An explainable dynamic security protection method for medical big data

Abstract

The present invention discloses an interpretable medical big data dynamic security protection method. The method relates to the field of security technology digital processing technology, and includes the following steps: collecting and processing infectious disease patient data; performing classification and encryption processing; real-time monitoring of encrypted data; obtaining an encryption security assessment index for infectious disease patient identity information data, an infectious disease patient mobile path key management security assessment index, and an infectious disease patient electronic medical record data leakage risk assessment index; comprehensive analysis, evaluation, and adjustment. The present invention improves the dynamic security protection dynamic capability of interpretable medical big data by evaluating and analyzing the encrypted data of infectious disease patients, and adjusts the dynamic security of medical big data according to the comprehensive evaluation results, thereby solving the problem of insufficient dynamic security protection dynamic capability of interpretable medical big data in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of security technology digital processing technology, and in particular to an explainable medical big data dynamic security protection method. Background Art

[0002] With the development of informatization and digitalization in the medical industry, the security of medical data, such as personal health information and case records, is directly related to the privacy of patients and the quality of medical services due to its sensitivity. Therefore, it is crucial to protect medical data from illegal access, leakage, tampering and loss. With the development of technology, data security governance in the medical industry faces more and more challenges. For example, the multi-database system used by medical institutions, the traditional operation and maintenance methods lead to chaotic account management, and the risk of sensitive data diffusion caused by data interconnection. However, large amounts of data collection also bring many data security and privacy issues to big data medical care. The parts of the data set related to the patient's personal health information, such as medical records and drug prescriptions, may lead to personal privacy leakage. In addition, the recording and sharing of medical data will make the data traceable. When the data is used inappropriately, it will cause a series of serious consequences.

[0003] An existing explainable dynamic security protection method for medical big data uses encryption technology to protect medical data from illegal access and leakage, including encryption during data transmission and data storage encryption; by implementing strict access control and identity authentication mechanisms, it can ensure that only authorized personnel can access sensitive data. This usually involves multi-factor authentication, role-based access control, and attribute-based access control; through data desensitization technology, sensitive information such as patient names and addresses can be hidden.

[0004] For example, the invention patent announcement with announcement number: CN118051948B discloses a dynamic and secure storage method based on a big data platform, including: judging the data security level of each piece of historical data in the historical database of the big data platform and the location security level of the storage location of each piece of historical data, and determining the permitted storage method for each piece of historical data according to the judgment result; determining the storage mapping table for each batch of historical data in the historical database according to the permitted storage method; dividing the storage area of ​​the big data platform according to all storage mapping tables, and setting a storage dynamic threshold for each divided area; parsing the latest stored data and combining the setting results of the divided areas to dynamically and securely store the latest stored data.

[0005] For example, the invention patent with publication number: CN117195291A discloses a method for secure storage of medical data based on a heat grading strategy, including detecting the data to be stored; using the heat grading strategy to process the detected data to be stored: dividing the heat based on the evaluation characteristics, and classifying the storage level based on the heat; and storing the classified data in an expandable storage medium, regularly verifying the heat of the stored data, and changing the storage level accordingly; outputting and saving summary information of data storage at all levels for easy query, and including saving the attack features detected during the data storage process, and using these features to optimize the performance of the intelligent attack detection model to ensure the completeness of the processing flow of user needs, while continuously enhancing the system performance; being able to adjust the task progress in real time; storing the data through the designed heat grading strategy to realize the storage of dynamically changing data; designing an intelligent attack detection model optimization method to improve the system's detection performance against attacks.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, since the real-time status of dynamic security protection of medical big data is difficult to adjust, it is difficult to manage medical big data in high-speed data streams, and there is an explainable problem of insufficient dynamic capabilities of dynamic security protection of medical big data. Summary of the invention

[0008] The embodiments of the present application solve the problem of insufficient dynamic capability of interpretable dynamic security protection of medical big data in the prior art by providing an interpretable dynamic security protection method for medical big data, and improve the dynamic capability of interpretable dynamic security protection of medical big data.

[0009] The embodiment of the present application provides an explainable medical big data dynamic security protection method, comprising the following steps: collecting and processing infectious disease patient data; classifying and encrypting the cleaned infectious disease patient data to obtain infectious disease patient encrypted data; monitoring the infectious disease patient encrypted data in real time, and tracking the use and access of the infectious disease patient encrypted data; performing a first performance evaluation analysis of medical big data dynamic security protection on the infectious disease patient encrypted data to obtain an infectious disease patient identity information data encryption security evaluation index; performing a second performance evaluation analysis of medical big data dynamic security protection on the infectious disease patient encrypted data to obtain an infectious disease patient mobile path key management security evaluation index; performing a third performance evaluation analysis of medical big data dynamic security protection on the infectious disease patient encrypted data to obtain an infectious disease patient electronic medical record data leakage risk assessment index; performing a comprehensive analysis based on the infectious disease patient identity information data encryption security evaluation index, the infectious disease patient mobile path key management security evaluation index and the infectious disease patient electronic medical record data leakage risk assessment index to obtain a medical big data dynamic security comprehensive adjustment evaluation index; and adjusting the medical big data dynamic security according to the results of the medical big data dynamic security comprehensive adjustment evaluation analysis.

[0010] Furthermore, the specific process of collecting and processing infectious disease patient data is: collecting infectious disease patient data from a medical big data database, and cleaning the collected infectious disease patient data.

[0011] Furthermore, the specific process of obtaining the encrypted data of infectious disease patients is: obtaining the encrypted data of infectious disease patient identity information, the encrypted key data of infectious disease patient movement path and the encrypted data of infectious disease patient electronic medical records through open source disk encryption software.

[0012] Furthermore, the specific process of tracking the use and access of the encrypted data of infectious patients is: by setting up a monitoring server to monitor the encrypted data of infectious patients in real time, and obtaining the number of uses and accesses to the encrypted infectious patients' data.

[0013] Furthermore, the specific process of obtaining the encryption security assessment index of infectious disease patient identity information data is as follows: the infectious disease patient identity information encryption data includes the coverage rate of infectious disease patient data encryption transmission times, the coverage rate of infectious disease patient data encryption storage times and the frequency of use of infectious disease patient identity information keys; the number of infectious disease patient data encryption transmission times and the total number of infectious disease patient data transmissions are extracted from the medical big data database, and the coverage rate of infectious disease patient data encryption transmission times is the ratio of infectious disease patient data encryption transmission times to the total number of infectious disease patient data transmissions; the number of infectious disease patient data encryption storage times and the total number of infectious disease patient data storages are extracted from the medical big data database, and the coverage rate of infectious disease patient data encryption storage times is the ratio of infectious disease patient data encryption storage times to the total number of infectious disease patient data storages; the actual number of uses of infectious disease patient identity information keys and the preset number of uses of infectious disease patient identity information keys are extracted from the medical big data database, and the frequency of use of infectious disease patient identity information keys is the ratio of the actual number of uses of infectious disease patient identity information keys to the preset number of uses; and the encryption security assessment index of infectious disease patient identity information data is obtained through comprehensive analysis.

[0014] Furthermore, the specific process of obtaining the infectious disease patient mobile path key management security assessment index is as follows: the infectious disease patient mobile path encryption key data, including the infectious disease patient mobile path encryption key strength, the infectious disease patient mobile path key usage frequency and the infectious disease patient mobile path key rotation frequency; the infectious disease patient mobile path key bit number and the infectious disease patient mobile path key base bit number extracted from the medical big data database, the infectious disease patient mobile path encryption key strength is the ratio of the infectious disease patient mobile path key bit number to the key base bit number; the actual number of uses of the infectious disease patient mobile path key and the preset number of uses of the infectious disease patient mobile path key extracted from the medical big data database, the infectious disease patient mobile path key usage frequency is the ratio of the actual number of uses of the infectious disease patient mobile path key to the preset number of uses; the actual rotation frequency of the infectious disease patient mobile path key and the maximum rotation frequency of the infectious disease patient mobile path key extracted from the medical big data database, the infectious disease patient mobile path key rotation frequency is the ratio of the actual rotation frequency of the infectious disease patient mobile path key to the maximum rotation frequency; comprehensive analysis is performed to obtain the infectious disease patient mobile path key management security assessment index.

[0015] Furthermore, the specific process of obtaining the risk assessment index for electronic medical records of infectious disease patients is as follows: the electronic medical records of infectious disease patients are encrypted, including the update frequency of electronic medical records of infectious disease patients, the access frequency of electronic medical records of infectious disease patients and the coverage rate of the monitoring system for electronic medical records of infectious disease patients; the actual update times of electronic medical records of infectious disease patients and the maximum update times of electronic medical records of infectious disease patients are extracted from the medical big data database, and the update frequency of electronic medical records of infectious disease patients is the ratio of the actual update times of electronic medical records of infectious disease patients to the maximum update times; the access times of electronic medical records of infectious disease patients and the maximum access times of electronic medical records of infectious disease patients are extracted from the medical big data database, and the access frequency of electronic medical records of infectious disease patients is the ratio of the access times of electronic medical records of infectious disease patients to the maximum access times; the number of vulnerabilities covered by the monitoring system for electronic medical records of infectious disease patients and the total number of vulnerabilities of the monitoring system for electronic medical records of infectious disease patients are extracted from the medical big data database, and the coverage rate of the monitoring system for electronic medical records of infectious disease patients is the ratio of the number of vulnerabilities covered by the monitoring system for electronic medical records of infectious disease patients to the total number of vulnerabilities; and the risk assessment index for electronic medical records of infectious disease patients is obtained through comprehensive analysis.

[0016] Furthermore, the specific process of obtaining the dynamic security comprehensive adjustment evaluation index of medical big data is as follows: the dynamic security comprehensive adjustment evaluation index of medical big data is obtained through the encryption security evaluation index of the identity information data of infectious disease patients, the key management security evaluation index of the mobile path of infectious disease patients and the electronic medical record data leakage risk evaluation index of infectious disease patients; ; ; It represents the comprehensive adjustment evaluation index of dynamic security of medical big data. represents the encryption security assessment index of infectious disease patient identity information data, represents the security evaluation index of key management of infectious disease patient mobile paths, represents the risk assessment index of electronic medical record data leakage of infectious disease patients, The weight factor affecting the encryption security assessment index of the preset infectious disease patient identity information data obtained from the medical big data database, is the weight factor affecting the key management security assessment index of the preset infectious disease patient mobile path obtained from the medical big data database. It is the influencing weight factor of the risk assessment index of electronic medical record data leakage of infectious disease patients obtained from the medical big data database.

[0017] Furthermore, the specific process of adjusting the dynamic security of medical big data includes: importing the first risk judgment value, the second risk judgment value and the third risk judgment value of the dynamic security comprehensive adjustment of medical big data in the database into the dynamic security risk detection model of medical big data, and outputting the adjustment method and risk level through the dynamic security risk detection model of medical big data; the specific process of the adjustment method output by the dynamic security risk detection model of medical big data is: if the encryption security assessment index of the identity information data of infectious disease patients is less than the first risk judgment value of the dynamic security comprehensive adjustment of medical big data, then the frequency of replacing the key of the identity information data of infectious disease patients is increased by real-time monitoring of the actual number of uses of the identity information key of infectious disease patients; if the security assessment index of the key management of the mobile path of infectious disease patients is less than the second risk judgment value of the dynamic security comprehensive adjustment of medical big data, then the mobile path key of infectious disease patients is distributed through an encrypted VPN to implement multi-factor identity authentication; if the risk assessment index of the electronic medical record data leakage of infectious disease patients is greater than the third risk judgment value of the dynamic security comprehensive adjustment of medical big data, then the frequency of monitoring the electronic medical record data of infectious disease patients is increased by increasing the coverage of the dynamic security monitoring system of medical big data.

[0018] Furthermore, the specific process of outputting the risk level through the medical big data dynamic security risk detection model is: importing the medical big data dynamic security comprehensive adjustment evaluation index and the medical big data dynamic security comprehensive adjustment risk detection definition evaluation value in the database into the medical big data dynamic security risk detection model, and comprehensively analyzing to obtain the medical big data dynamic security comprehensive adjustment risk deviation value, which is the medical big data dynamic security comprehensive adjustment risk detection evaluation value, and the medical big data dynamic security risk detection model outputs the risk level according to the medical big data dynamic security comprehensive adjustment risk detection evaluation value; the process of the medical big data dynamic security risk detection model judging the risk level is: if the medical big data dynamic security comprehensive adjustment risk detection evaluation value is less than zero, then the medical big data dynamic security comprehensive adjustment risk level corresponding to the medical big data dynamic security comprehensive adjustment risk detection evaluation value is high risk; if the medical big data dynamic security comprehensive adjustment risk detection evaluation value is equal to zero, then the medical big data dynamic security comprehensive adjustment risk level corresponding to the medical big data dynamic security comprehensive adjustment risk detection evaluation value is medium risk; if the medical big data dynamic security comprehensive adjustment risk detection evaluation value is greater than zero, then the medical big data dynamic security comprehensive adjustment risk level corresponding to the medical big data dynamic security comprehensive adjustment risk detection evaluation value is low risk.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. Through the performance evaluation and analysis of the dynamic security protection of medical big data on the encrypted data of infectious disease patients, the dynamic security of medical big data is adjusted according to the comprehensive evaluation results, which improves the dynamic capability of an explainable dynamic security protection of medical big data and solves the problem of insufficient dynamic capability of an explainable dynamic security protection of medical big data in the existing technology;

[0021] 2. By classifying and encrypting the data of infectious disease patients, the personal privacy information of infectious disease patients can be protected, unauthorized access and data leakage can be prevented, thereby improving the security of infectious disease patient data;

[0022] 3. By real-time monitoring of the encrypted data of infectious disease patients, and by monitoring and analyzing the access and use of the encrypted data of infectious disease patients, the big data dynamic security infectious disease patient encrypted data protection strategy is continuously optimized, thereby improving the efficiency of dynamic security protection of medical big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of an explainable dynamic security protection method for medical big data provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of an explainable dynamic security protection function for medical big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The embodiments of the present application solve the problem of insufficient dynamic security capabilities of interpretable medical big data in the prior art by providing an interpretable dynamic security protection method for medical big data. The method collects and processes infectious disease patient data; classifies and encrypts the cleaned infectious disease patient data; and monitors the encrypted data in real time, thereby improving the dynamic security capabilities of interpretable medical big data.

[0026] The technical solution in the embodiment of the present application is to solve the above-mentioned problem of insufficient dynamic security protection capabilities of medical big data that can be explained. The overall idea is as follows:

[0027] By conducting a dynamic security protection performance evaluation and analysis on the encrypted data of infectious disease patients, the dynamic security of medical big data is adjusted according to the comprehensive evaluation results, thereby improving the dynamic capability of explainable dynamic security protection of medical big data.

[0028] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] like Figure 1As shown, it is a flow chart of an interpretable medical big data dynamic security protection method provided in an embodiment of the present application, the method is applied to an interpretable medical big data dynamic security protection device, and the method includes the following steps: collecting and processing infectious disease patient data; classifying and encrypting the cleaned infectious disease patient data to obtain infectious disease patient encrypted data; real-time monitoring of infectious disease patient encrypted data, and tracking the use and access of infectious disease patient encrypted data; performing a first performance evaluation and analysis of medical big data dynamic security protection on infectious disease patient encrypted data to obtain an infectious disease patient identity information data encryption security evaluation index; performing a second performance evaluation and analysis of medical big data dynamic security protection on infectious disease patient encrypted data to obtain an infectious disease patient mobile path key management security evaluation index; performing a third performance evaluation and analysis of medical big data dynamic security protection on infectious disease patient encrypted data to obtain an infectious disease patient electronic medical record data leakage risk assessment index; comprehensively analyzing the infectious disease patient identity information data encryption security evaluation index, the infectious disease patient mobile path key management security evaluation index and the infectious disease patient electronic medical record data leakage risk assessment index to obtain a medical big data dynamic security comprehensive adjustment evaluation index; and adjusting the medical big data dynamic security according to the results of the medical big data dynamic security comprehensive adjustment evaluation analysis.

[0030] In this embodiment, in a viral infectious disease hospital, the medical management-related departments need patient-related information, especially the patient's movement trajectory, because the patient's health changes in real time, and all collected patient identity information data needs to be encrypted during transmission and storage to prevent data leakage. The encrypted data of infectious patients undergoes dynamic security protection of medical big data, and the first performance evaluation and analysis is performed to evaluate the performance of dynamic security protection of medical big data when processing infectious disease patient data. The second performance evaluation and analysis is performed to evaluate the mobile path key management of infectious disease patients in the dynamic security protection of medical big data. The encrypted data of infectious disease patients undergoes dynamic security protection of medical big data, and the third performance evaluation and analysis is performed to evaluate the leakage risk of dynamic security protection of medical big data when processing and storing electronic medical record data.

[0031] Furthermore, the specific process of collecting and processing infectious disease patient data is: collecting infectious disease patient data from the medical big data database, and cleaning the collected infectious disease patient data.

[0032] In this embodiment, the infectious disease patient data includes infectious disease patient identity information data, infectious disease patient movement path data, and infectious disease patient electronic medical record data.

[0033] Furthermore, the specific process of obtaining the encrypted data of infectious disease patients is: hiding the identity information, movement paths and electronic medical records of infectious disease patients through data desensitization technology; obtaining the encrypted data of infectious disease patient identity information, the encrypted key data of infectious disease patient movement paths and the encrypted data of infectious disease patient electronic medical records through open source disk encryption software.

[0034] In this embodiment, during the transmission of sensitive data of infectious disease patients, the medical information system will automatically desensitize the sensitive information in the data, including hiding the identity information data, mobile path data and electronic medical record data of infectious disease patients. At the same time, the medical information system will generate a number for each piece of sensitive data of infectious disease patients so as to find the complete original sensitive data of infectious disease patients in the medical big data database. A pair of keys, a public key and a private key, are generated using the RSA encryption algorithm. The public key is used to encrypt data and the private key is used to decrypt data. The generated public key is used to encrypt the sensitive data of infectious disease patients. Each field of sensitive data of infectious disease patients is encrypted separately. The encrypted sensitive data of infectious disease patients is stored in a secure medical big data database. A new encrypted volume is created through open source disk encryption software. The infectious disease patient data is imported into the encrypted volume to ensure the correctness of the encryption of the infectious disease patient data.

[0035] Furthermore, the specific process of tracking the use and access of the encrypted data of infectious disease patients is: by setting up a monitoring server to monitor the encrypted data of infectious disease patients in real time, and obtaining the number of uses and accesses to the encrypted infectious disease patient data.

[0036] In this embodiment, by monitoring the medical information system through homomorphic encryption monitoring technology, certain characteristics of the data can be monitored and analyzed in the ciphertext state. For example, the encrypted infectious disease patient movement path key is summed or averaged to monitor the overall trend of the infectious disease patient movement path key in real time without decrypting the specific infectious disease patient movement path key. At the same time, by monitoring the calculation times and access frequency of the encrypted data, the usage and flow of the infectious disease patient movement path key data can be indirectly inferred.

[0037] Furthermore, the specific process of obtaining the encryption security assessment index of infectious disease patient identity information data is as follows: the infectious disease patient identity information encryption data includes the coverage rate of infectious disease patient data encryption transmission times, the coverage rate of infectious disease patient data encryption storage times and the frequency of use of infectious disease patient identity information keys; the number of infectious disease patient data encryption transmission times and the total number of infectious disease patient data transmissions are extracted from the medical big data database, and the coverage rate of infectious disease patient data encryption transmission times is the ratio of infectious disease patient data encryption transmission times to the total number of infectious disease patient data transmissions; the number of infectious disease patient data encryption storage times and the total number of infectious disease patient data storages are extracted from the medical big data database, and the coverage rate of infectious disease patient data encryption storage times is the ratio of infectious disease patient data encryption storage times to the total number of infectious disease patient data storages; the actual number of uses of infectious disease patient identity information keys and the preset number of uses of infectious disease patient identity information keys are extracted from the medical big data database, and the frequency of use of infectious disease patient identity information keys is the ratio of the actual number of uses of infectious disease patient identity information keys to the preset number of uses; and the encryption security assessment index of infectious disease patient identity information data is obtained through comprehensive analysis.

[0038] In this embodiment, the specific formula for the encryption security evaluation index of the infectious disease patient identity information data is obtained through this analysis is as follows:

[0039] ;

[0040] ;

[0041] The medical big data dynamic security data encryption detection segments are numbered in sequence. Indicates the number of the medical big data dynamic security data encryption detection point, , Indicates the total number of medical big data dynamic security data encryption detection segments;

[0042] Indicates The security assessment index of infectious disease patient identity information data encryption during the infectious disease patient identity information data encryption period;

[0043] Indicates The number of encrypted transmissions of infectious disease patient data during the encrypted period of infectious disease patient identity information data;

[0044] Indicates The total number of transmissions of infectious disease patient data during the infectious disease patient identity information data encryption period;

[0045] Indicates The number of encrypted storage times of infectious disease patient data during the encrypted time period of infectious disease patient identity information data;

[0046] Indicates The total number of times infectious disease patient data is stored during the infectious disease patient identity information data encryption period;

[0047] Indicates The actual number of times the infectious disease patient identity information key is used during the infectious disease patient identity information data encryption period;

[0048] Indicates The preset number of times the infectious disease patient identity information key is used during the infectious disease patient identity information data encryption period, and the threshold number of times the historical infectious disease patient identity information key is used obtained from the medical big data database;

[0049] The weight factor affecting the number of encrypted transmissions of the preset infectious disease patient data obtained from the medical big data database;

[0050] A weight factor affecting the number of encrypted storage times of preset infectious disease patient data obtained from a medical big data database;

[0051] The weight factor is the actual number of times the preset infectious disease patient identity information key obtained from the medical big data database is used.

[0052] The infectious disease patient identity information access control is an impact factor corresponding to the unit value of the infectious disease patient data encryption transmission times, the infectious disease patient data encryption storage times, and the actual number of uses of the infectious disease patient identity information key preset in the medical big data database, which represents the numerical value of the degree of influence of the actual number of uses of the infectious disease patient identity information key on the dynamic security data encryption evaluation index of the medical big data. When used, the impact factor corresponding to the actual number of uses of the infectious disease patient identity information key can be directly obtained from the medical big data database, and the corresponding relationship can be a pre-set mapping relationship. For example, the infectious disease patient identity information access control and the impact factor corresponding to the unit value of the infectious disease patient data encryption transmission times, the infectious disease patient data encryption storage times, and the actual number of uses of the infectious disease patient identity information key preset in the medical big data database form a mapping set, and the real-time actual number of uses of the infectious disease patient identity information key is input into the mapping set to obtain the impact factor corresponding to the unit value of the infectious disease patient data encryption transmission times, the infectious disease patient data encryption storage times, and the actual number of uses of the infectious disease patient identity information key, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0053] The higher the ratio of the number of encrypted transmissions of infectious disease patient data to the total number of transmissions of infectious disease patient data, the more positive the impact on the encryption security assessment index of infectious disease patient identity information data;

[0054] The higher the ratio of the number of encrypted storage times of infectious disease patient data to the total number of infectious disease patient data storage times, the more positive the impact on the encryption security assessment index of infectious disease patient identity information data;

[0055] The closer the ratio of the actual usage times of the medical big data dynamic security patient identity information key to the preset usage times is to 1, the positive impact it has on the encryption security assessment index of the infectious disease patient identity information data.

[0056] Furthermore, the specific process of obtaining the infectious disease patient mobile path key management security assessment index is as follows: the infectious disease patient mobile path encryption key data, including the infectious disease patient mobile path encryption key strength, the infectious disease patient mobile path key usage frequency and the infectious disease patient mobile path key rotation frequency; the infectious disease patient mobile path key bit number and the infectious disease patient mobile path key base bit number extracted from the medical big data database, the infectious disease patient mobile path encryption key strength is the ratio of the infectious disease patient mobile path key bit number to the key base bit number; the actual number of uses of the infectious disease patient mobile path key and the preset number of uses of the infectious disease patient mobile path key extracted from the medical big data database, the infectious disease patient mobile path key usage frequency is the ratio of the actual number of uses of the infectious disease patient mobile path key to the preset number of uses; the actual rotation frequency of the infectious disease patient mobile path key and the maximum rotation frequency of the infectious disease patient mobile path key extracted from the medical big data database, the infectious disease patient mobile path key rotation frequency is the ratio of the actual rotation frequency of the infectious disease patient mobile path key to the maximum rotation frequency; comprehensive analysis is performed to obtain the infectious disease patient mobile path key management security assessment index.

[0057] In this embodiment, the specific formula of the key management security evaluation index of the infectious disease patient mobile path is obtained by analysis as follows:

[0058] ;

[0059] ;

[0060] The infectious disease patient movement path key management time is divided into infectious disease patient movement path key management time periods of the same length. Indicates the current infectious disease patient movement path key management time period;

[0061] Indicates The security assessment index of the key management of the mobile path of infectious patients in the key management time period of the mobile path of infectious patients;

[0062] Indicates The number of infectious disease patient mobile path key bits in the infectious disease patient mobile path key management time period;

[0063] Indicates The base number of infectious disease patient mobile path keys in the infectious disease patient mobile path key management period, and the threshold number of historical infectious disease patient mobile path keys obtained from the medical big data database;

[0064] Indicates The actual number of times the infectious disease patient mobile path key is used in the infectious disease patient mobile path key management time period;

[0065] Indicates The preset number of times the infectious disease patient's mobile path key is used in the infectious disease patient's mobile path key management time period, and the threshold number of times the historical infectious disease patient identity information key is used obtained from the medical big data database;

[0066] Indicates The actual rotation frequency of the infectious disease patient movement path key in the infectious disease patient movement path key management time period;

[0067] Indicates The maximum rotation frequency of the infectious disease patient mobile path key in the infectious disease patient mobile path key management time period;

[0068] The weight factor affecting the number of key bits of the preset infectious disease patient movement path obtained from the medical big data database;

[0069] The weight factor is the actual number of times the preset infectious disease patient movement path key obtained from the medical big data database is used;

[0070] The actual rotation frequency of the preset infectious disease patient movement path key obtained from the medical big data database affects the weight factor.

[0071] The frequency of key generation for the mobile path of infectious patients is an impact factor corresponding to the number of key bits, actual number of uses, and unit values ​​of rotation frequency of the mobile path of infectious patients preset in the medical big data database, which represents the numerical value of the degree of influence of the number of key bits, actual number of uses, and rotation frequency of the mobile path of infectious patients on the dynamic security data encryption evaluation index of medical big data. When used, the impact factor corresponding to the number of key bits, actual number of uses, and rotation frequency of the mobile path of infectious patients can be directly obtained from the medical big data database, and the corresponding relationship can be a pre-set mapping relationship. For example, the frequency of key generation for the mobile path of infectious patients and the impact factor corresponding to the number of key bits, actual number of uses, and unit values ​​of rotation frequency of the mobile path of infectious patients preset in the medical big data database form a mapping set, and the real-time number of key bits, actual number of uses, and rotation frequency of the mobile path of infectious patients are input into the mapping set to obtain the impact factor corresponding to the number of key bits, actual number of uses, and unit values ​​of rotation frequency of the mobile path of infectious patients, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0072] Table 1 is an example table of security evaluation index of key management of mobile path of infectious patients. The example parameters in Table 1 only take the parameters of key management time period of mobile path of infectious patients for example. Set to 0.4, weight factor Set to 0.2, the weight factor Set to 0.4 Table 1 is shown below;

[0073] Table 1 Example of security evaluation index of key management for infectious disease patient movement paths:

[0074]

[0075] The more digits the infectious disease patient mobile path key has, the more times the infectious disease patient mobile path key is actually used, and the higher the actual rotation frequency of the infectious disease patient mobile path key; the higher the actual rotation frequency of the infectious disease patient mobile path key increases, the more digits the infectious disease patient mobile path key has, and the higher the infectious disease patient mobile path key management security assessment index increases.

[0076] The closer the ratio of the number of key bits of the infectious disease patient's mobile path to the benchmark number of key bits of the infectious disease patient's mobile path is to 1, the higher the security assessment index of the infectious disease patient's mobile path key management is, which has a positive impact on the security assessment index of the infectious disease patient's mobile path key management.

[0077] The closer the ratio of the actual number of uses of the infectious disease patient mobile path key to the preset number of uses of the infectious disease patient mobile path key is to 1, the higher the security assessment index of the infectious disease patient mobile path key management is, which has a positive impact on the security assessment index of the infectious disease patient mobile path key management.

[0078] The higher the actual rotation frequency of the infectious disease patient mobile path key, the higher the security assessment index of the infectious disease patient mobile path key management, which has a positive impact on the security assessment index of the infectious disease patient mobile path key management.

[0079] Furthermore, the specific process of obtaining the risk assessment index of electronic medical records data leakage of infectious disease patients is as follows: the electronic medical records of infectious disease patients are encrypted, including the update frequency of electronic medical records data of infectious disease patients, the access frequency of electronic medical records data of infectious disease patients and the coverage rate of the monitoring system of electronic medical records data of infectious disease patients; the actual update times of electronic medical records data of infectious disease patients and the maximum update times of electronic medical records data of infectious disease patients are extracted from the medical big data database, and the update frequency of electronic medical records data of infectious disease patients is the ratio of the actual update times of electronic medical records data of infectious disease patients to the maximum update times; the access times of electronic medical records data of infectious disease patients and the maximum access times of electronic medical records data of infectious disease patients are extracted from the medical big data database, and the access frequency of electronic medical records data of infectious disease patients is the ratio of the access times of electronic medical records data of infectious disease patients to the maximum access times; the number of vulnerabilities covered by the monitoring system of electronic medical records data of infectious disease patients and the total number of vulnerabilities of the monitoring system of electronic medical records data of infectious disease patients are extracted from the medical big data database, and the coverage rate of the monitoring system of electronic medical records data of infectious disease patients is the ratio of the number of vulnerabilities covered by the monitoring system of electronic medical records data of infectious disease patients to the total number of vulnerabilities; comprehensive analysis is performed to obtain the risk assessment index of electronic medical records data leakage of infectious disease patients.

[0080] In this embodiment, the specific formula of the risk assessment index of electronic medical record data leakage of infectious disease patients is obtained through analysis as follows:

[0081] ;

[0082] ;

[0083] Divide the risk time of electronic medical record data leakage of infectious disease patients into risk time periods of the same length. Indicates the current risk period for electronic medical records of infectious disease patients to be leaked;

[0084] Indicates The risk assessment index of electronic medical records of infectious disease patients for each period of time;

[0085] Indicates The actual number of updates to the electronic medical records of infectious disease patients during the risk period of electronic medical records data leakage of infectious disease patients;

[0086] Indicates The maximum number of updates to the electronic medical records of infectious disease patients during the risk period of electronic medical records data leakage for each infectious disease patient, and the threshold number of updates to the electronic medical records of historical infectious disease patients obtained from the medical big data database;

[0087] Indicates The number of times the electronic medical records of infectious disease patients were accessed during the risk period of electronic medical records of infectious disease patients being leaked;

[0088] Indicates The maximum number of accesses to the electronic medical records of infectious disease patients during the risk period of electronic medical records data leakage of infectious disease patients, and the threshold number of accesses to the electronic medical records of historical infectious disease patients obtained from the medical big data database;

[0089] Indicates The number of vulnerabilities covered by the electronic medical record data monitoring system for infectious disease patients during the risk period of electronic medical record data leakage for infectious disease patients;

[0090] Indicates The total number of vulnerabilities in the electronic medical record data monitoring system for infectious disease patients during the risk period of electronic medical record data leakage for infectious disease patients;

[0091] The weight factor affecting the actual update times of the electronic medical record data of patients with preset infectious diseases obtained from the medical big data database;

[0092] The weight factor affecting the number of accesses to the electronic medical record data of patients with preset infectious diseases obtained from the medical big data database;

[0093] The weight factor affecting the number of vulnerabilities covered by the monitoring system for the electronic medical record data of patients with preset infectious diseases obtained from the medical big data database.

[0094] The download volume of electronic medical record data of infectious disease patients is the impact factor corresponding to the actual number of updates, accesses and the number of vulnerabilities covered by the monitoring system of the electronic medical record data of infectious disease patients preset in the medical big data database, which represents the numerical value of the influence of the actual number of updates, accesses and the number of vulnerabilities covered by the monitoring system of the electronic medical record data of infectious disease patients on the dynamic security data encryption assessment index of medical big data. When used, the impact factor corresponding to the actual number of updates, accesses and the number of vulnerabilities covered by the monitoring system of the electronic medical record data of infectious disease patients can be directly obtained from the medical big data database, and the corresponding relationship can be a pre-set mapping relationship. For example, the download volume of electronic medical record data of infectious disease patients and the impact factor corresponding to the actual number of updates, accesses and the number of vulnerabilities covered by the monitoring system of the electronic medical record data of infectious disease patients preset in the medical big data database form a mapping set, and the real-time actual number of updates, accesses and the number of vulnerabilities covered by the monitoring system of the electronic medical record data of infectious disease patients are input into the mapping set to obtain the impact factor corresponding to the actual number of updates, accesses and the number of vulnerabilities covered by the monitoring system of the electronic medical record data of infectious disease patients, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0095] The more times the electronic medical record data of infectious disease patients are actually updated, the more times the electronic medical record data of infectious disease patients are accessed, the fewer vulnerabilities are covered by the electronic medical record data monitoring system of infectious disease patients, and the higher the risk assessment index of electronic medical record data leakage of infectious disease patients.

[0096] Furthermore, the specific process of obtaining the dynamic security comprehensive adjustment evaluation index of medical big data is as follows: through the encryption security evaluation index of infectious disease patient identity information data, the key management security evaluation index of infectious disease patient mobile path and the risk evaluation index of infectious disease patient electronic medical record data leakage, the dynamic security comprehensive adjustment evaluation index of medical big data is obtained; ; ; It represents the comprehensive adjustment evaluation index of dynamic security of medical big data. represents the encryption security assessment index of infectious disease patient identity information data, represents the security evaluation index of key management of infectious disease patient mobile paths, represents the risk assessment index of electronic medical record data leakage of infectious disease patients, The weight factor affecting the encryption security assessment index of the preset infectious disease patient identity information data obtained from the medical big data database, is the weight factor affecting the key management security assessment index of the preset infectious disease patient mobile path obtained from the medical big data database. It is the influencing weight factor of the risk assessment index of electronic medical record data leakage of infectious disease patients obtained from the medical big data database.

[0097] In this embodiment, the distribution of dynamic security keys for medical big data is the impact factor corresponding to the unit value of the identity information data encryption security assessment index, mobile path key management security assessment index and electronic medical record data leakage risk assessment index of infectious disease patients preset in the medical big data database, which represents the numerical value of the influence of the identity information data encryption security assessment index, mobile path key management security assessment index and electronic medical record data leakage risk assessment index of infectious disease patients on the dynamic security data encryption assessment index of medical big data. When used, the impact factors corresponding to the identity information data encryption security assessment index, mobile path key management security assessment index and electronic medical record data leakage risk assessment index of infectious disease patients can be directly obtained from the medical big data database, and the corresponding key The system can be a pre-set mapping relationship. For example, the distribution of dynamic security keys of medical big data and the impact factors corresponding to the unit values ​​of the identity information data encryption security assessment index, mobile path key management security assessment index and electronic medical record data leakage risk assessment index of infectious disease patients preset in the medical big data database form a mapping set. The real-time identity information data encryption security assessment index, mobile path key management security assessment index and electronic medical record data leakage risk assessment index of infectious disease patients are input into the mapping set to obtain the impact factors corresponding to the unit values ​​of the identity information data encryption security assessment index, mobile path key management security assessment index and electronic medical record data leakage risk assessment index of infectious disease patients. The mapping relationship can be one-to-one or many-to-one.

[0098] like Figure 2 As shown, it is a schematic diagram of the dynamic security comprehensive adjustment evaluation index function of medical big data provided in the embodiment of the present application; x is the positive semi-axis of the horizontal axis, y is the positive semi-axis of the vertical axis, and the weight factor Set to 0.5, weight factor Set to 0.3, weight factor Set to 0.2.

[0099] Curve a indicates that if the encryption security assessment index of infectious disease patient identity information data is fixed at 1, the key management security assessment index of infectious disease patient mobile path is fixed at 1.6, the electronic medical record data leakage risk assessment index of infectious disease patients is x, and the dynamic security comprehensive adjustment assessment index of medical big data is y, the dynamic security comprehensive adjustment assessment index of medical big data decreases as the risk assessment index of infectious disease patient electronic medical record data leakage increases.

[0100] Curve b indicates that if the encryption security assessment index of infectious disease patient identity information data and the risk assessment index of infectious disease patient electronic medical record data leakage are set as a fixed value of 1, the security assessment index of infectious disease patient mobile path key management is x, and the dynamic security comprehensive adjustment assessment index of medical big data is y, the dynamic security comprehensive adjustment assessment index of medical big data increases with the increase of the security assessment index of infectious disease patient mobile path key management.

[0101] Curve c indicates that if the security assessment index of infectious disease patient mobile path key management is fixed at 1.6, the risk assessment index of infectious disease patient electronic medical record data leakage is fixed at 1, the encryption security assessment index of infectious disease patient identity information data is x, and the dynamic security comprehensive adjustment assessment index of medical big data is y, the dynamic security comprehensive adjustment assessment index of medical big data increases with the increase of the encryption security assessment index of infectious disease patient identity information data.

[0102] Furthermore, the specific process of adjusting the dynamic security of medical big data includes: importing the first risk judgment value, the second risk judgment value and the third risk judgment value of the dynamic security comprehensive adjustment of medical big data in the database into the dynamic security risk detection model of medical big data, and outputting the adjustment method and risk level through the dynamic security risk detection model of medical big data; the specific process of the adjustment method output by the dynamic security risk detection model of medical big data is: if the encryption security assessment index of the identity information data of infectious disease patients is less than the first risk judgment value of the dynamic security comprehensive adjustment of medical big data, then the frequency of replacing the key of the identity information data of infectious disease patients is increased by real-time monitoring of the actual number of uses of the identity information key of infectious disease patients; if the security assessment index of the key management of the mobile path of infectious disease patients is less than the second risk judgment value of the dynamic security comprehensive adjustment of medical big data, then the mobile path key of infectious disease patients is distributed through an encrypted VPN to implement multi-factor identity authentication; if the risk assessment index of the electronic medical record data leakage of infectious disease patients is greater than the third risk judgment value of the dynamic security comprehensive adjustment of medical big data, then the frequency of monitoring the electronic medical record data of infectious disease patients is increased by increasing the coverage of the dynamic security monitoring system of medical big data.

[0103] Furthermore, the specific process of outputting the risk level through the medical big data dynamic security risk detection model is: importing the medical big data dynamic security comprehensive adjustment evaluation index and the medical big data dynamic security comprehensive adjustment risk detection definition evaluation value in the database into the medical big data dynamic security risk detection model, and comprehensively analyzing to obtain the medical big data dynamic security comprehensive adjustment risk deviation value, which is the medical big data dynamic security comprehensive adjustment risk detection evaluation value, and the medical big data dynamic security risk detection model outputs the risk level according to the medical big data dynamic security comprehensive adjustment risk detection evaluation value; the process of the medical big data dynamic security risk detection model judging the risk level is: if the medical big data dynamic security comprehensive adjustment risk detection evaluation value is less than zero, then the medical big data dynamic security comprehensive adjustment risk level corresponding to the medical big data dynamic security comprehensive adjustment risk detection evaluation value is high risk; if the medical big data dynamic security comprehensive adjustment risk detection evaluation value is equal to zero, then the medical big data dynamic security comprehensive adjustment risk level corresponding to the medical big data dynamic security comprehensive adjustment risk detection evaluation value is medium risk; if the medical big data dynamic security comprehensive adjustment risk detection evaluation value is greater than zero, then the medical big data dynamic security comprehensive adjustment risk level corresponding to the medical big data dynamic security comprehensive adjustment risk detection evaluation value is low risk.

[0104] In this embodiment, the medical big data dynamic security risk detection model has two functions, namely, inputting the medical big data dynamic security comprehensive adjustment evaluation index, the medical big data dynamic security comprehensive adjustment risk detection definition evaluation value in the database, the medical big data dynamic security comprehensive adjustment first risk judgment value, the medical big data dynamic security comprehensive adjustment second risk judgment value and the medical big data dynamic security comprehensive adjustment third risk judgment value, and outputting two functions of adjustment method and risk level. The specific model implements the above two functions by: importing the medical big data dynamic security comprehensive adjustment first risk judgment value, the medical big data dynamic security comprehensive adjustment second risk judgment value and the medical big data dynamic security comprehensive adjustment third risk judgment value in the database into the medical big data dynamic security risk detection model In the output adjustment method of the dynamic security risk detection model of medical big data, the output risk level of the dynamic security risk detection model of medical big data is output as follows: the dynamic security comprehensive adjustment evaluation index of medical big data and the dynamic security comprehensive adjustment risk detection definition evaluation value of medical big data in the database are imported into the dynamic security risk detection model of medical big data, and the dynamic security comprehensive adjustment risk deviation value of medical big data is obtained by comprehensive analysis, which is the dynamic security comprehensive adjustment risk detection evaluation value of medical big data, and the output risk level of the dynamic security risk detection model of medical big data is output; the dynamic security comprehensive adjustment risk deviation value of medical big data is the difference between the dynamic security comprehensive adjustment evaluation index of medical big data and the dynamic security comprehensive adjustment risk detection definition evaluation value of medical big data in the database.

[0105] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0109] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0110] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An explainable dynamic security protection method for medical big data, characterized in that: The following steps are involved: Collect and process data on patients with infectious diseases; Classifying and encrypting the cleaned infectious disease patient data to obtain encrypted infectious disease patient data; Monitor the encrypted data of infectious disease patients in real time and track the use and access of the encrypted data of infectious disease patients; Conduct the first performance evaluation analysis of the dynamic security protection of medical big data on the encrypted data of infectious disease patients, and obtain the encryption security evaluation index of the identity information data of infectious disease patients; The second performance evaluation analysis of the dynamic security protection of medical big data is conducted on the encrypted data of infectious disease patients, and the security evaluation index of the key management of the mobile path of infectious disease patients is obtained; Conduct a third-party performance evaluation and analysis of the dynamic security protection of medical big data on the encrypted data of infectious disease patients, and obtain the risk assessment index of electronic medical record data leakage of infectious disease patients; The dynamic security comprehensive adjustment assessment index of medical big data is obtained through comprehensive analysis of the encryption security assessment index of infectious disease patient identity information data, the key management security assessment index of infectious disease patient mobile path, and the risk assessment index of infectious disease patient electronic medical record data leakage. Adjust the dynamic security of medical big data based on the comprehensive adjustment assessment and analysis results of the dynamic security of medical big data; The specific process of obtaining the dynamic security comprehensive adjustment evaluation index of medical big data is as follows: The dynamic security comprehensive adjustment assessment index of medical big data is obtained through the encryption security assessment index of infectious disease patient identity information data, the key management security assessment index of infectious disease patient mobile path, and the risk assessment index of infectious disease patient electronic medical record data leakage. ； ； It represents the comprehensive adjustment evaluation index of dynamic security of medical big data. represents the encryption security assessment index of infectious disease patient identity information data, represents the security evaluation index of key management of infectious disease patient mobile paths, represents the risk assessment index of electronic medical record data leakage of infectious disease patients, The weight factor affecting the encryption security assessment index of the preset infectious disease patient identity information data obtained from the medical big data database, is the weight factor affecting the key management security assessment index of the preset infectious disease patient movement path obtained from the medical big data database. It is the influencing weight factor of the risk assessment index of electronic medical record data leakage of infectious disease patients obtained from the medical big data database.

2. The interpretable medical big data dynamic security protection method as claimed in claim 1, characterized in that: The specific process of collecting and processing infectious disease patient data is as follows: Collect infectious disease patient data from the medical big data database and clean the collected infectious disease patient data.

3. The interpretable medical big data dynamic security protection method as claimed in claim 1, characterized in that: The specific process of obtaining the encrypted data of infectious disease patients is as follows: Hide the identity information, movement paths and electronic medical records of infectious disease patients through data desensitization technology; The encrypted data of infectious disease patients’ identity information, the encrypted key data of infectious disease patients’ movement paths, and the encrypted data of infectious disease patients’ electronic medical records are obtained through open source disk encryption software.

4. The interpretable medical big data dynamic security protection method as claimed in claim 1, characterized in that: The specific process of tracking the use and access of encrypted data of infectious disease patients is as follows: By setting up a monitoring server to monitor the encrypted data of infectious disease patients in real time, the number of times the encrypted infectious disease patient data is used and the number of times it is accessed are obtained.

5. The explainable dynamic security protection method for medical big data as claimed in claim 3, characterized in that: The specific process of obtaining the encryption security evaluation index of the infectious disease patient identity information data is as follows: The encrypted data of the infectious disease patient identity information includes the coverage rate of the number of encrypted transmissions of infectious disease patient data, the coverage rate of the number of encrypted storages of infectious disease patient data, and the frequency of use of the infectious disease patient identity information key; Extracting the number of encrypted transmissions of infectious disease patient data and the total number of transmissions of infectious disease patient data from the medical big data database, wherein the coverage rate of the number of encrypted transmissions of infectious disease patient data is the ratio of the number of encrypted transmissions of infectious disease patient data to the total number of transmissions of infectious disease patient data; Extracting the number of encrypted storage times of infectious disease patient data and the total number of storage times of infectious disease patient data from the medical big data database, wherein the coverage rate of the number of encrypted storage times of infectious disease patient data is the ratio of the number of encrypted storage times of infectious disease patient data to the total number of storage times of infectious disease patient data; Extracting the actual number of times the infectious disease patient identity information key is used and the preset number of times the infectious disease patient identity information key is used from the medical big data database, wherein the frequency of use of the infectious disease patient identity information key is the ratio of the actual number of times the infectious disease patient identity information key is used to the preset number of times; Comprehensive analysis resulted in the encryption security assessment index for infectious disease patient identity information data.

6. The explainable dynamic security protection method for medical big data as claimed in claim 3, characterized in that: The specific process of obtaining the key management security evaluation index of the infectious disease patient's mobile path is as follows: The infectious disease patient movement path encryption key data includes the infectious disease patient movement path encryption key strength, infectious disease patient movement path key usage frequency and infectious disease patient movement path key rotation frequency; The number of key bits of the infectious disease patient's mobile path and the base number of key bits of the infectious disease patient's mobile path extracted from the medical big data database, wherein the encryption key strength of the infectious disease patient's mobile path is the ratio of the number of key bits of the infectious disease patient's mobile path to the base number of key bits; The actual number of times the infectious disease patient movement path key is used and the preset number of times the infectious disease patient movement path key is used are extracted from the medical big data database, wherein the infectious disease patient movement path key use frequency is the ratio of the actual number of times the infectious disease patient movement path key is used to the preset number of times; The actual rotation frequency of the infectious disease patient mobile path key and the maximum rotation frequency of the infectious disease patient mobile path key extracted from the medical big data database, wherein the infectious disease patient mobile path key rotation frequency is the ratio of the actual rotation frequency of the infectious disease patient mobile path key to the maximum rotation frequency; Comprehensive analysis is used to obtain the security assessment index of key management of mobile paths of infectious disease patients.

7. The explainable dynamic security protection method for medical big data as claimed in claim 3, characterized in that: The specific process of obtaining the risk assessment index of electronic medical record data leakage of infectious disease patients is as follows: The encrypted data of electronic medical records of patients with infectious diseases, including the update frequency of electronic medical records of patients with infectious diseases, the access frequency of electronic medical records of patients with infectious diseases, and the coverage rate of the monitoring system of electronic medical records of patients with infectious diseases; Extracting the actual update times of the electronic medical records of patients with infectious diseases and the maximum update times of the electronic medical records of patients with infectious diseases from the medical big data database, wherein the update frequency of the electronic medical records of patients with infectious diseases is the ratio of the actual update times of the electronic medical records of patients with infectious diseases to the maximum update times; Extracting the number of accesses to the electronic medical records of patients with infectious diseases and the maximum number of accesses to the electronic medical records of patients with infectious diseases from the medical big data database, wherein the access frequency of the electronic medical records of patients with infectious diseases is the ratio of the number of accesses to the electronic medical records of patients with infectious diseases to the maximum number of accesses; Extracting the number of vulnerabilities covered by the electronic medical record data monitoring system for infectious disease patients and the total number of vulnerabilities of the electronic medical record data monitoring system for infectious disease patients from the medical big data database, wherein the coverage rate of the electronic medical record data monitoring system for infectious disease patients is the ratio of the number of vulnerabilities covered by the electronic medical record data monitoring system for infectious disease patients to the total number of vulnerabilities; A comprehensive analysis was conducted to obtain the risk assessment index for electronic medical record data leakage of infectious disease patients.

8. The interpretable medical big data dynamic security protection method as claimed in claim 1, characterized in that: The specific process of adjusting the dynamic security of medical big data includes: Importing the first risk determination value, the second risk determination value and the third risk determination value of the dynamic safety comprehensive adjustment of the medical big data in the database into the dynamic safety risk detection model of the medical big data, and outputting the adjustment method and risk level through the dynamic safety risk detection model of the medical big data; The specific process of the output adjustment method of the medical big data dynamic security risk detection model is as follows: If the encryption security assessment index of the infectious disease patient identity information data is less than the first risk judgment value of the dynamic security comprehensive adjustment of the medical big data, the frequency of replacing the infectious disease patient identity information data key is increased by real-time monitoring of the actual number of times the infectious disease patient identity information key is used; If the infectious disease patient mobile path key management security assessment index is less than the second risk determination value of the medical big data dynamic security comprehensive adjustment, the infectious disease patient mobile path key is distributed through an encrypted VPN to implement multi-factor identity authentication; If the risk assessment index for electronic medical record data leakage of infectious disease patients is greater than the third risk judgment value of the comprehensive adjustment of dynamic security of medical big data, the frequency of monitoring electronic medical record data of infectious disease patients shall be increased by increasing the coverage of the dynamic security monitoring system of medical big data.

9. The interpretable medical big data dynamic security protection method as claimed in claim 8, characterized in that: The specific process of outputting risk levels through the dynamic security risk detection model of medical big data is as follows: The medical big data dynamic security comprehensive adjustment evaluation index and the medical big data dynamic security comprehensive adjustment risk detection definition evaluation value in the database are introduced into the medical big data dynamic security risk detection model, and the medical big data dynamic security comprehensive adjustment risk deviation value is obtained through comprehensive analysis, which is the medical big data dynamic security comprehensive adjustment risk detection evaluation value. The medical big data dynamic security risk detection model outputs the risk level according to the medical big data dynamic security comprehensive adjustment risk detection evaluation value. The process of judging the risk level of the medical big data dynamic security risk detection model is as follows: If the medical big data dynamic security comprehensive adjustment risk detection assessment value is less than zero, the medical big data dynamic security comprehensive adjustment risk level corresponding to the medical big data dynamic security comprehensive adjustment risk detection assessment value is high risk; If the dynamic security comprehensive adjustment risk detection and assessment value of medical big data is equal to zero, the dynamic security comprehensive adjustment risk level of medical big data corresponding to the dynamic security comprehensive adjustment risk detection and assessment value of medical big data is medium risk; If the dynamic security comprehensive adjustment risk detection assessment value of medical big data is greater than zero, the dynamic security comprehensive adjustment risk level of medical big data corresponding to the dynamic security comprehensive adjustment risk detection assessment value of medical big data is low risk.

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