain HPV test encrypted data, and identify privacy features on the data to obtain a privacy feature set;

[0059] A symmetric encryption algorithm is used to encrypt the test sample information. The symmetric encryption algorithm has high encryption strength and is widely used in fields with strict data security requirements, such as medicine. It can resist attacks such as brute force cracking and ensure the confidentiality of data during storage and transmission.

[0060] Use a key management system to generate, store, and manage encryption keys. Assign a unique session key to each piece of test sample information. The session key is derived from the master key, which is stored in the hardware security module to ensure key security and reliability. Regularly replace the session key to prevent security risks caused by long-term key use.

[0061] When the test sample information is transmitted from step S1 to step S2, the system automatically calls the key management system to obtain the corresponding session key, encrypts the test sample information such as sample collection time, collection site, and viral load measurement value field by field, and generates HPV test encrypted data after encryption is completed, ensuring that sensitive information exists in ciphertext form;

[0062] Identify characteristics associated with privacy risks, including high-risk viral infection information, abnormal viral load values, and special collection sites;

[0063] Privacy features are identified by combining machine learning algorithms with rule engines. A rule library containing various privacy feature patterns is pre-built. At the same time, historical test sample data is trained using machine learning algorithms to learn the distribution patterns and correlations of privacy features. HPV test encrypted data is first quickly screened through a rule engine, and then in-depth analysis is performed using machine learning algorithms to identify potential privacy features and form a privacy feature set.

[0064] In this step, a symmetric encryption algorithm is used to encrypt the test sample information. This algorithm has high encryption strength and is widely used in medical and other fields with strict data security requirements. It can effectively resist attacks such as brute force cracking, ensure the confidentiality of data during storage and transmission, and prevent the test sample information from being illegally obtained and tampered with. A key management system is used to generate, store and manage encryption keys, and a unique session key is assigned to each test sample information. The session key is derived from the master key stored in the hardware security module, which greatly improves the security and reliability of the key. At the same time, regular replacement of the session key can effectively prevent the security risks caused by long-term use of the key, and further ensure the security of the encrypted data. When the test sample information is transmitted from step S1 to step S2, the system automatically calls the key management system to obtain the corresponding session key for field-by-field encryption. After the encryption is completed, HPV test encrypted data is generated, which realizes the automation of the encryption process, improves data processing efficiency, and can ensure that sensitive information always exists in ciphertext form, reducing the risk of data leakage. It clarifies the risks related to privacy Risk-related features provide a clear goal and direction for privacy feature identification, which helps to more accurately discover key content that may involve personal privacy in test sample information; privacy feature identification is performed by combining machine learning algorithms with rule engines; a rule library containing various privacy feature patterns is pre-built to quickly screen HPV test encrypted data and quickly locate some obvious privacy features; at the same time, historical test sample data is trained using machine learning algorithms to learn the distribution patterns and correlations of privacy features, which can conduct in-depth analysis of encrypted data and identify some hidden potential privacy features that are difficult to define through rules; this method combines the high efficiency of rule engines and the intelligence of machine learning algorithms, greatly improving the accuracy and efficiency of privacy feature identification, and providing a reliable basis for subsequent privacy risk assessment and management; this step provides a solid security guarantee for HPV test information management through effective encryption processing and accurate privacy feature identification, which helps to protect the personal privacy of the examinees and reduce the risk of privacy leakage.

[0065] S3. Performing a privacy risk assessment on the privacy feature set to obtain a privacy risk index;

[0066] Based on medical industry standards, privacy protection laws and regulations, and expert experience, the features in the privacy feature set are divided into different categories, including high-risk virus infection information, abnormal viral load values, and special collection sites. For each category, a sensitivity level is set, divided into three levels: high, medium, and low.

[0067] Develop specific scoring criteria for each sensitivity level; highly sensitive features are given higher scores of 8–10 points; medium sensitive features are given 4–7 points; and low sensitive features are given 1–3 points. Each privacy feature is assigned a corresponding sensitivity score based on its category and sensitivity level according to the scoring criteria.

[0068] Using data mining techniques, such as association rule mining algorithms, we analyze the features in the privacy feature set and identify the correlations between them. This analysis found a strong correlation between specific collection sites and high-risk viral infection information. This means that when these two features appear simultaneously, they may reveal more detailed health information about the patient.

[0069] Based on the results of association rule mining, the strength of association between features is calculated. Association strength can be measured by indicators such as support and confidence. Support represents the proportion of records containing a specific feature combination in all records, and confidence represents the probability of containing another feature when one feature is included. If the support of a feature combination is 0.3 and the confidence is 0.8, it means that the feature combination is relatively common in the data, and the appearance of one feature has a strong predictive effect on the appearance of another feature. Based on the size of the association strength, the feature association is assigned a corresponding score. The higher the association strength, the higher the score.

[0070] Analyze the propagation path of each privacy feature by combining the business processes and data flows of the HPV testing information management system;

[0071] Quantify the transmission risk of each privacy feature based on factors such as the complexity of the transmission path, the scope involved, and the security measures of the data recipient. If a privacy feature needs to be transmitted to multiple external partners and the security measures of these partners vary, then the transmission risk of this feature is higher.

[0072] The analytic hierarchy process (AHP) is used to determine the weight of each privacy feature in the overall privacy risk assessment. The AHP constructs a judgment matrix, compares the importance of each feature, and calculates the weight of each feature.

[0073] The privacy risk index is calculated using a weighted summation method based on the sensitivity score, relevance score, propagation risk score, and corresponding weight of each privacy feature.

[0074] The formula for calculating the privacy risk index is:

[0075]

[0076] Among them, R represents the privacy risk index, n represents the number of features in the privacy feature set, S i represents the risk score of the i-th feature, Wi represents the weight of the i-th feature.

[0077] In this step, based on medical standards, regulations and expert experience, privacy features are classified and graded, and scoring standards are formulated. Data mining technology is combined to quantify the strength of feature associations, and the transmission risk is analyzed in combination with business processes. The weights are determined through the hierarchical analysis method, and the privacy risk index is finally calculated by weight, which converts the fuzzy risk assessment into a precise value, improves the accuracy of risk assessment, and avoids the one-sidedness and arbitrariness of human assessment. This step conducts an assessment from three key dimensions: feature sensitivity, feature correlation, and transmission risk. The sensitivity score directly measures the privacy sensitivity of the feature itself. Association rule mining identifies the potential risks of feature combinations. Transmission path analysis considers the risks in data flow. It can capture more complex and hidden risks, and discover high-risk scenarios when special collection sites and high-risk virus infection information appear at the same time. Improve the completeness of risk scenario coverage; combine the business processes and data flows of the HPV testing information management system to analyze transmission risks, so that the assessment results are closely aligned with actual medical scenarios; assign higher risk scores to features that need to be transmitted to multiple external cooperative institutions and have uneven security guarantees, thereby prompting medical institutions to focus their resources on the prevention and control of high-risk links, improve the utilization rate of privacy protection resources, and avoid inefficient protection caused by average resource distribution; a clear privacy risk index provides a quantitative basis for subsequent risk disposal. When the index exceeds the threshold, the corresponding level of warning and protection measures can be immediately triggered, which can increase the risk warning response speed compared to traditional vague risk descriptions; at the same time, the index also makes it easier for medical managers to intuitively grasp the overall risk situation and assist in formulating long-term privacy protection strategies and resource planning.

[0078] S4. Obtaining the full-process information security parameters of the target examinee's HPV test information;

[0079] The full-process information security parameters include storage environment security parameters, transmission process security parameters and information usage security parameters;

[0080] The storage environment security parameter adopts any one of the access control authority level and the data encryption storage mode quantified value, or a weighted comprehensive value of the two parameters;

[0081] According to the importance and sensitivity of HPV test information, access rights are divided into different levels; it can be set to three levels of permissions: Level 1 permissions are for system administrators, who have the highest access rights to all test information, including data modification, deletion, and system configuration operations; Level 2 permissions are for relevant medical personnel, such as doctors and nurses responsible for interpreting test reports, who can view and partially edit test information directly related to patient diagnosis and treatment; Level 3 permissions are for ordinary staff, such as laboratory sample processing personnel, who can only access partial information related to the sample processing process;

[0082] Assign a corresponding score to each permission level, and calculate the comprehensive quantitative value of the access control permission level by counting the different permission access situations for HPV test information in the system;

[0083] There are two types of encryption: symmetric and asymmetric. Symmetric encryption is fast and efficient, making it suitable for storing large amounts of data. Asymmetric encryption offers greater security, but the encryption and decryption processes are relatively complex. Evaluate the encryption method used to store HPV test information and determine its strength and security. Assign scores to different encryption methods, and consider how encryption keys are managed.

[0084] If both access control permission levels and data encryption storage methods are used for quantification, different weights can be assigned to each according to actual conditions, and then the weighted sum can be used to obtain a comprehensive quantification value of the storage environment security parameter.

[0085] The transmission process security parameter adopts any one of the transmission protocol security level and the encryption channel state quantization value, or a weighted comprehensive value of the two parameters;

[0086] Analyze the network transmission protocol used to transmit HPV test information; the protocol encrypts data through an encryption layer for high security;

[0087] The security of transmission protocols is divided into three levels: high, medium and low;

[0088] Real-time monitoring of the status of the encryption channel, including channel connectivity, encryption key validity, data transmission integrity, etc.; using professional network monitoring tools to detect whether there are any anomalies in the encryption channel;

[0089] A quantitative score is calculated based on the status of the encryption channel. If the encryption channel always maintains normal connectivity, the key is valid, and data transmission is complete, the security level is high. If the channel is occasionally interrupted or the key expires, but can be restored in time, the security level is medium. If the encryption channel frequently fails, seriously affecting the security of data transmission, the security level is low.

[0090] Using a method similar to that used for storage environment security parameters, weights are assigned to the transmission protocol security level and the encryption channel state quantization value based on actual conditions. The weighted sum is then used to obtain the comprehensive quantization value of the transmission process security parameter.

[0091] The information usage security parameter adopts any one of the quantitative values ​​of the number of access personnel, the number of accesses, the access time and the operation content, or a weighted comprehensive value of the two parameters;

[0092] Through system log records, count the number of people who accessed HPV test information within a specific time period;

[0093] Directly use the actual number of visitors as the quantitative value;

[0094] Also based on the system log, count the number of times each visitor accessed the HPV test information within a specific time period;

[0095] Add up the number of visits of all visitors to get the total number of visits as a quantitative value;

[0096] Analyze the time distribution of visitors accessing HPV test information to determine whether there are abnormal access times;

[0097] You can set a normal access time range, count the number of visits during abnormal access time periods, and assign a certain weight;

[0098] Classify the operations performed by accessors on HPV test information. Different operations have different impacts on information security, with editing and deleting operations having relatively higher risks.

[0099] Assign corresponding scores to different operation types;

[0100] According to the actual situation, weights are assigned to the quantitative values ​​of the number of access personnel, number of accesses, access time and operation content, and then the weighted sum is performed to obtain the comprehensive quantitative value of the information use security parameter.

[0101] In this step, information security parameters are obtained from all aspects, from the storage environment, transmission process to information use, covering the entire life cycle of HPV test information from generation to utilization; this makes the protection of information security no longer limited to a single link, but forms a complete protection system, effectively reducing the risk of privacy leakage of information in each link; multiple parameters are used for quantitative evaluation in each link, the transmission process is combined with the security level of the transmission protocol and the status of the encryption channel, and information use covers multiple aspects such as the number of access personnel, number of accesses, access time and operation content; it can reflect the information security status more comprehensively and meticulously, and avoid ignoring potential security risks due to the limitations of a single parameter evaluation; in the acquisition of storage environment security parameters, access rights are divided into three levels and assigned corresponding scores, and a comprehensive quantitative value is calculated by counting the access situations of different permissions; it can accurately identify the access behavior of personnel with different permissions to information, and promptly discover abnormal permission access, thereby effectively preventing the risk of privacy leakage caused by improper permission management; in the acquisition of transmission process security parameters, the transmission protocol is divided into security levels, and the encryption is monitored in real time. Quantitative scoring of channel status; helps to accurately judge the security level of data during transmission, and promptly discover security vulnerabilities in transmission protocols or abnormalities in encryption channels, so that timely measures can be taken to repair and improve them; in the acquisition of information usage security parameters, detailed statistics and quantification of the number of access personnel, number of accesses, access time and operation content are carried out; by analyzing these data, abnormal usage behaviors can be accurately identified, so as to promptly discover potential privacy leakage risk points, and provide a strong basis for subsequent risk warning and prevention; the obtained full-process information security parameters provide an accurate data basis for the subsequent quantitative assessment of privacy leakage impact; by weighted summation and other processing of these parameters, a comprehensive quantitative assessment index can be obtained, making the assessment of information security status more objective and scientific, and avoiding the arbitrariness of subjective judgment; based on the obtained information security parameters and subsequent assessment index, the corresponding privacy leakage risk threshold can be retrieved in combination with personal basic information to make leakage risk warning judgments; it helps to discover possible privacy leakage risks in advance, and take corresponding measures to prevent and deal with them in a timely manner to protect the privacy security of the examinees.

[0102] S5. Based on the full-process information security parameters, perform a quantitative assessment of the privacy leakage impact on the privacy risk index to obtain a privacy leakage risk assessment index;

[0103] Based on medical information security practices and expert experience, weights are assigned to storage environment security parameters, transmission process security parameters, and information usage security parameters. The weights can be dynamically adjusted based on the actual security status of the hospital information system and the results of historical data leakage analysis.

[0104] Mapping the quantitative values ​​of each security parameter in the whole process information security parameters into an impact coefficient;

[0105] According to the influence coefficient of each security parameter, the privacy risk index is modified to obtain the modified privacy risk index;

[0106] Taking the revised privacy risk index as the basis and combining it with the impact of security parameters throughout the entire process, the privacy leakage risk assessment index is calculated.

[0107] In this step, by incorporating security parameters from three key links—the storage environment, the transmission process, and information usage—a cross-linked assessment of the privacy risks of HPV testing information throughout its lifecycle is achieved, avoiding the limitations of single-link risk assessment and meeting the industry requirement of "full-process data protection" in medical information security. Dynamic adjustment of parameter weights based on the actual security status of hospital information systems and historical leakage data can adapt to the differences in security management levels among different medical institutions, making risk assessment more tailored to actual scenarios and enhancing the adaptive capabilities of the assessment model. Various security parameters are converted into standardized impact coefficients, transforming abstract security status into calculable quantitative indicators. Combined with the privacy risk index, a hierarchical risk assessment system is formed, facilitating an intuitive understanding of risk levels by security managers. The initial privacy risk index is corrected using the security parameter impact coefficient to reflect the actual suppression effect of current system protection measures on privacy risks, avoiding the one-sidedness of risk assessment based solely on data characteristics. The resulting privacy leakage risk assessment index integrates the data's inherent risks with system protection capabilities, providing a more reliable basis for subsequent risk warnings. This helps medical institutions identify high-risk links in advance, optimize security strategies in a targeted manner, and reduce the probability of privacy leakage incidents.

[0108] S6. Retrieving a corresponding privacy leakage risk threshold based on the personal basic information, and performing a leakage risk warning judgment on the privacy leakage risk assessment index accordingly;

[0109] In the medical information management system, each examinee is assigned a unique anonymized identifier, which isolates the examinee from specific identity information but serves as a key index to associate their privacy leakage risk threshold;

[0110] A mapping table is pre-built between anonymized identifiers and privacy leakage risk thresholds. This table classifies subjects of different age groups and genders into different risk levels based on factors such as their cancer risk, social attention, and privacy sensitivity, and sets corresponding privacy leakage risk thresholds for each level.

[0111] After obtaining the anonymous identifier of the target subject, the corresponding privacy leakage risk threshold is quickly located and retrieved by querying the mapping table;

[0112] Age and gender are important factors that affect the privacy leakage risk threshold. The privacy sensitivity of HPV test information varies among subjects of different age groups and genders. The system has built-in threshold adjustment rules based on age and gender.

[0113] After the basic threshold based on the anonymized identifier is retrieved, the basic threshold is dynamically adjusted according to the age and gender information of the target subject. The adjustment method can adopt the weighted average method to calculate the final privacy leakage risk threshold based on the weight of the impact of age and gender on the privacy leakage risk;

[0114] The privacy leakage risk assessment index is compared with the retrieved privacy leakage risk threshold in real time. If the privacy leakage risk assessment index is greater than or equal to the privacy leakage risk threshold, it is determined that there is a privacy leakage risk and the early warning process is initiated. If the privacy leakage risk assessment index is less than the privacy leakage risk threshold, it is determined that the current privacy leakage risk is within an acceptable range and the system continues with regular monitoring.

[0115] Based on the difference between the privacy leakage risk assessment index and the threshold, the system divides warnings into different levels, including low-risk warnings, medium-risk warnings, and high-risk warnings;

[0116] For low-risk warnings, a reminder message will be sent to relevant medical staff through the internal messaging system, reminding them to pay attention to the information usage of the examinee and strengthen security protection measures;

[0117] For medium-risk warnings, not only will a notification be sent to medical staff, but an alert will also be sent to the hospital's information security management department, requiring them to focus on monitoring the examinee's information and conduct further security checks;

[0118] For high-risk warnings, a comprehensive emergency response mechanism will be triggered immediately, including suspending access to the examinee's information, initiating data leak tracing procedures, notifying hospital management and relevant regulatory authorities, etc., to minimize the losses caused by privacy leakage risks.

[0119] In this step, the basic threshold is dynamically adjusted by combining factors such as age and gender through the anonymous identification association mapping table, fully considering the differences in cancer risk, social attention and privacy sensitivity of different groups of examinees, so that the privacy leakage risk threshold setting is more in line with the actual situation and the accuracy of risk assessment is improved; the privacy leakage risk assessment index is compared with the threshold in real time, which can timely discover potential risks, divide different warning levels according to the size of the difference, take corresponding measures for different levels, achieve rapid response, and effectively reduce the losses caused by privacy leakage risks; different warning levels correspond to different processing methods, low-risk warnings remind medical staff to pay attention to information use and strengthen protection; medium-risk warnings, in addition to notifying staff, also require information security management departments to focus on monitoring and inspection; high-risk warnings trigger a comprehensive emergency response mechanism; this hierarchical management method makes resource allocation more reasonable and improves management efficiency; through accurate warnings and effective response measures, the risk of patient privacy leakage is minimized to the greatest extent, the privacy, reputation and personal safety of the examinees are protected, and the patient's trust in the medical information management system is enhanced.

[0120] Example 2: Figure 2 As shown, the privacy protection-based HPV detection information management method and system of the present invention specifically include the following modules:

[0121] The data collection and feature extraction module obtains the HPV test information of the target subject and performs feature extraction to obtain personal basic information and test sample information;

[0122] An information encryption and privacy feature identification module encrypts the test sample information to obtain HPV test encrypted data, and identifies privacy features on the data to obtain a privacy feature set;

[0123] a privacy risk assessment module, performing a privacy risk assessment on the privacy feature set to obtain a privacy risk index;

[0124] The full-process security parameter collection module obtains the full-process information security parameters of the target examinee's HPV test information;

[0125] A quantitative assessment module, which performs a quantitative assessment of the privacy leakage impact of the privacy risk index based on the full-process information security parameters to obtain a privacy leakage risk assessment index;

[0126] The leakage risk warning judgment module retrieves the corresponding privacy leakage risk threshold based on the personal basic information, and performs leakage risk warning judgment on the privacy leakage risk assessment index accordingly.

[0127] The system achieves full lifecycle management of HPV test information, from acquisition and processing to storage, transmission, and use, through the collaboration of multiple modules, including data collection and feature extraction, information encryption and privacy feature identification, and full-process security parameter collection. The information encryption and privacy feature identification module encrypts test sample information, while the full-process security parameter collection module monitors security parameters in storage, transmission, and use. This addresses the drawback of traditional methods that simply encrypt only some key information, effectively ensuring the privacy and security of information at all stages.

[0128] Traditional management struggles to accurately identify potential privacy threats. This system's privacy risk assessment module evaluates a set of privacy features to obtain a privacy risk index and quantify potential risks. The quantitative assessment module further quantifies the impact of privacy leakage on the privacy risk index based on full-process information security parameters, generating a privacy leakage risk assessment index that more accurately identifies potential privacy threats.

[0129] In the past, monitoring and assessment of information security parameters during information storage and transmission were insufficient. The system's full-process security parameter collection module specifies in detail the security parameters for the storage environment, transmission process, and information usage. The quantitative assessment module analyzes these parameters and assesses privacy leakage risks based on the analysis results. This effectively monitors and assesses the security status of each link, allowing for timely detection and mitigation of data leakage risks.

[0130] Since traditional methods cannot provide timely and accurate warnings for privacy leakage risks, the leakage risk warning judgment module of this system retrieves the corresponding privacy leakage risk threshold based on personal basic information and compares the privacy leakage risk assessment index with it. Once the assessment index exceeds the threshold, an early warning can be issued. Compared with traditional methods, this greatly improves the timeliness and accuracy of risk warnings, and measures can be taken in advance to avoid threats to the privacy, reputation and personal safety of the examinee.

[0131] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for managing HPV detection information based on privacy protection, characterized in that: The method comprises: Obtain the HPV test information of the target subject and perform feature extraction to obtain basic personal information and test sample information; Encrypting the test sample information to obtain HPV test encrypted data, and performing privacy feature identification on the data to obtain a privacy feature set; Performing a privacy risk assessment on the privacy feature set to obtain a privacy risk index; Obtain the full-process information security parameters of the target examinee's HPV test information; Based on the full-process information security parameters, quantitatively assess the privacy leakage impact of the privacy risk index to obtain a privacy leakage risk assessment index; The corresponding privacy leakage risk threshold is retrieved based on the personal basic information, and a leakage risk warning judgment is performed on the privacy leakage risk assessment index accordingly.

2. The method for managing HPV detection information based on privacy protection according to claim 1, wherein: The basic personal information includes at least anonymized identifier, age and gender; The test sample information includes at least the sample collection time, collection site and viral load measurement value.

3. The privacy protection-based HPV detection information management method according to claim 2, wherein: The full-process information security parameters include storage environment security parameters, transmission process security parameters and information usage security parameters.

4. The method for managing HPV detection information based on privacy protection according to claim 3, wherein: The storage environment security parameter adopts any one of the access control authority level and the data encryption storage mode quantified value, or a weighted comprehensive value of the two parameters; The transmission process security parameter adopts any one of the transmission protocol security level and the encryption channel state quantization value, or a weighted comprehensive value of the two parameters; The information usage security parameter adopts any one of the parameters of the number of access personnel, the number of access times, the access time and the quantitative value of the operation content, or a weighted comprehensive value of the two parameters.

5. The method for managing HPV detection information based on privacy protection according to claim 1, wherein: The method for obtaining the privacy feature set includes: Use symmetric encryption algorithm to encrypt the test sample information; A unique session key is assigned to each piece of test sample information. The session key is derived from the master key, which is stored in the hardware security module. According to the session key corresponding to the test sample information, the test sample information such as the sample collection time, collection site and viral load measurement value is encrypted field by field, and after the encryption is completed, the HPV test encrypted data is generated; The encrypted HPV test data is input into the pre-trained privacy feature recognition model for in-depth analysis to identify potential privacy features and form a privacy feature set.

6. The method for managing HPV detection information based on privacy protection according to claim 1, wherein: The formula for calculating the privacy risk index is: Among them, R represents the privacy risk index, n represents the number of features in the privacy feature set, S i represents the risk score of the i-th feature, W i represents the weight of the i-th feature.

7. The method for managing HPV testing information based on privacy protection according to claim 1, wherein: Based on the full-process information security parameters, a privacy leakage impact quantitative assessment is performed on the privacy risk index to obtain a privacy leakage risk assessment index: Based on medical information security practices and expert experience, weights are assigned to storage environment security parameters, transmission process security parameters, and information use security parameters; Mapping the quantitative values ​​of each security parameter in the whole process information security parameters into an impact coefficient; According to the influence coefficient of each security parameter, the privacy risk index is corrected to obtain the corrected privacy risk index; Taking the revised privacy risk index as the basis and combining it with the impact of security parameters throughout the entire process, the privacy leakage risk assessment index is calculated.

8. An HPV detection information management system based on privacy protection, characterized in that: The system comprises: The data collection and feature extraction module obtains the HPV test information of the target subject and performs feature extraction to obtain personal basic information and test sample information; An information encryption and privacy feature identification module encrypts the test sample information to obtain HPV test encrypted data, and identifies privacy features on the data to obtain a privacy feature set; a privacy risk assessment module, performing a privacy risk assessment on the privacy feature set to obtain a privacy risk index; The full-process security parameter collection module obtains the full-process information security parameters of the target examinee's HPV test information; A quantitative assessment module, which performs a quantitative assessment of the privacy leakage impact of the privacy risk index based on the full-process information security parameters to obtain a privacy leakage risk assessment index; The leakage risk warning judgment module retrieves the corresponding privacy leakage risk threshold based on the personal basic information, and performs leakage risk warning judgment on the privacy leakage risk assessment index accordingly.

9. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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