Cloud data security management method and device

Abstract

The invention belongs to the technical field of data security, and discloses a cloud data security management method and device, and the method comprises the steps: carrying out the environment risk assessment of the current cloud health file access, and then adaptively adjusting the security parameter of a dynamic verification code through an environment risk assessment value. And according to the adjusted security parameter, generating a dynamic verification code for granting the cloud health record access permission to the user of the session, thereby effectively ensuring the security of the cloud personal health record data, simplifying the user operation process, reducing the hardware equipment cost, and improving the public service efficiency.

Description

Technical Field

[0001] The present application relates to the field of data security technology, and more specifically, to a cloud data security management method and device. Background Art

[0002] In public service places such as libraries, community centers, and citizen service stations, residents often need to use public computers to quickly handle various matters, including accessing personal cloud data, such as checking electronic health records or applying for online medical services. These health record data involve highly sensitive personal privacy, and data security is of vital importance. The traditional account password authentication method faces severe challenges in the open environment of public places. Passwords are easily peeped, maliciously recorded, and even users themselves are prone to forgetting passwords, which poses a great security risk and potential leakage. Although multi-factor authentication technology can improve security to a certain extent, its operation process is relatively complicated. For the elderly or temporary visitors who are not familiar with technology, the learning and use costs are high, and the user experience is not friendly. At the same time, frequently entering account passwords or performing complex authentication operations is also contrary to the demand for fast and convenient services in public service places, affecting service efficiency. In addition, adding additional hardware equipment for identity authentication in public service places, such as fingerprint readers or IC card readers, will undoubtedly greatly increase the additional construction and maintenance costs, which is less economical, and may lead to new equipment management and maintenance issues, increasing the management burden.

[0003] Therefore, how to make full use of existing public computer equipment and provide an efficient access control method for specific application scenarios in public service places that can significantly simplify user operation processes and effectively protect the security of personal health record data in the cloud has become a key technical problem that needs to be urgently solved in the current public service field. It is directly related to the efficiency of public services and the level of user data security. A more complete and efficient technical solution is urgently needed to fill the shortcomings of existing technologies.

[0004] In view of the above problems, the existing technology needs to be improved urgently. Summary of the invention

[0005] The purpose of this application is to provide a cloud data security management method and device, which can effectively ensure the security of cloud personal health record data, simplify user operation processes, reduce hardware equipment costs, and improve public service efficiency.

[0006] In a first aspect, the present application provides a cloud data security management method for securely managing the process of accessing cloud health records through a public computer, the method comprising the following steps:

[0007] A1. In response to a cloud-based health record access request initiated by a user through a public computer, collect risk-related information for calculating an environmental risk assessment value; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information;

[0008] A2. According to the environmental risk assessment value, adaptively adjust the security parameters of the dynamic verification code; the greater the environmental risk assessment value, the stricter the security parameters; the security parameters include verification code length, verification code character set, verification code validity duration, and verification code error retry times;

[0009] A3. Generate a dynamic verification code according to the adjusted verification code length and verification code character set, and send the generated dynamic verification code to the mobile phone pre-bound by the user;

[0010] A4. Under the constraints of the adjusted verification code validity duration and verification code error retry times, when receiving the dynamic verification code input by the user on the public computer, verify the correctness of the input dynamic verification code according to the generated dynamic verification code;

[0011] A5. After successful verification, grant the user the access permission to the cloud-based health record for this session.

[0012] This method adaptively adjusts the security parameters of the dynamic verification code according to the environmental risk assessment value, and generates a dynamic verification code for granting the user the access permission to the cloud-based health record for this session according to the adjusted security parameters, thereby effectively protecting the security of cloud-based personal health record data, simplifying the user operation process, reducing the hardware device cost, and improving the public service efficiency.

[0013] Preferably, step A1 includes:

[0014] A101. Obtain the device information of the public computer for calculating and generating a device fingerprint, and determine the device risk value according to the device fingerprint; the device information includes multiple of operating system type, version number, browser type, installed plugin list, screen resolution, CPU model, and hard disk serial number;

[0015] A102. Obtain the geographical location of the public computer for determining the location risk value;

[0016] A103. Obtain the network type of the public computer for determining the network risk value;

[0017] A104. Obtain the access behavior information of the user for determining the behavior risk value; the access behavior information includes the time of accessing the cloud-based health record, access frequency, and data download volume;

[0018] A105. Calculate the environmental risk assessment value according to the device risk value, the location risk value, the network risk value, and the behavior risk value.

[0019] Thereby, the system can comprehensively consider the device environment, geographical location, network environment where the public computer is located, and the access behavior of the user, realize a comprehensive assessment of the environmental risk, provide an accurate risk basis for adaptively adjusting the security parameters of the dynamic verification code subsequently, and further enhance the security of the process of accessing the cloud health record by the public computer.

[0020] Preferably, step A101 includes:

[0021] Obtain the device information of the public computer in real time, and calculate and generate the current device fingerprint.

[0022] Perform a hashing operation on the current device fingerprint using the SM3 cryptographic hashing algorithm to obtain the current device fingerprint hash value.

[0023] Compare the current device fingerprint hash with the reference device fingerprint hash to determine the device risk value; the reference device fingerprint hash is the device fingerprint hash value generated according to the original and unmodified device information of the public computer.

[0024] Through the above steps, it is possible to effectively identify whether the device information of the public computer has been tampered with, thereby more accurately assessing the device risk and enhancing the effectiveness of subsequent security management.

[0025] Preferably, step A104 includes:

[0026] Obtain the current time when the user accesses the cloud health record, and calculate the access time risk degree according to the deviation between the current time and the reference access time of the user; the reference access time is the access time reference quantity statistically obtained according to the historical access records of the user.

[0027] Statistically obtain the access frequency of the user within the first preset time period, and calculate the access frequency risk degree according to the deviation between the access frequency within the first preset time period and the reference access frequency of the user; the reference access frequency is the average access frequency statistically obtained according to the historical access records of the user.

[0028] Calculate the single - access download volume according to the data download volume and the number of accesses of the user within the second preset time period, and calculate the download volume risk degree according to the deviation between the single - access download volume and the reference single - access download volume of the user; the reference single - access download volume is the average single - access download volume statistically obtained according to the historical access records of the user.

[0029] Calculate the behavior risk value according to the access time risk degree, the access frequency risk degree, and the download volume risk degree.

[0030] Preferably, step A2 includes:

[0031] A201. Calculate the safety factor of the current safety parameter, denoted as the current safety factor; the current safety parameter is a preset safety parameter or a set of safety parameters randomly selected from the historical safety parameter set;

[0032] A202. According to the environmental risk assessment value, query the preset safety parameter adjustment strategy table to determine the target safety factor corresponding to the environmental risk assessment value;

[0033] A203. If the target safety factor is higher than the current safety factor, then adjust each parameter in the current safety parameter in turn according to the first preset adjustment priority until the safety factor corresponding to the adjusted safety parameter reaches the target safety factor;

[0034] A204. If the target safety factor is less than the current safety factor, then adjust each parameter in the current safety parameter in turn according to the second preset adjustment priority opposite to the first preset adjustment priority until the safety factor corresponding to the adjusted safety parameter reaches the target safety factor.

[0035] Preferably, step A2 further includes the steps:

[0036] A204. Obtain the average speed at which the user inputs the dynamic verification code;

[0037] A205. Generate a duration correction factor according to the average speed to correct the valid duration of the adjusted verification code.

[0038] Preferably, step A205 includes:

[0039] According to the adjusted verification code character set, determine the verification code type; the verification code type includes pure digital verification code, pure letter verification code, alphanumeric mixed verification code, special symbol - number mixed verification code, special symbol - letter mixed verification code, and special symbol - alphanumeric mixed verification code;

[0040] Determine the type adjustment factor according to the verification code type; the more complex the verification code type, the larger the type adjustment factor;

[0041] Calculate the duration correction factor according to the average speed and the type adjustment factor; wherein, the duration correction factor is inversely proportional to the average speed and directly proportional to the type adjustment factor.

[0042] Preferably, step A3 includes:

[0043] A301. Generate a dynamic verification code using a random algorithm according to the adjusted verification code length and verification code character set;

[0044] A302. Encrypt the generated dynamic verification code to obtain the encrypted dynamic verification code ciphertext;

[0045] A303. Package the dynamic verification code ciphertext and the mobile phone number pre-bound by the user into a short message sending request, and send the short message sending request to the short message center through the short message gateway, so that the short message center decrypts the dynamic verification code ciphertext to obtain the original dynamic verification code, and sends the short message containing the dynamic verification code to the mobile phone pre-bound by the user.

[0046] Preferably, step A302 includes:

[0047] Select the encryption algorithm type according to the adjusted verification code character set; when the verification code character set only contains numbers, select the XOR encryption algorithm, when the verification code character set contains letters but does not contain special characters, select the AES encryption algorithm, and when the verification code character set contains special characters, select the SM4 encryption algorithm;

[0048] Generate the corresponding encryption key according to the selected encryption algorithm type; if the XOR encryption algorithm is selected, generate a random number of the same length as the verification code as the encryption key, and if the AES or SM4 encryption algorithm is selected, generate a fixed-length encryption key that meets the algorithm requirements;

[0049] Use the selected encryption algorithm and the encryption key to encrypt the dynamic verification code to obtain the encrypted dynamic verification code ciphertext.

[0050] In a second aspect, the present application provides a cloud data security management device for securely managing the process of accessing cloud health records through a public computer. The device includes:

[0051] A risk assessment module, configured to collect risk-related information in response to a cloud health record access request initiated by a user through a public computer, for calculating an environmental risk assessment value; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information;

[0052] A parameter adjustment module, configured to adaptively adjust the security parameters of the dynamic verification code according to the environmental risk assessment value; the greater the environmental risk assessment value, the stricter the security parameters; the security parameters include verification code length, verification code character set, verification code validity period, and verification code error retry times;

[0053] A verification code generation and sending module, configured to generate a dynamic verification code according to the adjusted verification code length and verification code character set, and send the generated dynamic verification code to the mobile phone pre-bound by the user;

[0054] The verification code verification module is used to verify the correctness of the input dynamic verification code according to the generated dynamic verification code when receiving the dynamic verification code input by the user on the public computer under the constraints of the adjusted effective duration of the verification code and the number of retry attempts for verification code errors.

[0055] The permission control module is used to grant the user the access permission to the cloud health record for this session after the verification is passed.

[0056] Beneficial effects: A cloud data security management method and device provided by the present application adaptively adjust the security parameters of the dynamic verification code through the environmental risk assessment value, and generate a dynamic verification code for granting the user the access permission to the cloud health record for this session according to the adjusted security parameters, so as to effectively ensure the security of cloud personal health record data, simplify the user operation process, reduce the hardware device cost, and improve the public service efficiency. Description of the Drawings

[0057] Figure 1 It is a flowchart of the cloud data security management method provided by the embodiment of the present application.

[0058] Figure 2 It is a schematic structural diagram of the cloud data security management device provided by the embodiment of the present application.

[0059] Label description: 1. Risk assessment module; 2. Parameter adjustment module; 3. Verification code generation and sending module; 4. Verification code verification module; 5. Permission control module. Detailed Embodiments

[0060] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0061] It should be noted that: Similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0062] ReferenceFigure 1 , this application proposes a cloud data security management method for securely managing the process of accessing cloud health records through public computers. The steps of this method include:

[0063] A1. In response to a cloud health record access request initiated by a user through a public computer, collect risk-associated information for calculating an environmental risk assessment value; the risk-associated information includes network type, geographical location, device fingerprint, and access behavior information;

[0064] A2. According to the environmental risk assessment value, adaptively adjust the security parameters of the dynamic verification code; the greater the environmental risk assessment value, the stricter the security parameters; the security parameters include verification code length, verification code character set, verification code validity duration, and verification code error retry times;

[0065] A3. Generate a dynamic verification code according to the adjusted verification code length and verification code character set, and send the generated dynamic verification code to the mobile phone pre-bound by the user;

[0066] A4. Under the constraints of the adjusted verification code validity duration and verification code error retry times, when receiving the dynamic verification code input by the user on the public computer, verify the correctness of the input dynamic verification code according to the generated dynamic verification code;

[0067] A5. When the verification passes, grant the user the access permission to the cloud health record for this session.

[0068] Among them, in step A1, the risk-associated information is collected, and this information can include the network type to which the public computer is connected, such as whether it is public WiFi or an internal network of an institution; the geographical location of the public computer, which can be obtained through GPS or IP address location technology; the device fingerprint, which is generated by extracting the hardware and software configuration information of the public computer and is used to identify the uniqueness of the device; and the user's access behavior information, such as access time, frequency, and data download volume. The calculation of the environmental risk assessment value can be obtained by performing a weighted calculation on the collected risk-associated information through a preset risk assessment model.

[0069] Among them, in step A2, the adaptive adjustment of the security parameters is determined according to the magnitude of the environmental risk assessment value. The greater the environmental risk assessment value, the higher the security risk of the current access environment. At this time, it is necessary to adjust the security parameters of the dynamic verification code to be more strict, and vice versa, it can be appropriately relaxed. The adjustment of the security parameters can be carried out by methods such as table lookup method or function calculation method. For example, a mapping relationship table between the environmental risk assessment value and the security parameters is preset in advance, or a function for the security parameters to change with the environmental risk assessment value is designed.

[0070] Among them, in step A3, the generation of the dynamic verification code is carried out according to the adjusted verification code length and character set. For example, if the character set is adjusted to include special characters, the generated dynamic verification code will include special characters. After the dynamic verification code is generated, it is sent to the mobile phone pre-bound by the user through the SMS gateway.

[0071] Among them, in step A4, when verifying the dynamic verification code entered by the user, the system will check whether the verification code is entered within the set valid duration, and whether the number of times the user enters the wrong verification code exceeds the allowed number of error retries. The verification of the correctness of the verification code is to compare the verification code entered by the user with the dynamic verification code generated in step A3.

[0072] Among them, in step A5, when the verification code is verified successfully, the system will grant the user the permission to access the cloud health record. The access permission for this session can be temporary and will automatically expire after the user closes the browser or the session times out. Thus, it ensures that the user can safely access their cloud health record during this session.

[0073] Specifically, after the user initiates a request to access the cloud health record through a public computer, the system first executes step A1 to collect information on network type, geographical location, device fingerprint, and access behavior. For example, the network type can be identified as public WiFi, the geographical location can be located at a city public library, the device fingerprint is generated by collecting operating system, browser, and hardware information, and the access behavior information can be recorded as the user accessing during non-working hours and attempting to download a large amount of data. Subsequently, the system calculates an environmental risk assessment value based on this information. Suppose the assessment value is high risk. Then, in step A2, according to the high-risk assessment value, the system adaptively adjusts the security parameters of the dynamic verification code to a strict level. For example, the verification code length is increased to 8 digits, the verification code character set is adjusted to a combination of numbers, letters, and special characters, the valid duration of the verification code is shortened to 30 seconds, and the number of error retries is limited to 2 times. Then, in step A3, the system generates an 8-digit dynamic verification code containing numbers, letters, and special characters according to the adjusted parameters and sends it to the user's mobile phone via SMS. After the user enters the received dynamic verification code on the public computer, in step A4, the system verifies whether the verification code entered by the user is correct under the constraints of a 30-second valid duration and 2 error retries. If the verification is successful, in step A5, the system grants the user the permission to access the cloud health record for this session. On the contrary, if the environmental risk assessment value is low, for example, the user accesses through a public computer for internal personnel, the system can adaptively reduce the security parameters of the dynamic verification code, such as shortening the verification code length, simplifying the character set, extending the valid duration, and increasing the number of error retries, to improve the convenience of user operations. Thus, the technical effect of dynamically adjusting the security policy in different risk environments and taking into account both security and usability is achieved.

[0074] In some embodiments, step A1 includes:

[0075] A101. Obtain the device information of the public computer to calculate and generate a device fingerprint, and determine the device risk value according to the device fingerprint; the device information includes multiple of the operating system type, version number, browser type, installed plugin list, screen resolution, CPU model, and hard disk serial number;

[0076] A102. Obtain the geographical location of the public computer to determine the location risk value;

[0077] A103. Obtain the network type of the public computer to determine the network risk value;

[0078] A104. Obtain the user's access behavior information to determine the behavior risk value; the access behavior information includes the time of accessing the cloud health record, access frequency, and data download volume;

[0079] A105. Calculate the environmental risk assessment value according to the device risk value, location risk value, network risk value, and behavior risk value.

[0080] Among them, in step A101, the device information can be obtained in various ways, such as obtaining it on the browser side through a JavaScript script, or obtaining it at the operating system level through client software. The generation of the device fingerprint can adopt the method of combining multiple device information and performing a hash operation, such as using hash algorithms such as MD5, SHA256, or SM3. The determination of the device risk value can pre-set risk levels (such as high, medium, and low three levels) and corresponding device risk values, and for different device fingerprints, set corresponding risk levels. For example, if it is detected that the public computer has installed a high-risk plugin, the device risk value can be set to the device risk value corresponding to the high-risk level.

[0081] Among them, in step A102, the geographical location can be obtained by using IP address location technology, GPS location technology, or Wi-Fi location technology, etc. The determination of the location risk value can be judged based on the abnormality degree of the geographical location. For example, if the geographical location of the user changes suddenly within a short period of time (for example, the distance between the public computer used by the user for this access and the public computer used for the previous access exceeds the preset distance threshold, and the time interval between the current time and the time of the previous access to the cloud health record is less than the preset interval threshold, then it is determined that the geographical location of the user has changed suddenly), or the geographical location is very far from the user's usual residence (the user's usual residence can be the place of residence or work place pre-entered by the user), then the location risk value can be set to a relatively high level. The setting of the location risk value can be achieved by setting a threshold according to the deviation degree or abnormality degree of the geographical location.

[0082] Among them, in step A103, the acquisition of the network type can be achieved by detecting the network environment to which the public computer is connected. For example, it can be determined whether the currently connected network is a WIFI network, a mobile network, or a wired network. The determination of the network risk value can be evaluated based on the security of the network type. For example, due to the openness of the public WIFI network, there may be a risk of data leakage, so the network risk value can be set at a relatively high level. For relatively secure network types such as home networks or enterprise dedicated lines, the network risk value can be set at a relatively low level.

[0083] Among them, in step A104, the access behavior information includes the time of accessing the cloud-based health record, the access frequency, and the data download volume, etc. The acquisition of the access behavior information can be achieved through server-side log records, client-side data collection points, etc. The determination of the behavior risk value can be judged based on the abnormality degree of the user's access behavior. For example, if the user accesses the cloud-based health record during an unconventional time period, or the access frequency is significantly higher than the average level, or a large amount of data is downloaded within a short period of time, the behavior risk value can be set at a relatively high level. The setting of the behavior risk value can be achieved by setting thresholds according to the access time deviation, access frequency deviation, and download volume deviation, etc.

[0084] Among them, in step A105, the device risk value, location risk value, network risk value, and behavior risk value are comprehensively considered to calculate the environmental risk assessment value. The calculation of the environmental risk assessment value can adopt methods such as weighted summation, fuzzy evaluation, or neural network. In the weighted summation method, different weight coefficients can be set according to the importance of each risk factor. For example, the weight of the device risk value is higher than that of the location risk value and the network risk value, and the weight of the behavior risk value is higher than that of the device risk value. By comprehensively considering various risk factors, the environmental risk of the user accessing the cloud-based health record can be evaluated more comprehensively and accurately.

[0085] Specifically, when the user accesses the cloud-based health record through a public computer, the solution of the present application first comprehensively collects risk-related information from four dimensions: device, location, network, and behavior through steps A101 to A104. In step A101, various device information of the public computer is obtained to generate a device fingerprint and determine a device risk value, thereby identifying potential risks in the device environment. Steps A102 and A103 respectively evaluate the environmental risks from the two dimensions of geographical location and network type to determine whether the user is in an abnormal geographical location or an insecure network environment. Step A104 starts from the perspective of the user's access behavior, analyzes behavioral characteristics such as the user's access time, frequency, and data download volume, and identifies whether there are abnormalities in the user's access behavior. Subsequently, in step A105, the risk values of the above four dimensions are integrated to calculate the final environmental risk assessment value. Through this multi-dimensional and multi-angle risk assessment method, the environmental risks of the user accessing the cloud-based health record can be identified more precisely and accurately, overcoming the problems of insufficient meticulousness and comprehensiveness in environmental risk assessment in traditional technologies, and providing a more reliable basis for adaptively adjusting the security parameters of the dynamic verification code subsequently. Compared with the solutions that rely only on a single factor or a few factors for risk assessment, the solution of the present application can more effectively identify potential security risks, thereby enhancing the security of accessing the cloud-based health record in open environments such as public computers.

[0086] Through the above technical solution, the present application can more precisely and comprehensively evaluate the environmental risks when the user accesses the cloud-based health record through a public computer, thereby providing a more accurate basis for adaptively adjusting the security parameters of the dynamic verification code subsequently, and further enhancing the security of accessing the cloud-based health record in public places such as public computers, effectively reducing the risk of leakage of health record data.

[0087] In some preferred embodiments, step A101 includes:

[0088] Obtain the device information of the public computer in real time and calculate to generate the current device fingerprint;

[0089] Perform a hashing operation on the current device fingerprint using the SM3 cryptographic hashing algorithm to obtain the current device fingerprint hash value;

[0090] Compare the current device fingerprint hash with the reference device fingerprint hash to determine the device risk value; the reference device fingerprint hash is the device fingerprint hash value generated based on the original and unmodified device information of the public computer.

[0091] Among them, real-time acquisition of device information means that every time a user attempts to access the cloud health record through a public computer, the system will re-collect the device information of the public computer to ensure the timeliness of the device fingerprint. The device information may include data that can reflect the device status, such as the operating system type, version number, browser type, and installed plug-in list. The calculation and generation of the current device fingerprint can be to combine the collected multiple device information and calculate the fingerprint information representing the current device characteristics through a preset algorithm. The SM3 cryptographic hash algorithm is used to perform a hash operation on the current device fingerprint in order to convert the device fingerprint into a hash value of a fixed length. The one-way hashing feature of the SM3 cryptographic hash algorithm ensures that the original device fingerprint cannot be deduced from the hash value, thereby ensuring the security of the device fingerprint. Compare the current device fingerprint hash with the reference device fingerprint hash. The reference device fingerprint hash is pre-calculated and stored, representing the device fingerprint hash value of the public computer in the original untampered state. By comparing these two hash values, it can be determined whether the device information of the public computer has been tampered with. The device risk value is determined based on the hash value comparison result. For example, if the current device fingerprint hash is consistent with the reference device fingerprint hash, the device risk value can be set to a lower level, indicating that the risk of the device information not being tampered with is low; conversely, if the two are inconsistent, the device risk value can be set to a higher level, indicating that the risk of the device information being tampered with is high.

[0092] Specifically, when a user initiates a cloud health record access request on a public computer, the security management device first collects the device information of the public computer in real time, such as the operating system version, browser type, and installed plug-in list. Then, the device combines these device information and calculates the current device fingerprint through the fingerprint generation algorithm. In order to ensure the security of the device fingerprint, the device uses the SM3 cryptographic hash algorithm to perform a hash operation on the current device fingerprint to obtain the current device fingerprint hash value. The device pre-stores a reference device fingerprint hash value, which is calculated by the same method when the initial configuration of the public computer is completed and in a safe state. The device compares the current device fingerprint hash value with the reference device fingerprint hash value. If the two are consistent, it indicates that the device information of the public computer has not been tampered with and the device risk value is low. If the two are inconsistent, it indicates that the device information of the public computer may have been tampered with and the device risk value is high. The device risk value will be used as part of the environmental risk assessment value for the adaptive adjustment of the subsequent dynamic verification code security parameters. Through the above steps, it is possible to effectively identify whether the public computer device information has been tampered with, thereby more accurately assessing the device risk and improving the effectiveness of subsequent security management.

[0093] By adopting the above technical means, the problem that the information of public computer devices is easily tampered with, resulting in inaccurate device risk assessment, can be effectively solved. The SM3 cryptographic hash algorithm is used to perform a hash operation on the device fingerprint, ensuring the security of the device fingerprint. By comparing the current and reference device fingerprint hash values, it is possible to accurately determine whether the device information has been tampered with, thereby providing a reliable basis for the effective implementation of subsequent security management measures for device risk assessment.

[0094] In some preferred embodiments, step A104 includes:

[0095] Obtain the current time when the user accesses the cloud health record, and calculate the access time risk degree according to the deviation between the current time and the user's reference access time; the reference access time is the access time reference amount statistically obtained based on the user's historical access records;

[0096] Statistically obtain the access frequency of the user within the first preset time period, and calculate the access frequency risk degree according to the deviation between the access frequency within the first preset time period and the user's reference access frequency; the reference access frequency is the average access frequency statistically obtained based on the user's historical access records;

[0097] According to the data download volume and access times of the user within the second preset time period, calculate the download volume per access, and calculate the download volume risk degree according to the deviation between the download volume per access and the user's reference download volume per access; the reference download volume per access is the average download volume per access statistically obtained based on the user's historical access records;

[0098] Calculate the behavior risk value according to the access time risk degree, access frequency risk degree, and download volume risk degree.

[0099] Among them, the present technical solution focuses on the calculation of the behavior risk value, specifically considering three dimensions: access time, access frequency, and data download volume. The access time risk degree is quantified by comparing the deviation degree between the current access time and the reference access time (the access time here refers to the time within 24 hours of a day, for example, 14:00). For example, if the user is accustomed to accessing during the day and the current access occurs at night, the access time risk degree increases. The reference access time can be determined by the average access time or the common access time range of the user's historical access records. The access time risk degree can be calculated using a preset calculation model or obtained by looking up a table according to the deviation between the current time and the user's reference access time.

[0100] Among them, the access frequency risk degree is evaluated by examining the deviation degree between the access frequency within the first preset time period and the reference access frequency. For example, if a user accesses once a week on average and accesses frequently within a short period of time, the access frequency risk degree increases. The reference access frequency can be calculated from the average access frequency of the user's historical access records, and the first preset time period can be set to days, weeks, months, etc. The access frequency risk degree can be calculated or obtained by looking up a table according to the deviation between the access frequency within the first preset time period and the user's reference access frequency using a preset calculation model.

[0101] Among them, the download volume risk degree is measured by analyzing the difference between the single - access download volume within the second preset time period and the reference single - access download volume. For example, if a user usually only downloads a small amount of data and attempts to download a large amount of data during this access, the download volume risk degree rises. The reference single - access download volume can be statistically obtained from the average single - access download volume of the user's historical access records, and the second preset time period can be set according to actual needs. The download volume risk degree can be calculated or obtained by looking up a table according to the deviation between the single - access download volume and the user's reference single - access download volume using a preset calculation model.

[0102] Finally, the access time risk degree, access frequency risk degree, and download volume risk degree are combined to calculate the behavior risk value, realizing the comprehensive risk assessment of the user's access behavior.

[0103] Specifically, the behavior risk value calculation method aims to more precisely evaluate the security risks of the user's access behavior. The system first learns and records the user's historical access habits, establishing benchmarks such as the reference access time, reference access frequency, and reference single - access download volume. When the user initiates a cloud - based health record access request on a public computer, the system real - time obtains the user's current access behavior information such as the access time, access frequency, and data download volume, and compares this information with the historical benchmarks. If the current access behavior deviates significantly from the user's historical habits, such as an abnormal access time, too high an access frequency, or an abnormal increase in data download volume, the system will determine that the risk of this access behavior is high and assign a higher behavior risk value. This method can effectively identify the theft of the user account or abnormal access behavior because the behavior patterns of thieves or malicious visitors usually differ significantly from the user's historical habits. By comprehensively considering the access time risk degree, access frequency risk degree, and download volume risk degree, the system can more comprehensively and accurately evaluate the behavior risk, providing a more reliable basis for subsequent environmental risk assessment and security parameter adjustment.

[0104] In some specific embodiments, a weighted average method can be used to calculate the behavioral risk value. For example, different weight coefficients can be preset for the access time risk degree, access frequency risk degree, and download volume risk degree. The weight coefficients can be determined according to the influence degree of each risk factor on security. Then, each risk degree is multiplied by its corresponding weight coefficient, and the products are added together to obtain the final behavioral risk value. As a preferred embodiment, the reference access time can be set as the time period with the highest frequency of occurrence in the user's historical access records, the reference access frequency can be set as the average access frequency in the user's historical access records, and the reference single download volume can be set as the average single download volume in the user's historical access records. The first preset time period and the second preset time period can be flexibly adjusted according to the actual application scenario. For example, for users with a low access frequency, the time period can be set longer to more accurately count the access frequency.

[0105] In some embodiments, step A2 includes:

[0106] A201. Calculate the safety coefficient of the current security parameter, denoted as the current safety coefficient; the current security parameter is a preset security parameter or a set of security parameters randomly selected from the historical security parameter set;

[0107] A202. According to the environmental risk assessment value, query the preset security parameter adjustment strategy table to determine the target safety coefficient corresponding to the environmental risk assessment value;

[0108] A203. If the target safety coefficient is higher than the current safety coefficient, then according to the first preset adjustment priority, adjust each parameter in the current security parameter in sequence until the safety coefficient corresponding to the adjusted security parameter reaches the target safety coefficient;

[0109] A204. If the target safety coefficient is lower than the current safety coefficient, then according to the second preset adjustment priority opposite to the first preset adjustment priority, adjust each parameter in the current security parameter in sequence until the safety coefficient corresponding to the adjusted security parameter reaches the target safety coefficient.

[0110] Among them, in step A201, the current safety factor is calculated. The safety factor is a quantitative indicator of the safety level. The default safety parameter can be preset or randomly selected from the set of historical safety parameters to provide a benchmark for subsequent adjustment. The set of historical safety parameters can be the set of all safety parameters generated within a preset time window or the set of the most recently generated N groups of safety parameters, where N is a preset positive integer. The set of historical safety parameters can also record the historical environmental risk assessment values corresponding to each group of safety parameters. Several groups of safety parameters with the historical environmental risk assessment values closest to the current environmental risk assessment value can be extracted from the set of historical safety parameters as alternative safety parameters, and then a group of safety parameters can be randomly selected from the alternative safety parameters as the current safety parameter, thereby reducing the adjustment workload in subsequent steps A203 and A204 and enabling the safety parameter to reach the target safety factor faster.

[0111] Among them, the calculation of the safety factor can be implemented using a preset safety factor calculation model. This model can comprehensively consider factors such as the verification code length, the complexity of the verification code character set, the effective duration, and the number of error retries, assign different weights, and calculate a comprehensive safety factor value. The complexity of the verification code character set can be obtained by weighted calculation using the character types and the corresponding number of characters included in the verification code character set. For example, if the verification code character set includes A digits, B letters, and C special symbols, then the complexity of the verification code character set = w1*A + w2*B + w3*C, where w1, w2, and w3 are the weight values of digits, letters, and special symbols, respectively.

[0112] Among them, in step A202, a preset safety parameter adjustment strategy table is introduced. The system queries this table based on the environmental risk assessment value, and the target safety factor is determined. The target safety factor matches the current risk level. The strategy table establishes a mapping relationship between the risk level and the safety factor, enabling the adjustment of the safety parameter to be based on the risk assessment result.

[0113] Among them, in steps A203 and A204, specific adjustment strategies are described. When the target safety factor is higher than the current value, it indicates an increase in risk. The system adjusts the security parameters step by step according to the first preset adjustment priority (for example, the first preset adjustment priority is: verification code length > verification code character set > verification code validity period > verification code error retry times, that is, first adjust the verification code length, then adjust the verification code character set, then adjust the verification code validity period, and finally adjust the verification code error retry times) until the safety factor of the adjusted security parameters reaches the target level. This priority adjustment method makes the improvement of security intensity hierarchical and targeted. On the contrary, when the target safety factor is lower than the current value, it indicates a decrease in risk. The system then reduces the security parameters according to the second preset adjustment priority opposite to the first priority (for example, the first preset adjustment priority is: verification code length < verification code character set < verification code validity period < verification code error retry times, that is, first adjust the verification code error retry times, then adjust the verification code validity period, then adjust the verification code character set, and finally adjust the verification code length), which can optimize the user experience while ensuring security. Thus, by introducing the safety factor, policy table, and priority adjustment mechanism, a structured and refined dynamic verification code security parameter adaptive adjustment scheme is provided. This scheme can not only flexibly adjust the security intensity according to the environmental risk assessment value, but also take into account efficiency and user experience during the security intensity adjustment process.

[0114] Specifically, this solution provides an effective mechanism for adaptively adjusting the security parameters of dynamic verification codes through steps A201 to A204. First, step A201 calculates the security coefficient of the current security parameters, which provides a quantitative benchmark for the adjustment of security parameters. The introduction of the security coefficient makes it possible to evaluate and compare security levels, laying the foundation for subsequent adaptive adjustment. Secondly, step A202 introduces a preset security parameter adjustment strategy table, which establishes a mapping relationship between the environmental risk assessment value and the target security coefficient. By querying this strategy table, the system can quickly determine the appropriate target security coefficient according to the current environmental risk assessment value, ensuring that the adjustment of security parameters matches the risk level. Then, steps A203 and A204 propose specific implementation methods based on priority adjustment. When the target security coefficient is higher than the current security coefficient, the system follows the first preset adjustment priority. For example, it can first increase the verification code length and then enhance the character set complexity to gradually increase the security strength. This hierarchical adjustment method avoids excessive adjustment of security parameters and can minimize the impact on user experience while meeting security requirements. Conversely, when the risk decreases, the security parameters are reduced according to the opposite priority, achieving a balance between security strength and user experience. Thus, this solution can adaptively adjust the security parameters of dynamic verification codes according to the environmental risk assessment value, providing a refined, effective and user experience - considerate security parameter adjustment mechanism, overcoming the deficiencies in the background technology.

[0115] Further, step A2 may further include the steps:

[0116] A204. Obtain the average speed of the user inputting the dynamic verification code;

[0117] A205. Generate a duration correction coefficient according to the average speed to correct the effective duration of the adjusted verification code.

[0118] Among them, in step A204, the way to obtain the average speed of the user inputting the dynamic verification code can be that the system records the total duration and total number of times of the user's multiple dynamic verification code inputs, and calculates it by dividing the total duration by the total number of times. As another implementation method, the average speed can also be obtained by weighted average. The recorded data closer to the input speed at the current moment is given a higher weight. Thus, the average speed can more accurately reflect the user's recent input habits.

[0119] Among them, in step A205, the generation of the duration correction coefficient can consider the correlation between the average user speed and the verification code type. The more complex the verification code type, the longer the time usually taken for the user to input. Therefore, a type adjustment factor can be introduced, and the higher the complexity of the verification code type, the larger the value of the type adjustment factor. The duration correction coefficient can be designed to be inversely proportional to the average speed and directly proportional to the type adjustment factor. For example, the duration correction coefficient can be calculated as the type adjustment factor divided by the average speed. Through the duration correction coefficient, the adjusted valid duration of the verification code can better fit the actual input speed of the user and the complexity of the verification code itself.

[0120] Specifically, during the adaptive adjustment of the dynamic verification code security parameters, in addition to considering the environmental risk assessment value, the average speed of the user inputting the dynamic verification code is also taken into account. The system first obtains the average speed of the user inputting the dynamic verification code, which can reflect the user's input habits and speed capabilities. Then, based on this average speed, the duration correction coefficient is generated. This duration correction coefficient is then used to correct the valid duration of the verification code that has been adjusted based on the environmental risk assessment value. Thus, the adjustment of the valid duration of the verification code no longer depends solely on the environmental risk assessment value, but also combines the actual input speed of the user, achieving a more refined dynamic adjustment. This adjustment mechanism makes the setting of the valid duration of the verification code more reasonable and user-friendly. For users with a slower input speed, the system can provide more sufficient input time to avoid verification failures caused by insufficient time, thereby enhancing user friendliness. At the same time, for users with a fast input speed, the system can also avoid setting an overly long valid duration of the verification code, reducing potential security risks.

[0121] In some specific embodiments, after a user initiates a request to access a cloud-based health record on a public computer, the risk assessment module first collects risk-related information such as network type, geographical location, device fingerprint, and access behavior information, and calculates an environmental risk assessment value. The parameter adjustment module queries a preset security parameter adjustment policy table based on the environmental risk assessment value to preliminarily determine the security parameters of the dynamic verification code, including the valid duration of the verification code. Before sending the verification code, the average input speed of the user's historical dynamic verification codes is further obtained. For example, the average speed is obtained by dividing the total duration of the user's last ten verification code inputs by ten. Then, a type adjustment factor is determined according to the character set type of the verification code. For example, the type adjustment factor for a pure numeric verification code is set to 1, and the type adjustment factor for an alphanumeric mixed verification code is set to 2, etc. The duration correction coefficient is calculated by dividing the type adjustment factor by the average speed, and then the duration correction coefficient is multiplied by the preliminarily determined valid duration of the verification code (i.e., the adjusted valid duration of the verification code obtained in step A203 or step A204). Suppose the preliminarily determined valid duration of the verification code is 60 seconds, and the calculated duration correction coefficient is 0.8, then the finally adjusted valid duration of the verification code is 60 seconds * 0.8 = 48 seconds. The adjusted valid duration of the verification code is used in the subsequent verification code verification step to ensure that the user completes the verification code input within the adjusted valid duration. Thus, the valid duration of the verification code can be dynamically corrected according to the user's input speed, realizing a security control that is more in line with the user's input efficiency and improving the user experience.

[0122] In some possible embodiments, step A205 includes:

[0123] Determine the verification code type according to the adjusted verification code character set; the verification code types include pure numeric verification codes, pure alphabetic verification codes, alphanumeric mixed verification codes, special symbol-numeric mixed verification codes, special symbol-alphabet mixed verification codes, and special symbol-alphanumeric mixed verification codes;

[0124] Determine a type adjustment factor according to the verification code type; the more complex the verification code type, the larger the type adjustment factor;

[0125] Calculate a duration correction coefficient according to the average speed and the type adjustment factor; wherein, the duration correction coefficient is inversely proportional to the average speed and directly proportional to the type adjustment factor.

[0126] Among them, the verification code type is identified based on the adjusted verification code character set. For example, if the character set only contains numbers, the verification code type is determined to be a pure numeric verification code.

[0127] Among them, the type adjustment factor is set according to the complexity of the verification code type. A more complex verification code type corresponds to a larger type adjustment factor. Sorted from the largest to the smallest in terms of complexity, the order is: pure digital verification code, pure letter verification code, alphanumeric mixed verification code, special symbol and digital mixed verification code, special symbol and letter mixed verification code, special symbol, letter and digital mixed verification code. For example, the type adjustment factor of a pure digital verification code can be set to 1.0, while the type adjustment factor of a special symbol, letter and digital mixed verification code can be set to 1.5.

[0128] Among them, the duration correction coefficient is calculated based on the average speed and the type adjustment factor. The duration correction coefficient is inversely proportional to the average speed. For example, the faster the average speed, the smaller the duration correction coefficient. The duration correction coefficient is directly proportional to the type adjustment factor. For example, the larger the type adjustment factor, the larger the duration correction coefficient. Thus, the duration correction coefficient can be expressed as: duration correction coefficient = type adjustment factor / average speed.

[0129] Specifically, during the adjustment process of the verification code valid duration, first, the system analyzes the verification code character set determined after the adjustment in step A2 to judge the type of the current dynamic verification code. The verification code types are divided into multiple levels, including but not limited to pure digital, pure letter, alphanumeric mixed, special symbol and digital mixed, special symbol and letter mixed, and special symbol, letter and digital mixed verification codes. Each verification code type is pre-assigned a type adjustment factor, which quantifies the input complexity of this type of verification code. For example, for a pure digital verification code, since its input is relatively simple, it is assigned a lower type adjustment factor, such as 1.0. For an alphanumeric mixed verification code containing special symbols, since the input is more complex and time-consuming, it is assigned a higher type adjustment factor, such as 1.5. Then, the system combines the average input speed of the user's dynamic verification code obtained in step A204 and the type adjustment factor corresponding to the current verification code type to calculate the duration correction coefficient. The calculation method is to divide the type adjustment factor by the average speed to obtain the duration correction coefficient. The duration correction coefficient is then used to correct the verification code valid duration adjusted in step A2 to generate the final verification code valid duration. In this way, the adjustment of the verification code valid duration not only considers the user's average input speed but also takes into account the complexity of the verification code itself, making the setting of the verification code valid duration more reasonable.

[0130] In some embodiments, step A3 includes:

[0131] A301. Generate a dynamic verification code using a random algorithm according to the adjusted verification code length and verification code character set;

[0132] A302. Encrypt the generated dynamic verification code to obtain the encrypted dynamic verification code ciphertext;

[0133] A303. Package the dynamic verification code ciphertext and the mobile phone number pre-bound by the user into a short message sending request, and send the short message sending request to the short message center through the short message gateway, so that the short message center decrypts the dynamic verification code ciphertext to obtain the original dynamic verification code, and sends the short message containing the dynamic verification code to the mobile phone pre-bound by the user.

[0134] Among them, in step A301, the generation of the dynamic verification code is based on the adjusted verification code length and verification code character set in the previous step. Specifically, if the adjusted verification code length is 6 digits and the verification code character set is numbers and letters, the generated dynamic verification code will be a 6-digit string randomly composed of numbers and letters. The selection of the random algorithm can use a mature pseudo-random number generator. For example, the Random class in Java or the random module in Python can be used to implement the random generation of the dynamic verification code.

[0135] Among them, step A302 is to perform an encryption process on the dynamic verification code generated in step A301. The selection of the encryption algorithm needs to be weighed according to the actual security requirements and performance requirements. For example, a symmetric encryption algorithm such as AES or SM4 can be used, or a lightweight exclusive-or encryption algorithm can be used. The management of the encryption key is also crucial, and a secure way needs to be used to store and transmit the key. For example, a key management system can be used to uniformly manage the encryption key.

[0136] Among them, step A303 describes the sending process of the encrypted dynamic verification code ciphertext. The dynamic verification code ciphertext and the user's mobile phone number are packaged into a short message sending request and sent to the short message center through the short message gateway. It is worth noting that after receiving the short message sending request, the short message center needs to perform a decryption operation to restore the dynamic verification code ciphertext to the original dynamic verification code, and then send the short message containing the original dynamic verification code to the user's mobile phone. This design of encrypting first and then decrypting ensures that the dynamic verification code exists in the form of ciphertext in the short message transmission link. Even if the short message is illegally intercepted, the original dynamic verification code cannot be directly obtained.

[0137] Specifically, for the security issues in the process of sending dynamic verification codes, this solution effectively solves the potential security risk of verification code leakage during transmission by performing encryption processing before sending the dynamic verification code. In the scenario of using a public computer in a public service place to access the cloud health record, after the user initiates an access request, the system will adaptively adjust the security parameters of the dynamic verification code according to the environmental risk assessment value of the public computer, including the verification code length and character set. The adjusted verification code parameters are used to generate the dynamic verification code. To prevent the dynamic verification code from being intercepted during the SMS transmission process, before sending the SMS, the generated dynamic verification code will first be encrypted into ciphertext. The encrypted ciphertext, together with the user's mobile phone number, is encapsulated into an SMS sending request and sent to the SMS center through the SMS gateway. After receiving the request, the SMS center first decrypts the ciphertext to restore the original dynamic verification code, and then sends the SMS containing the original dynamic verification code to the user's mobile phone. After the user enters the received dynamic verification code on the public computer, the system will verify it. Only when the verification passes can the user obtain the access right to the cloud health record. Thus, even if the SMS is illegally intercepted by a third party during the transmission process, since what is intercepted is the encrypted ciphertext rather than the original dynamic verification code, the attacker cannot directly obtain valid verification information, thereby ensuring the security of the dynamic verification code during transmission and enhancing the overall security protection ability of the system.

[0138] Preferably, step A302 may include:

[0139] According to the adjusted verification code character set, select the encryption algorithm type; when the verification code character set only contains numbers, select the exclusive OR encryption algorithm, when the verification code character set contains letters but does not contain special characters, select the AES encryption algorithm, and when the verification code character set contains special characters, select the SM4 encryption algorithm;

[0140] According to the selected encryption algorithm type, generate the corresponding encryption key; if the exclusive OR encryption algorithm is selected, generate a random number of the same length as the verification code as the encryption key, and if the AES or SM4 encryption algorithm is selected, generate a fixed-length encryption key that meets the algorithm requirements;

[0141] Adopt the selected encryption algorithm and encryption key to encrypt the dynamic verification code to obtain the encrypted dynamic verification code ciphertext.

[0142] Among them, for the selection of the encryption algorithm type, the system will first analyze the adjusted verification code character set. If the character set only contains numbers, the faster XOR encryption algorithm will be selected to ensure encryption efficiency. When the character set is extended to include letters but not special characters, the security requirements increase, and the system will upgrade to use the more secure AES encryption algorithm. If the character set further includes special characters, the more secure SM4 encryption algorithm will be selected to meet the requirements of a higher security level. For the generation of the encryption key, it is determined according to the selected encryption algorithm type. Specifically, if the XOR encryption algorithm is selected, a random number of the same length as the verification code will be generated as the encryption key to ensure that the key is the same length as the plaintext and meets the requirements of XOR encryption. If the AES or SM4 encryption algorithm is selected, a fixed-length encryption key that meets the requirements of the corresponding algorithm will be generated, such as the commonly used 128-bit, 192-bit, or 256-bit keys for the AES algorithm. After the selection and generation of the encryption algorithm type and key, the system will use the selected encryption algorithm and the generated encryption key to encrypt the dynamic verification code, and finally obtain the encrypted dynamic verification code ciphertext for subsequent SMS sending and verification processes to ensure the security of the dynamic verification code during transmission.

[0143] Specifically, this solution aims to solve the problem that a fixed encryption algorithm may not be able to balance efficiency and security simultaneously during the encryption process of dynamic verification codes. By analyzing the dynamic verification code character set and adaptively selecting an appropriate encryption algorithm based on it, the dynamic matching between the encryption algorithm and the complexity of the verification code character set is achieved. When the verification code character set only contains numbers, the fast XOR encryption algorithm is selected to ensure encryption efficiency. When the character set is extended to include letters but no special characters, the more secure AES encryption algorithm is upgraded and selected. When the character set further includes special characters, the more secure SM4 encryption algorithm is selected. Thus, the balance between efficiency and security is achieved under different security requirements, and the security of the dynamic verification code during transmission is more effectively guaranteed. The XOR encryption algorithm is suitable for the scenario of pure digital verification codes with relatively low security requirements due to its high efficiency, while advanced encryption algorithms such as AES and SM4 provide stronger security guarantees for verification codes with more complex character sets. This differential processing method enables the encryption process to meet the requirements of different security levels, avoid reducing efficiency due to over-encryption, and improve the overall performance and security of the system.

[0144] Reference Figure 2 , this application provides a cloud data security management device for securely managing the process of accessing cloud health records through public computers. The device includes:

[0145] A risk assessment module 1, which is used to collect risk-related information in response to a cloud-based health record access request initiated by a user through a public computer, and is used to calculate an environmental risk assessment value; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information (for the specific process, refer to step A1 in the previous text);

[0146] A parameter adjustment module 2, which is used to adaptively adjust the security parameters of the dynamic verification code according to the environmental risk assessment value; the greater the environmental risk assessment value, the stricter the security parameters; the security parameters include verification code length, verification code character set, verification code validity duration, and verification code error retry times (for the specific process, refer to step A2 in the previous text);

[0147] A verification code generation and sending module 3, which is used to generate a dynamic verification code according to the adjusted verification code length and verification code character set, and send the generated dynamic verification code to the mobile phone pre-bound by the user (for the specific process, refer to step A3 in the previous text);

[0148] A verification code verification module 4, which is used to verify the correctness of the input dynamic verification code according to the generated dynamic verification code under the constraints of the adjusted verification code validity duration and verification code error retry times when receiving the dynamic verification code input by the user on the public computer (for the specific process, refer to step A4 in the previous text);

[0149] A permission control module 5, which is used to grant the user the access permission to the cloud-based health record for this session after successful verification (for the specific process, refer to step A5 in the previous text).

[0150] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0151] In addition, the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] Furthermore, in each embodiment of the present application, each functional module may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0153] In this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0154] The above description is only for the embodiments of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A cloud data security management method for securely managing the process of accessing cloud-based health records through public computers, characterized in that, The steps of the method include: A1. In response to a cloud-based health record access request initiated by a user through a public computer, collect risk-related information for calculating an environmental risk assessment value; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information; A2. Adaptively adjust the security parameters of the dynamic verification code according to the environmental risk assessment value; the greater the environmental risk assessment value, the stricter the security parameters; the security parameters include verification code length, verification code character set, verification code validity duration, and verification code error retry times; A3. Generate a dynamic verification code according to the adjusted verification code length and verification code character set, and send the generated dynamic verification code to the mobile phone pre-bound by the user; A4. Under the constraints of the adjusted verification code validity duration and verification code error retry times, when receiving the dynamic verification code input by the user on the public computer, verify the correctness of the input dynamic verification code according to the generated dynamic verification code; A5. When the verification is passed, grant the user the access permission to the cloud-based health record for this session.

2. The cloud data security management method according to claim 1, characterized in that Step A1 includes: A101. Obtain the device information of the public computer for calculating and generating a device fingerprint, and determine the device risk value according to the device fingerprint; the device information includes multiple of operating system type, version number, browser type, installed plugin list, screen resolution, CPU model, and hard disk serial number; A102. Obtain the geographical location of the public computer for determining the location risk value; A103. Obtain the network type of the public computer for determining the network risk value; A104. Obtain the access behavior information of the user for determining the behavior risk value; the access behavior information includes the time of accessing the cloud-based health record, access frequency, and data download volume; A105. Calculate the environmental risk assessment value according to the device risk value, the location risk value, the network risk value, and the behavior risk value.

3. The cloud data security management method according to claim 2, wherein, Step A101 includes: Obtain the device information of the public computer in real time and calculate and generate the current device fingerprint; Perform a hash operation on the current device fingerprint using the SM3 cryptographic hashing algorithm to obtain the current device fingerprint hash value; Compare the current device fingerprint hash with the reference device fingerprint hash to determine the device risk value; the reference device fingerprint hash is the device fingerprint hash value generated according to the original and unmodified device information of the public computer.

4. A cloud data security management method according to claim 2, characterized in that, Step A104 includes: Obtain the current time when the user accesses the cloud-based health record, and calculate the access time risk degree according to the deviation between the current time and the user's reference access time; the reference access time is the access time reference quantity statistically obtained according to the user's historical access records; Statistically count the access frequency of the user within a first preset time period, and calculate the access frequency risk degree according to the deviation between the access frequency within the first preset time period and the user's reference access frequency; the reference access frequency is the average access frequency statistically obtained according to the user's historical access records; Calculate the download amount per access according to the data download amount and the number of accesses of the user within the second preset time period, and calculate the download amount risk degree according to the deviation between the download amount per access and the reference download amount per access of the user; the reference download amount per access is the average download amount per access statistically obtained according to the historical access records of the user. Calculate the behavior risk value according to the access time risk degree, the access frequency risk degree, and the download amount risk degree.

5. A cloud data security management method according to claim 1, characterized in that, Step A2 includes: A201. Calculate the safety factor of the current security parameter, denoted as the current safety factor; the current security parameter is a preset security parameter or a set of security parameters randomly selected from the historical security parameter set. A202. Query the preset security parameter adjustment strategy table according to the environmental risk assessment value, and determine the target safety factor corresponding to the environmental risk assessment value. A203. If the target safety factor is higher than the current safety factor, adjust each parameter in the current security parameter in turn according to the first preset adjustment priority until the safety factor corresponding to the adjusted security parameter reaches the target safety factor. A204. If the target safety factor is less than the current safety factor, adjust each parameter in the current security parameter in turn according to the second preset adjustment priority opposite to the first preset adjustment priority until the safety factor corresponding to the adjusted security parameter reaches the target safety factor.

6. The method for cloud data security management according to claim 5, wherein Step A2 further includes the steps: A204. Obtain the average speed at which the user inputs the dynamic verification code. A205. Generate a duration correction factor according to the average speed to correct the effective duration of the adjusted verification code.

7. The method for cloud data security management according to claim 6, characterized in that, Step A205 includes: Determine the verification code type according to the adjusted verification code character set; the verification code type includes pure digital verification code, pure letter verification code, alphanumeric mixed verification code, special symbol and number mixed verification code, special symbol and letter mixed verification code, and special symbol, letter and number mixed verification code. Determine the type adjustment factor according to the verification code type; the more complex the verification code type, the larger the type adjustment factor. Calculate the duration correction factor according to the average speed and the type adjustment factor; wherein, the duration correction factor is inversely proportional to the average speed and directly proportional to the type adjustment factor.

8. A cloud data security management method according to claim 1, characterized in that Step A3 includes: A301. Generate a dynamic verification code by using a random algorithm according to the adjusted verification code length and verification code character set. A302. Encrypt the generated dynamic verification code to obtain the encrypted dynamic verification code ciphertext. A303. Package the dynamic verification code ciphertext and the mobile phone number pre-bound by the user into a short message sending request, and send the short message sending request to the short message center through the short message gateway, so that the short message center decrypts the dynamic verification code ciphertext to obtain the original dynamic verification code, and sends the short message containing the dynamic verification code to the mobile phone pre-bound by the user.

9. A cloud data security management method according to claim 8, characterized in that, Step A302 includes: Select the encryption algorithm type according to the adjusted verification code character set; when the verification code character set only contains numbers, select the XOR encryption algorithm; when the verification code character set contains letters but no special characters, select the AES encryption algorithm; when the verification code character set contains special characters, select the SM4 encryption algorithm; Generate the corresponding encryption key according to the selected encryption algorithm type; if the XOR encryption algorithm is selected, generate a random number of the same length as the verification code as the encryption key; if the AES or SM4 encryption algorithm is selected, generate a fixed-length encryption key that meets the algorithm requirements; Use the selected encryption algorithm and the encryption key to encrypt the dynamic verification code to obtain the encrypted dynamic verification code ciphertext.

10. A cloud data security management device for securely managing the process of accessing cloud-based health records through public computers, characterized in that, The device includes: A risk assessment module, which is used to respond to a cloud health record access request initiated by a user through a public computer, collect risk-related information for calculating an environmental risk assessment value; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information; A parameter adjustment module, which is used to adaptively adjust the security parameters of the dynamic verification code according to the environmental risk assessment value; the greater the environmental risk assessment value, the stricter the security parameters; the security parameters include verification code length, verification code character set, verification code validity period, and verification code error retry times; A verification code generation and sending module, which is used to generate a dynamic verification code according to the adjusted verification code length and verification code character set, and send the generated dynamic verification code to the mobile phone pre-bound by the user; A verification code verification module, which is used to verify the correctness of the input dynamic verification code according to the generated dynamic verification code when receiving the dynamic verification code input by the user on the public computer under the constraints of the adjusted verification code validity period and verification code error retry times; A permission control module, which is used to grant the user the access permission to the cloud health record for this session after the verification is passed.

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