Risk assessment method and device, equipment and storage medium

Abstract

The invention provides a risk assessment method and device, equipment and a storage medium, and the method comprises the steps: determining a target medical data risk assessment index system based on a data safety text, a preset data risk assessment index system and medical data features, so as to guarantee the update iteration of risk index types; determining target membership function values of the plurality of first target data risk indexes under different evaluation levels based on the initial membership function values of each first target data risk index at the last level under different evaluation levels of the plurality of evaluation contents; the assessment content is quantified, and the membership function values of the first target data risk index under different assessment levels are optimized in combination with different assessment contents, so that the problem of conflicts among different assessment contents is solved, and in the process of assessing the risk levels, the assessment efficiency is improved. And in combination with the weight of each first target data risk index and the target membership function value under different assessment levels, the accuracy of risk level assessment is improved.

Description

Technical Field

[0001] The present application belongs to the field of data security technology, and specifically relates to a risk assessment method, device, equipment and storage medium. Background Art

[0002] In recent years, the maturity of big data, cloud computing, and the Internet of Things, as well as the widespread promotion and use of medical networking platforms, have led to a sharp increase in the amount of medical data. At the same time, the probability of medical data being threatened by external attacks has also increased significantly, and security incidents such as data leakage, destruction, and abuse have emerged in an endless stream. The security risks of technical medical data are unknown and uncertain. In the face of the increasingly prominent data security issues in the digital transformation of medical institutions, the study of technical medical data risk assessment methods is of great significance for improving the risk control capabilities of technical medical data and reducing the damage caused by technical medical data security risks.

[0003] At present, there are many types of medical data risk indicators in medical network scenarios. The risk assessment method of technical medical data is mainly based on the evaluation content used by medical data experts to evaluate various risk indicators. On the one hand, due to the update and iteration of risk indicator types, it leads to waste of expert resources or inaccurate risk assessment of technical medical data. On the other hand, due to the ambiguity and uncertainty of the evaluation content and the frequent conflicts between the evaluation contents of different experts, it makes it impossible to quantitatively evaluate the risks of technical medical data or the evaluation is inaccurate. Summary of the invention

[0004] The present application proposes a risk assessment method, device, equipment and storage medium, which can solve the current technical problems of quantitative assessment or inaccurate assessment of the risks of technical form medical data.

[0005] The first embodiment of the present application proposes a risk assessment method, including:

[0006] Determine multiple data risk indicators based on the data security text, the preset data risk assessment indicator system and medical data characteristics, wherein the medical data characteristics include at least one of medical network environment characteristics and medical data characteristics;

[0007] Based on the necessity of existence of the multiple data risk indicators, determining multiple target data risk indicators existing in the target medical data risk assessment indicator system and the levels to which the multiple target data risk indicators belong;

[0008] Calculating the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels;

[0009] Determine target membership function values ​​of multiple first target data risk indicators at different evaluation levels based on the initial membership function values ​​of each first target data risk indicator at the final level at different evaluation levels under multiple evaluation contents;

[0010] The data risk level of the target medical data risk assessment indicator system is determined based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels and the target membership function values ​​of the multiple first target data risk indicators at different assessment levels.

[0011] An embodiment of the second aspect of the present application provides a risk assessment device, including:

[0012] Determine a target comprehensive weight value corresponding to each of the first target data risk indicators based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels;

[0013] Determine the comprehensive membership function value of each first target data risk indicator at different evaluation levels based on the target comprehensive weight value corresponding to each first target data risk indicator and the target membership function value of each first target data risk indicator at different evaluation levels;

[0014] Determining the comprehensive membership function value of each of the second target data risk indicators at different evaluation levels based on the comprehensive membership function value of each of the first target data risk indicators at different evaluation levels;

[0015] The evaluation level with the highest comprehensive membership function value corresponding to each of the second target data risk indicators is determined as the data risk level corresponding to each of the second target data risk indicators.

[0016] An embodiment of the third aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0017] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the method described in the first aspect above.

[0018] The technical solution provided in the embodiments of the present application has at least the following technical effects or advantages:

[0019] The present application proposes a risk assessment method, apparatus, equipment and storage medium, including: determining a target medical data risk assessment indicator system based on data security text, a preset data risk assessment indicator system and medical data characteristics to ensure the update and iteration of risk indicator types; determining target membership function values ​​of multiple first target data risk indicators at different assessment levels based on the initial membership function value of each first target data risk indicator at the final level under multiple assessment contents at different assessment levels; quantifying the assessment contents and combining different assessment contents to optimize the membership function values ​​of the first target data risk indicators at different assessment levels, solving the problem of conflicts between different assessment contents, and combining the weights of each first target data risk indicator and the target membership function values ​​at different assessment levels in the process of assessing the risk level to improve the accuracy of the risk level assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0021] Figure 1 A flow chart of a risk assessment method provided by an embodiment of the present application is shown;

[0022] Figure 2 A schematic diagram of the structure of a risk assessment device provided by an embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0026] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by technicians in the field to which this application belongs.

[0027] The risk assessment method of the present application can be executed by a computing device, and the computing device applies cloud computing and virtualization technology. The computing device can be a server, such as one server, multiple servers, a server cluster, a cloud computing platform, etc. Optionally, the computing device can also be a terminal device, such as a mobile phone, a tablet computer, a game console, a portable computer, a desktop computer, an advertising machine, an all-in-one machine, etc. The present application does not limit the device type and number of computing devices.

[0028] A risk assessment method proposed according to an embodiment of the present application is described below in conjunction with the accompanying drawings.

[0029] See also Figure 1 , the method specifically comprises the following steps:

[0030] S101. Determine multiple data risk indicators based on the data security text, the preset data risk assessment indicator system and the medical data characteristics.

[0031] The medical data characteristics include: at least one of medical network environment characteristics and medical data characteristics.

[0032] Among them, the data security text can be data security-related standard specification texts, including GB / T 20984-2007 "Information Security Technology Information Security Risk Assessment Specification", GB / T31509-2015 "Information Security Technology Information Security Risk Assessment Implementation Guide", GB / T 20984-2022 "Information Security Technology Information Security Risk Assessment Method" and GB / T39725-2020 "Information Security Technology Health and Medical Data Security Guidelines" and so on.

[0033] The preset data risk assessment indicator system can be a currently existing data risk assessment indicator system, or it can be a summary of experts in the fields of medical health and data security.

[0034] It is understandable that since the data security text is continuously updated based on the current medical data risks, the data risk indicators corresponding to the data security text will be updated relative to the data risk indicators of the preset data risk assessment indicator system. Therefore, the data risk indicators of the preset data risk assessment indicator system can be supplemented based on the data risk indicators corresponding to the data security text.

[0035] In addition, the supplemented data risk indicators need to be screened in combination with the characteristics of medical data.

[0036] Among them, both the data security text and the preset data risk assessment indicator system may include multiple initial data risk indicators. The preset data risk assessment indicator system may be updated based on the multiple initial data risk indicators of the data security text to obtain some risk data indicators. The data risk indicators may be further screened and processed based on the characteristics of medical data to screen out multiple data risk indicators.

[0037] Among them, multiple data risk indicators can be hierarchical. For example, multiple data risk indicators include: medical data privacy leakage risk, employee malicious leakage of patient privacy risk and employee misoperation leakage of patient privacy risk. Then the first-level indicator can be the medical data privacy leakage risk, and the second-level indicators to which the medical data privacy leakage risk belongs can include employee malicious leakage of patient privacy risk and employee misoperation leakage of patient privacy risk.

[0038] S102. Based on the necessity of the existence of multiple data risk indicators, determine multiple target data risk indicators existing in the target medical data risk assessment indicator system and the levels to which the multiple target data risk indicators belong.

[0039] It is understandable that due to the continuous optimization of risk technology, the risks corresponding to some previously existing risk indicators have been resolved. Therefore, after determining multiple data risk indicators, multiple data risk indicators can be screened based on the necessity of their existence, and multiple target data risk indicators can be determined to form a target medical data risk assessment indicator system.

[0040] Among them, the necessity degree is used to measure whether the data risk indicators need to be screened out.

[0041] In some embodiments, multiple data risk indicators may be sent to a screening device to obtain the existence necessity degree corresponding to each of the multiple data risk indicators output by the screening device.

[0042] The screening device may store the frequency of occurrence of the risk corresponding to each of the multiple data risk indicators in the medical system of the screening device. The higher the frequency of occurrence, the higher the necessity of existence corresponding to the data risk indicator.

[0043] If there are multiple screening devices, the necessity of existence corresponding to each of the multiple data risk indicators can be determined based on the necessity of existence corresponding to each of the multiple data risk indicators output by the multiple screening devices.

[0044] Furthermore, a data risk indicator whose existence necessity is greater than a preset existence necessity threshold may be determined as a target data risk indicator.

[0045] S103. Calculate the comprehensive weight values ​​of multiple target data risk indicators at their respective levels.

[0046] The comprehensive weight value may be a subjective weight value, an objective weight value, or a weight value obtained by combining the subjective weight value and the objective weight value, and so on.

[0047] Since multiple target data risk indicators have a hierarchical structure, in order to save calculation amount, the comprehensive weight values ​​of multiple target data risk indicators at their respective levels can be calculated first, so that the comprehensive weight values ​​of multiple target data risk indicators in the target medical data risk assessment indicator system can be calculated based on the comprehensive weight values ​​of multiple target data risk indicators at their respective levels in the subsequent calculation.

[0048] S104: Determine target membership function values ​​of multiple first target data risk indicators at different evaluation levels based on the initial membership function value of each first target data risk indicator at the final level at different evaluation levels under multiple evaluation contents.

[0049] The evaluation content may include multiple evaluation levels, the uncertainty corresponding to each of the multiple evaluation levels, and the membership function center corresponding to each of the multiple evaluation levels. The evaluation level is used to indicate the risk level corresponding to the target medical data risk assessment indicator system. For example, if the multiple evaluation levels are five evaluation levels, they are divided into V1 (high), V2 (higher), V3 (medium), V4 (lower), and V5 (low) according to the severity of the risk.

[0050] In some embodiments, the initial membership function values ​​at different evaluation levels under a certain evaluation content can be determined based on the uncertainty at different evaluation levels under a certain evaluation content and the membership function centers corresponding to multiple evaluation levels, and combined into the initial membership function value under a certain evaluation content to perform a quantitative analysis of the evaluation content to resolve the ambiguity and uncertainty of the evaluation content.

[0051] The conflicts among multiple evaluation contents can be determined based on the initial membership function values ​​under multiple evaluation contents, and then based on the conflicts among multiple evaluation contents, the target membership function values ​​under different evaluation levels of multiple first target data risk indicators can be determined to resolve the conflicts among different evaluation contents.

[0052] In some embodiments, the conflict between multiple evaluation contents may be represented by a probability distance, a conflict coefficient, or a combination of the probability distance and the conflict coefficient.

[0053] Correspondingly, the target membership function values ​​of multiple first target data risk indicators at different evaluation levels can be determined based on the probability distance of multiple evaluation contents, based on the conflict coefficient of multiple evaluation contents, or based on the probability distance and conflict coefficient of multiple evaluation contents.

[0054] S105. Determine the data risk level of the target medical data risk assessment indicator system based on the comprehensive weight values ​​of multiple target data risk indicators at their respective levels and the target membership function values ​​of multiple first target data risk indicators at different assessment levels.

[0055] The target comprehensive weight value of each first target data risk indicator can be determined based on the comprehensive weight values ​​of multiple target data risk indicators at their respective levels, and the target comprehensive weight value can be determined based on the comprehensive weight value of the level to which each first target data risk indicator belongs and the comprehensive weight value of the level to which the target data risk indicator corresponding to each level to which each first target data risk indicator corresponds belongs. For example, the target comprehensive weight value of the first target data risk indicator can be the product of the comprehensive weight value of the level to which the first target data risk indicator belongs and the comprehensive weight value of the level to which the target data risk indicator corresponding to each level to which each first target data risk indicator corresponds belongs.

[0056] In some embodiments, based on the comprehensive weight values ​​of multiple target data risk indicators at their respective levels and the target membership function values ​​of multiple first target data risk indicators at different assessment levels, determining the data risk level of the target medical data risk assessment indicator system can be achieved by: using the target weight value of the first target data risk indicator and the target membership function values ​​of multiple first target data risk indicators at different assessment levels to calculate the comprehensive membership function value of each first target data risk indicator at different assessment levels, and determining the assessment level with the highest comprehensive membership function value as the data risk level corresponding to each first target data risk indicator.

[0057] In some embodiments, the comprehensive membership function value of each first-level target data risk indicator at different evaluation levels can be further determined based on the comprehensive membership function value of each first target data risk indicator at different evaluation levels, and the evaluation level with the highest comprehensive membership function value is determined as the data risk level corresponding to each first-level target data risk indicator.

[0058] Alternatively, the comprehensive membership function values ​​of the target data risk indicators at each level at different evaluation levels are determined based on the comprehensive membership function values ​​of each first target data risk indicator at different evaluation levels, and the evaluation level with the highest comprehensive membership function value is determined as the data risk level corresponding to the target data risk indicators at each level

[0059] An embodiment of the present application provides a risk assessment method, including: determining a target medical data risk assessment indicator system based on data security text, a preset data risk assessment indicator system and medical data characteristics to ensure the update and iteration of risk indicator types; determining target membership function values ​​of multiple first target data risk indicators at different assessment levels based on the initial membership function value of each first target data risk indicator at the final level under multiple assessment contents at different assessment levels; quantifying the assessment contents and combining different assessment contents to optimize the membership function values ​​of the first target data risk indicators at different assessment levels to resolve conflicts between different assessment contents; and in the process of assessing the risk level, combining the weights of each first target data risk indicator and the target membership function values ​​at different assessment levels to improve the accuracy of the risk level assessment.

[0060] In some embodiments, the data safety text includes a general data safety text and a medical data safety text, and multiple data risk indicators are determined based on the data safety text, a preset data risk assessment indicator system and medical data characteristics, including: determining at least one general data risk indicator at the first level based on the data risk indicators of the general data safety text and the data risk indicators in the preset data risk assessment indicator system; determining at least one medical data risk indicator at the first level based on the data risk indicators of the medical data safety text and the medical data characteristics; traversing the data risk indicators of the general data safety text and the data risk indicators in the preset data risk assessment indicator system to determine at least one general data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level; traversing the data risk indicators of the medical data safety text to determine at least one medical data risk indicator at each level corresponding to each of the at least one medical data risk indicator at the first level; and forming multiple data risk indicators based on at least one general data risk indicator at the first level, at least one general data risk indicator at the first level, at least one medical data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level, and at least one medical data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level.

[0061] It can be understood that the data security text includes general data security text and medical data security text.

[0062] The data security text and the preset data risk assessment indicator system both include data risk indicators, and the data risk indicators have corresponding hierarchical relationships.

[0063] In order to improve the efficiency of determining data risk indicators, at least one general data risk indicator at the first level can be determined based on the data risk indicators of the general data security text and the data risk indicators in the preset data risk assessment indicator system, and at least one medical data risk indicator at the first level can be determined based on the data risk indicators of the medical data security text and the medical data characteristics, so as to traverse the data security text and determine at least one data risk indicator at each level corresponding to each of the multiple data risk indicators at the first level.

[0064] It is understandable that the industry generally has a corresponding data risk assessment indicator system for common data risk indicators. Therefore, if there are new data risk indicators in the general data security text, the data risk indicators in the data risk assessment indicator system can be expanded.

[0065] Regarding medical data risk indicators, combined with the characteristics of the medical Internet such as ubiquitous connectivity, super-time and space, strong continuity, high security and high accessibility, and the high sensitivity of medical data, referring to GB / T 39725-2020 "Information Security Technology Health Medical Data Security Guidelines", combined with medical data application scenarios, the security risks of medical data are analyzed, and multiple medical data risk indicators can be determined.

[0066] In some embodiments, the process of determining the general data risk indicator may be implemented as follows:

[0067] Multiple data risk indicators in the general data security text will be added and deleted as technology is updated. Addition means that new risks have emerged, and deletion means that certain risks have been overcome. Therefore, the data risk assessment indicator system needs to be updated in a timely manner to obtain the target data risk assessment indicator system.

[0068] The data risk assessment indicator system has a corresponding generation time, and multiple data risk indicators in the general data security text may have corresponding adding times. The data risk indicators at the first level in the data security text are traversed to determine the data risk indicators at the first level that exist in the data security text but do not exist in the data risk assessment indicator system. The data risk indicators at the first level whose adding time is later than the generation time of the risk assessment indicator system are determined as multiple first-level general data risk indicators. Further, the data risk indicators at the first level that exist in the data risk assessment indicator system but do not exist in the data security text are determined, and these data risk indicators are deleted from the data risk assessment indicator system to obtain multiple second-level general data risk indicators, thereby combining the first-level general data risk indicators and the second-level general data risk indicators to determine multiple first-level general data risk indicators.

[0069] Furthermore, according to the above method, at least one common data risk indicator at each level corresponding to each of the plurality of common data risk indicators is determined.

[0070] In some embodiments, the process of determining the medical data risk indicator may be implemented as follows:

[0071] Traverse the top-level data risk indicators in the medical data security text, determine the top-level data risk indicators that meet the characteristics of the medical network such as pan-connectivity, super-time and space, strong continuity, high security and high accessibility, and the high sensitivity of medical data, and determine these top-level data risk indicators as multiple top-level medical data risk indicators.

[0072] Furthermore, according to the above method, at least one medical data risk indicator at each level corresponding to each of the multiple medical data risk indicators is determined.

[0073] In an embodiment of the present application, on the one hand, the data risk indicators in the preset data risk assessment indicator system can be updated in combination with the data security text, and on the other hand, multiple data risk indicators can be obtained by combining the data security text and medical data characteristics. The data risk indicators obtained from the two aspects are further combined to obtain multiple data risk indicators to obtain data risk indicators that are in line with medical scenarios and complete.

[0074] In some embodiments, based on the necessity of existence of multiple data risk indicators, multiple target data risk indicators existing in the target medical data risk assessment indicator system and the levels to which the multiple target data risk indicators belong are determined, including: obtaining multiple existence weights corresponding to each of the at least one data risk indicator at the first level, the existence weights being used to evaluate the probability of each data risk indicator existing in the target medical data risk assessment indicator system; calculating the necessity of existence of at least one data risk indicator at the first level based on the multiple existence weights corresponding to each of the at least one data risk indicator at the first level; determining a data risk indicator whose existence necessity is greater than a first existence necessity threshold as a second target data risk indicator at the first level, and obtaining multiple second target data risk indicators; traversing the data risk indicators at multiple levels corresponding to each of the multiple second target data risk indicators in hierarchical order, and obtaining the necessity of existence of the data risk indicators at multiple levels corresponding to each of the multiple second target data risk indicators; determining a data risk indicator whose existence necessity is greater than a second existence necessity threshold as a third target data risk indicator at each level, and obtaining multiple third target data risk indicators; and forming multiple target data risk indicators based on the multiple second target data risk indicators and the multiple third target data risk indicators.

[0075] In some embodiments, multiple existence weights corresponding to at least one data risk indicator at the first level can be outputted by multiple screening devices, and the screening device can store the frequency of occurrence of the risk corresponding to each of the multiple data risk indicators in the medical system of the screening device. The higher the frequency of occurrence, the higher the existence necessity corresponding to the data risk indicator.

[0076] For any data risk indicator, the necessity of the indicator can be determined based on the multiple existence weights of the data risk indicator. For example, the necessity of the data risk indicator can be the sum of the multiple existence weights of the data risk indicator divided by the number of existence weights. For example, there are five screening devices. Assuming that the frequency of the risk corresponding to the data risk indicator in the medical system to which the screening device belongs is 100 times, the corresponding existence weight is 1, and the existence weights output by the five screening devices are 0.8, 0.9, 1, 0.9, and 0.9, respectively, then the necessity of the data risk indicator is 0.9.

[0077] The first necessity threshold and the second necessity threshold may be the same or different, and the specific values ​​may be flexibly set based on actual conditions.

[0078] The data risk indicator whose existence necessity is greater than the first existence necessity threshold is determined as a second target data risk indicator at the first level, and a plurality of second target data risk indicators are obtained.

[0079] Furthermore, according to the above method, data risk indicators whose existence necessity is greater than the second existence necessity threshold are determined as third target data risk indicators at each level, and multiple third target data risk indicators are obtained.

[0080] Based on multiple second target data risk indicators and multiple third target data risk indicators, multiple target data risk indicators are composed to obtain all target data risk indicators of a target medical data risk assessment indicator system, and are arranged in a hierarchy to obtain a target medical data risk assessment indicator system.

[0081] In an embodiment of the present application, data risk indicators are screened by the degree of necessity of existence corresponding to each of the multiple data risk indicators to determine multiple target data risk indicators and a target medical data risk assessment indicator system is established based on the levels corresponding to each of the multiple target data risk indicators to determine the risks existing in the medical system based on the target medical data risk assessment indicator system.

[0082] In some embodiments, the comprehensive weight values ​​of multiple target data risk indicators at their respective levels are calculated, including: determining the subjective weight values ​​of the multiple target data risk indicators at their respective levels based on the relative importance scale values ​​of the multiple target data risk indicators; determining the objective weight values ​​of the multiple target data risk indicators at their respective levels based on the assessment weight values ​​corresponding to each of the multiple target data risk indicators; determining the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels based on the subjective weight values ​​of the multiple target data risk indicators at their respective levels and the objective weight values ​​of the multiple target data risk indicators at their respective levels.

[0083] The embodiment of the present application adopts a subjective and objective method based on the Analytic Hierarchy Process (AHP) and the objective weighting method (criteria importance though intercrieria correlation, CRITIC) to calculate the comprehensive weight values ​​of the target data risk indicators at their respective levels to improve the accuracy of the calculation.

[0084] With respect to the subjective weight values ​​of the target data risk indicators, relative importance scale values ​​of multiple target data risk indicators can be established in advance.

[0085] Table 1 shows the relative importance scale values ​​of multiple target data risk indicators.

[0086] Indicator comparison results Relative importance scale value The two factors are equally important. 1 Compared with the two factors, the former is slightly more important than the latter. 3 Compared with the two factors, the former is obviously more important than the latter. 5 Compared with the two factors, the former is more important than the latter. 7 Compared with the two factors, the former is more important than the latter. 9 The middle value between the two 2，4，6，8

[0087] Table 1

[0088] Further, the importance scale values ​​corresponding to the risk indicators of multiple target data are compared and judged pairwise to clarify the relative importance and establish the pairwise judgment matrix A, as shown in formula (1):

[0089]

[0090] Among them, a ij It is the ratio of the relative importance scale values ​​between two target data risk indicators.

[0091] According to the judgment matrix obtained above, the subjective weight calculation is performed on the target data risk indicators in the matrix. The main process is as follows:

[0092] Normalize the matrix:

[0093]

[0094] Where n is the order of the judgment matrix, It is the ratio of the relative importance scale values ​​between the two target data risk indicators after normalization.

[0095] Summation of judgment matrix A:

[0096]

[0097] The sum of the normalized matrix A:

[0098]

[0099] Maximum eigenvalue calculation:

[0100]

[0101] Where W = (W1, W2, ..., W n ) T , also known as the eigenvector of the judgment matrix, because After normalization, each element W1, W2, ..., W n is the subjective weight value of the level to which multiple target data risk indicators belong, λ max is the largest eigenvalue of the judgment matrix.

[0102] Calculate the consistency index:

[0103]

[0104] Calculate the CR consistency ratio by comparing it with RI:

[0105]

[0106] In the AHP evaluation method, the random consistency index RI is introduced to solve the deviation problem of multi-order judgment matrix. According to the order n of the judgment matrix, the corresponding value of R is obtained.

[0107] Consistency check judgment:

[0108] The consistency ratio CR obtained by the above formula is used for judgment. If CR is less than a preset value, the judgment matrix is ​​consistent, that is, the judgment matrix has no deviation problem and is a valid matrix.

[0109] Among them, RI k Indicates that the B layer is connected to the A k The average random consistency index of the single sort of related factors, CI k Represents the corresponding single sort consistency index. The consistency check of the total sort is also obtained by comparing CR with 0.1. If CR < 0.1, it is considered that the consistency check has passed and the result is acceptable.

[0110] In summary, through the above calculation process, the subjective weights of the target data risk indicators at their respective levels can be obtained.

[0111] Since CRITIC uses the comparative strength and conflict degree of evaluation indicators to measure the objective weight of indicators, it can eliminate the influence of some indicators with strong correlation and reduce the information overlap between indicators. The main process of using CRITIC to calculate the objective weight of risk indicators is as follows:

[0112] The data of the comparison matrix is ​​standardized, which is divided into forward and reverse processing: in forward processing, the larger the value, the better; in reverse processing, the smaller the value, the better. Make specific choices based on the scoring criteria. The processing process is as follows:

[0113] Positive:

[0114]

[0115] Among them, x ij is the weight evaluation result i corresponding to the target data risk index j, maxx j is the maximum value of the weight evaluation of the m weight evaluation results corresponding to the target data risk index i, minx j It is the minimum value of the weight evaluation of the m weight evaluation results corresponding to the target data risk indicator i.

[0116] Reversal:

[0117]

[0118] Calculate the standard deviation of the evaluation index: the larger the standard deviation, the greater its own difference, the greater the amount of information contained, and the greater the weight. The specific calculation formula is as follows:

[0119]

[0120] in, It is the average value of the weighted evaluation of the m weighted evaluation results corresponding to the target data risk indicator i.

[0121] Calculate the correlation coefficient of the evaluation indicator:

[0122]

[0123] Calculate objective weights:

[0124] Index X j The corresponding objective weights are expressed as follows:

[0125]

[0126] C jis the importance index of each data risk indicator, where:

[0127]

[0128] In summary, through the above calculation process, the objective weights of the target data risk indicators at their respective levels can be obtained.

[0129] A correlation analysis is performed on the weights, that is, to determine whether the P value obtained by the significance test of the subjective weights obtained by the hierarchical analysis method and the objective weights obtained by the CRITIC method is greater than 0.05. If P is greater than 0.05, it means that the two are not significantly correlated, that is, the contents reflected by the two weighting methods are independent of each other and are not repetitive.

[0130] Furthermore, according to the above calculation, the subjective weight values ​​and objective weight values ​​of multiple target data risk indicators at their respective levels have been obtained. The calculation process of the total hierarchical ranking is to calculate the single ranking results from top to bottom. Assuming that layer A is the upper layer of layer B, the subjective weight values ​​or objective weight values ​​of the m target data risk indicators at layer A at their respective levels are a1, a2, ..., a m , in layer B relative to A k The subjective weight value or objective weight value is b 1k ,b 2k ,...,b nk (When b i With A k When there is no relationship, b ik =0) includes the following main calculation process:

[0131] The ranking vector of each factor in the B layer relative to the total target, that is, the subjective weight value or objective weight value of each target data risk indicator:

[0132]

[0133] Calculate the consistency ratio of the total hierarchical order and determine:

[0134]

[0135] Calculate the comprehensive weight:

[0136] According to the weighting results of the evaluation indicators by the AHP method and the CRITIC method, the comprehensive weight value is calculated. The comprehensive weight calculation method is as follows:

[0137]

[0138] Among them, ω AHP-CRITIC is the comprehensive weight value of the target data risk index i, ω i-AHP is the subjective weight value of the target data risk index i, ωi-CRITIC is the objective weight value of the target data risk indicator i, wherein the subjective weight value of the target data risk indicator i is the product of the subjective weight value of the target data risk indicator i at the level to which it belongs and the subjective weight value of at least one target data risk indicator of the target data risk indicator i at the level to which it belongs, and the objective weight value of the target data risk indicator i is the product of the objective weight value of the target data risk indicator i at the level to which it belongs and the objective weight value of at least one target data risk indicator of the target data risk indicator i at the level to which it belongs.

[0139] The subjective weight value of the target data risk indicator, the subjective weight value of the target data risk indicator at the level to which it belongs, and the subjective weight value of at least one target data risk indicator to which the target data risk indicator belongs at the level to which it belongs are shown in Table 2: The target medical data risk assessment indicator system includes two levels of target data risk indicators, wherein the first-level indicator is the target data risk indicator at the first level, and the second-level indicator is the target data risk indicator at the last level.

[0140]

[0141] Table 2

[0142] The objective weight value of the target data risk indicator, the objective weight value of the target data risk indicator at the level to which it belongs, and the objective weight value of at least one target data risk indicator to which the target data risk indicator belongs at the level to which it belongs are shown in Table 3: The target medical data risk assessment indicator system includes two levels of target data risk indicators, wherein the first-level indicator is the target data risk indicator at the first level, and the second-level indicator is the target data risk indicator at the last level.

[0143]

[0144] Table 3

[0145] Furthermore, by combining the subjective weight values ​​of the multiple target data risk indicators at their respective levels shown in Table 2 and Table 3 and the objective weight values ​​of the multiple target data risk indicators at their respective levels, the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels are determined as shown in Table 4.

[0146]

[0147] Table 4

[0148] In some embodiments, each evaluation content includes multiple evaluation levels, uncertainties corresponding to each of the multiple evaluation levels, and membership function centers corresponding to each of the multiple evaluation levels.

[0149] Based on the initial membership function value of each first target data risk indicator at the final level under multiple evaluation contents and different evaluation levels, the target membership function values ​​of multiple first target data risk indicators at different evaluation levels are determined, including: based on the multiple evaluation levels corresponding to the multiple evaluation contents corresponding to each first target data risk indicator, the uncertainties corresponding to the multiple evaluation levels, and the membership function centers corresponding to the multiple evaluation levels, the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents is calculated; based on the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents, the conflict coefficient and probability distance of each first target data risk indicator between different evaluation contents are determined; based on the conflict coefficient and probability distance of each first target data risk indicator between different evaluation contents, the target membership function value of each first target data risk indicator at different evaluation levels is calculated.

[0150] Among them, the first target data risk indicator is the target data risk indicator at the last level in the target medical data risk assessment indicator system.

[0151] Since fuzzy set theory can handle the fuzziness of subjective data, this model introduces fuzzy set theory, brings the evaluation content into the membership function, and conducts quantitative analysis.

[0152] Since the fuzzy operation of Gaussian membership function has good anti-interference ability, this model uses Gaussian membership function to represent the fuzziness of relevant risk factors in medical data risk assessment. The Gaussian membership function is:

[0153]

[0154] Among them, y is the membership function value, x is the quantitative value corresponding to the evaluation level, μ is the center of the function, and σ is the uncertainty corresponding to the evaluation level.

[0155] Assuming that the assessment content uses five levels of risk description, it is divided into V1 (high), V2 (higher), V3 (medium), V4 (lower), and V5 (low) according to the severity of the risk. The evaluation set of the medical data risk assessment indicator system is shown in Table 5.

[0156] Assessment level Uncertainty Membership function center <![CDATA[V1]]> 0.9 1 <![CDATA[V2]]> 0.7 0.75 <![CDATA[V3]]> 0.5 0.5 <![CDATA[V4]]> 0.3 0.25 <![CDATA[V5]]> 0.1 0

[0157] Table 5

[0158] Specifically, Table 6 is a certain evaluation content for the first target risk indicator.

[0159]

[0160]

[0161] Table 6

[0162] According to the Gaussian membership function, the degree to which the first target data risk indicator belongs to each risk level in multiple evaluation contents is determined, and the center of the Gaussian membership function corresponding to the risk level is substituted into it to obtain the membership function.

[0163] The membership matrix of experts for each indicator in the evaluation index system is obtained. Taking indicator R as an example, the membership matrix is:

[0164]

[0165] Substitute the data in Table 6 into the fuzzy set membership function and perform matrix row normalization to obtain the basic probability distribution of the evaluation content corresponding to Table 6 for the first target data risk indicator, that is, the initial membership function value of each first target data risk indicator under different evaluation contents and different evaluation levels is shown in Table 7.

[0166]

[0167] Table 7

[0168] Assume that the two evaluation contents F1 and F2 corresponding to the first target data risk indicator have the corresponding basic probability distributions m1 and m2, respectively, and the focal elements are A i , B j , the focal element usually refers to the key point or center point in the calculation, which is used to analyze the relationship between evidence. In the calculation of the conflict coefficient, the focal element may be the core parameter or standard for measuring the conflict of evidence. Then the DS evidence synthesis rule under two pieces of evidence is:

[0169]

[0170] Among them, K is the conflict coefficient, which indicates the degree of conflict between the two evaluation contents.

[0171] By analogy, the DS evidence theory can also integrate multiple pieces of evidence. However, when integrating highly conflicting evidence, results that are contrary to reality often occur, which seriously affects judgment.

[0172] In order to better reduce the uncertainty brought by Basic Probability Assignment (BPA), reliability decision is made and BPA is transformed into Pignistic probability. The basic principle is to not change the reliability of a single subset and to evenly distribute the reliability of multiple subsets to a single subset.

[0173] Assuming the basic probability distribution function of the evaluation content corresponding to the first target data risk indicator is: m(A), then a single subset element θ of the evaluation content i The probability conversion function is:

[0174]

[0175] Here, |A| represents the cardinality of A.

[0176] Based on the probability transformation, the concept of Pignistic probability distance is proposed to characterize the degree of conflict between evidences.

[0177] Assume that the two evaluation contents corresponding to the first target data risk indicator correspond to the basic probability distribution functions: m1(A) and m2(A), and the corresponding probability conversion functions BetP m1 (A) and BetP m2 (A), then the probability distance between m1(A) and m2(A) is:

[0178]

[0179] in, is the difference in game probability between two subsets of evidence, and the maximum game probability difference between the subsets in the evidence is taken as the probability distance between m1(A) and m2(A).

[0180] Furthermore, based on the conflict coefficient and probability distance between different evaluation contents of each first target data risk indicator, the target membership function value of each first target data risk indicator at different evaluation levels can be calculated.

[0181] In some embodiments, based on the conflict coefficient and probability distance between different evaluation contents of each first target data risk indicator, the target membership function value of each first target data risk indicator at different evaluation levels is calculated, including: establishing a correlation matrix between the conflict coefficient and the probability distance according to the corresponding relationship between the conflict coefficient and the probability distance between different evaluation contents of each first target data risk indicator; determining the weights corresponding to the initial membership function values ​​of each first target data risk indicator at different evaluation levels under different evaluation contents based on the correlation matrix; determining the updated membership function values ​​of each first target data risk indicator at different evaluation levels under different evaluation contents based on the weights corresponding to the initial membership function values ​​of each first target data risk indicator at different evaluation levels under different evaluation contents and the initial membership function values ​​of each first target data risk indicator at different evaluation levels under different evaluation contents; determining the target membership function values ​​of each first target data risk indicator at different evaluation levels based on the updated membership function values ​​of each first target data risk indicator at different evaluation levels under different evaluation contents and the conflict coefficients of each first target data risk indicator between different evaluation contents.

[0182] The synthesis rules of the improved evidence synthesis theory based on Pignistic probability distance and conflict coefficient K binary group can effectively fuse high-conflict evidence and improve the reliability of the evaluation. The specific steps are as follows:

[0183] Calculate the conflict coefficient K between different evaluation contents of the first target data risk indicator ij , construct the evidence conflict coefficient matrix M k :

[0184]

[0185] Calculate the Pignistic probability distance between different evaluation contents of the first target data risk indicator Construct Pignistic probability distance matrix M f :

[0186]

[0187] The conflict coefficient K calculated in the above steps ij And the Pignistic probability distance corresponds one to one, forming several binary groups representing the conflicts between different evaluation contents of the first target data risk indicator Then the conflicting binary association matrix M kf for:

[0188]

[0189] The conflict coefficient K ijThe two-tuple consisting of the Pignistic probability distance is mapped as a point in the first quadrant to the rectangular coordinate system, and the distance from the point to the bisector of the second and fourth quadrants is defined as the conflict distance d between evidences ij , conflict coefficient K ij And the maximum distance from the point composed of Pignistic probability distance to the bisector of the second and fourth quadrants is Set the conflict distance d ij The normalization process is:

[0190]

[0191] This gives the conflict distance matrix:

[0192]

[0193] The conflict degree between different evaluation contents of the first target data risk indicator can be measured by similarity. The higher the similarity between two pieces of evidence, the smaller the conflict. The similarity between the pieces of evidence is:

[0194] s ij =1-d′ ij (twenty one)

[0195] From this we can get the similarity matrix between evidences:

[0196]

[0197] Support is the support of other evidences for a piece of evidence m. i The degree of confidence, evidence m i The support is:

[0198]

[0199] When the support of different evaluation contents of the first target data risk indicator is higher, the credibility of different evaluation contents of the first target data risk indicator is higher. The support of different evaluation contents of the first target data risk indicator is normalized, and the credibility of the evidence is obtained as follows:

[0200]

[0201] The credibility crd(m i ) as evidence i The weights of the evidence are weighted averaged, and the new evidence after weighted average is synthesized n-1 times using the DS synthesis rule.

[0202] According to the evaluation index weights, the mass functions of all indicators in the evaluation system are synthesized:

[0203]

[0204] Among them, n i For indicator A i The number of secondary indicators contained in w ij is the weight of the secondary index j under the primary index i, m ij (A) is the mass function value of each secondary indicator converted from different evaluation contents of the first target data risk indicator.

[0205] In some embodiments, the data risk level of the target medical data risk assessment indicator system is determined based on the comprehensive weight values ​​of multiple target data risk indicators at their respective levels and the target membership function values ​​of multiple first target data risk indicators at different assessment levels, including: determining the target comprehensive weight value corresponding to each first target data risk indicator based on the comprehensive weight values ​​of multiple target data risk indicators at their respective levels; determining the comprehensive membership function value of each first target data risk indicator at different assessment levels based on the target comprehensive weight value corresponding to each first target data risk indicator and the target membership function value of each first target data risk indicator at different assessment levels; determining the comprehensive membership function value of each second target data risk indicator at different assessment levels based on the comprehensive membership function value of each first target data risk indicator at different assessment levels; and determining the assessment level with the highest comprehensive membership function value corresponding to each second target data risk indicator as the data risk level corresponding to each second target data risk indicator.

[0206] Among them, the second target data risk indicator is a target data risk indicator at the first level. The embodiment of the present application determines the data risk level of the target medical data risk assessment indicator system based on the data risk level corresponding to the target data risk indicator at the first level.

[0207] In some embodiments, the target comprehensive weight value corresponding to each first target data risk indicator can be determined based on the comprehensive weight values ​​of multiple target data risk indicators at their respective levels, wherein the comprehensive weight value can be determined based on the subjective weight value of the first target data risk indicator at the level to which it belongs and the objective weight value of the target data risk indicator at the level to which it belongs. The target comprehensive weight value can be the product of the comprehensive weight value of the first target data risk indicator at the level to which it belongs and the comprehensive weight value of at least one target data risk indicator to which the first target data risk indicator belongs at the level to which it belongs.

[0208] That is, the target comprehensive weight value can be obtained by first determining the comprehensive weight values ​​of multiple target data risk indicators at their respective levels, and then determining the target comprehensive weight value based on the product of the comprehensive weight value of the first target data risk indicator at its level and the comprehensive weight value of at least one target data risk indicator to which the first target data risk indicator belongs at its level; or it can be obtained by first determining the subjective weight values ​​and objective weight values ​​of multiple target data risk indicators at their respective levels, then determining the subjective weight values ​​and objective weight values ​​corresponding to each of the multiple target data risk indicators based on the subjective weight values ​​and objective weight values ​​of the multiple target data risk indicators at their respective levels, and further determining the target comprehensive weight value of the first target data risk indicator based on the subjective weight value and objective weight value of the first target data risk indicator.

[0209] Furthermore, the mass functions of the first target data risk indicators can be weighted by the target comprehensive weight value of the first target data risk indicator, and the risk level can be determined according to the principle of maximum membership to achieve medical data risk assessment.

[0210] In some embodiments, the target membership function values ​​of each first target data risk indicator at different evaluation levels are shown in Table 8:

[0211]

[0212] Table 8

[0213] According to the comprehensive weight values ​​of multiple target data risk indicators at their respective levels shown in Table 4, combined with the data in Table 8, the mass function is further synthesized to determine the risk level. The synthesis results are shown in Table 9.

[0214]

[0215] Table 9

[0216] Combining the data in Table 4, Table 8 and Table 9, it can be seen that the synthesis process of the mass function is to weight the mass function values ​​corresponding to each secondary indicator and the target comprehensive weight values ​​corresponding to each secondary indicator to obtain the comprehensive membership function values ​​of each secondary indicator at different evaluation levels, and then merge the comprehensive membership function values ​​of different evaluation levels corresponding to each secondary indicator to obtain the comprehensive membership function values ​​of different evaluation levels under the corresponding primary indicator.

[0217] Combined with Table 9, it can be seen that the risk level with the highest comprehensive membership function value under each first-level indicator can be determined, so that the data risk level corresponding to each first-level indicator can be determined. For example, the risk level corresponding to the first-level indicator R1 is V3, that is, the risk level of the first-level indicator R1 is medium. This shows that there is a certain degree of vulnerability in the system in terms of medical data privacy protection, which needs to be paid attention to and corresponding protective measures should be taken. The remaining risks are all assessed as low risks, which shows that most of the key security areas of the hospital information system are in good condition, and the existing security measures have largely guaranteed the security and integrity of the data.

[0218] In addition, the risk level with the highest comprehensive membership function value can be determined as the risk level of the target medical data risk assessment index system. According to the data in Table 10, the risk levels are ranked as follows: lower > medium > low > higher > high. The risk level with the highest comprehensive membership function value is V4, which means the risk is relatively low. It can be seen that the medical data risk level of the target medical data risk assessment index system is low, which is consistent with the actual system situation, confirming that the target medical data risk assessment index system as a whole performs relatively well in medical data risk management.

[0219] The present application also provides a risk assessment device, which is used to execute the risk assessment method provided in any of the above embodiments. Figure 2 As shown, the device includes: a determination module 201 and a calculation module 202.

[0220] A determination module 201 is used to determine a plurality of data risk indicators based on the data security text, a preset data risk assessment indicator system and medical data characteristics, wherein the medical data characteristics include at least one of medical network environment characteristics and medical data characteristics;

[0221] The determination module 201 is further configured to determine, based on the necessity of existence of the multiple data risk indicators, multiple target data risk indicators existing in the target medical data risk assessment indicator system and the levels to which the multiple target data risk indicators belong;

[0222] A calculation module 202 is used to calculate the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels;

[0223] The determination module 201 is further configured to determine target membership function values ​​of multiple first target data risk indicators at different evaluation levels based on the initial membership function values ​​of each first target data risk indicator at the final level at different evaluation levels under multiple evaluation contents;

[0224] The determination module 201 is also used to determine the data risk level of the target medical data risk assessment indicator system based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels and the target membership function values ​​of the multiple first target data risk indicators at different assessment levels.

[0225] In some embodiments, the data security text includes a general data security text and a medical data security text, and the determination module 201 is specifically used to:

[0226] Determine at least one general data risk indicator at the first level based on the data risk indicators of the general data security text and the data risk indicators in the preset data risk assessment indicator system;

[0227] Determining at least one medical data risk indicator at a first level based on the data risk indicators and medical data characteristics of the medical data security text;

[0228] Traversing the data risk indicators of the general data security text and the data risk indicators in the preset data risk assessment indicator system, determining at least one general data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level;

[0229] Traversing the data risk indicators of the medical data security text, and determining at least one medical data risk indicator at each level corresponding to each of the at least one medical data risk indicator at the first level;

[0230] Based on the at least one general data risk indicator at the first level, the at least one general data risk indicator at the first level, the at least one medical data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level, and the at least one medical data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level, a plurality of data risk indicators are composed.

[0231] In some embodiments, the determination module 201 is further specifically configured to:

[0232] Acquire a plurality of existence weights corresponding to each of the at least one data risk indicator at the first level, wherein the existence weights are used to evaluate the probability of each data risk indicator existing in the target medical data risk assessment indicator system;

[0233] Calculating the necessity of existence of the at least one data risk indicator at the first level based on a plurality of existence weights corresponding to each of the at least one data risk indicator at the first level;

[0234] Determine the data risk indicator whose existence necessity is greater than the first necessity threshold as the second target data risk indicator at the first level, and obtain multiple second target data risk indicators;

[0235] Traversing the data risk indicators at multiple levels corresponding to each of the multiple second target data risk indicators in hierarchical order, and obtaining the existence necessity degree of the data risk indicators at multiple levels corresponding to each of the multiple second target data risk indicators;

[0236] Determine the data risk indicators whose existence necessity is greater than the second existence necessity threshold as third target data risk indicators at each level, and obtain multiple third target data risk indicators;

[0237] A plurality of target data risk indicators are composed based on the plurality of second target data risk indicators and the plurality of third target data risk indicators.

[0238] In some embodiments, the calculation module 202 is specifically configured to:

[0239] Determining subjective weight values ​​of the plurality of target data risk indicators at their respective levels based on the importance scale values ​​corresponding to the plurality of target data risk indicators;

[0240] Determining objective weight values ​​of the multiple target data risk indicators at their respective levels based on the assessment weight values ​​corresponding to the multiple target data risk indicators;

[0241] Based on the subjective weight values ​​of the multiple target data risk indicators at the levels to which they belong and the objective weight values ​​of the multiple target data risk indicators at the levels to which they belong, the comprehensive weight values ​​of the multiple target data risk indicators at the levels to which they belong are determined.

[0242] In some embodiments, each evaluation content includes multiple evaluation levels, uncertainties corresponding to each of the multiple evaluation levels, and membership function centers corresponding to each of the multiple evaluation levels. The determination module 201 is further specifically used for:

[0243] Based on the multiple evaluation levels corresponding to the multiple evaluation contents corresponding to each of the first target data risk indicators, the uncertainties corresponding to the multiple evaluation levels, and the membership function centers corresponding to the multiple evaluation levels, calculating the initial membership function values ​​of each of the first target data risk indicators at different evaluation levels under different evaluation contents;

[0244] Determine the conflict coefficient and probability distance between different evaluation contents of each first target data risk indicator based on the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents;

[0245] Based on the conflict coefficient and probability distance between different evaluation contents of each of the first target data risk indicators, the target membership function value of each of the first target data risk indicators at different evaluation levels is calculated.

[0246] In some embodiments, the determination module 201 is further specifically configured to:

[0247] According to the corresponding relationship between the conflict coefficient and the probability distance between different evaluation contents of each first target data risk indicator, a correlation matrix between the conflict coefficient and the probability distance is established;

[0248] Determine, based on the association matrix, the weight corresponding to the initial membership function value of each first target data risk indicator under different evaluation contents and different evaluation levels;

[0249] Determine the updated membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents based on the weight corresponding to the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents and the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents;

[0250] Based on the updated membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents and the conflict coefficient between each first target data risk indicator at different evaluation contents, the target membership function value of each first target data risk indicator at different evaluation levels is determined.

[0251] In some embodiments, the determination module 201 is further specifically configured to:

[0252] Determine a target comprehensive weight value corresponding to each of the first target data risk indicators based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels;

[0253] Determine the comprehensive membership function value of each first target data risk indicator at different evaluation levels based on the target comprehensive weight value corresponding to each first target data risk indicator and the target membership function value of each first target data risk indicator at different evaluation levels;

[0254] Determining the comprehensive membership function value of each of the second target data risk indicators at different evaluation levels based on the comprehensive membership function value of each of the first target data risk indicators at different evaluation levels;

[0255] The evaluation level with the highest comprehensive membership function value corresponding to each of the second target data risk indicators is determined as the data risk level corresponding to each of the second target data risk indicators.

[0256] The present application also provides an electronic device to perform the above risk assessment method. Figure 3 It shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 3 As shown, the electronic device 7 includes: a processor 700, a memory 701, a bus 702 and a communication interface 703, and the processor 700, the communication interface 703 and the memory 701 are connected via the bus 702; the memory 701 stores a computer program that can be run on the processor 700, and when the processor 700 runs the computer program, it executes the risk assessment method provided in any of the aforementioned embodiments of the present application.

[0257] The memory 701 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 703 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0258] The bus 702 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 701 is used to store programs, and the processor 700 executes the program after receiving the execution instruction. The risk assessment method disclosed in any implementation of the above-mentioned embodiment of the present application may be applied to the processor 700, or implemented by the processor 700.

[0259] The processor 700 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 700. The above processor 700 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 701, and the processor 700 reads the information in the memory 701 and completes the steps of the above method in combination with its hardware.

[0260] The electronic device provided in the embodiment of the present application and the risk assessment method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0261] The present application also provides a computer-readable storage medium corresponding to the risk assessment method provided in the above embodiment. Figure 4 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the risk assessment method provided by any of the aforementioned embodiments will be executed.

[0262] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0263] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the risk assessment method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0264] It should be noted that:

[0265] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0266] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0267] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A risk assessment method, characterized in that: include: Determine multiple data risk indicators based on the data security text, the preset data risk assessment indicator system and medical data characteristics, wherein the medical data characteristics include at least one of medical network environment characteristics and medical data characteristics; Based on the necessity of existence of the multiple data risk indicators, determining multiple target data risk indicators existing in the target medical data risk assessment indicator system and the levels to which the multiple target data risk indicators belong; Calculating the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels; Determine target membership function values ​​of multiple first target data risk indicators at different evaluation levels based on the initial membership function values ​​of each first target data risk indicator at the final level at different evaluation levels under multiple evaluation contents; The data risk level of the target medical data risk assessment indicator system is determined based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels and the target membership function values ​​of the multiple first target data risk indicators at different assessment levels.

2. The method according to claim 1, characterized in that The data security text includes a general data security text and a medical data security text. Based on the data security text, a preset data risk assessment indicator system and medical data characteristics, a plurality of data risk indicators are determined, including: Determine at least one general data risk indicator at the first level based on the data risk indicators of the general data security text and the data risk indicators in the preset data risk assessment indicator system; Determining at least one medical data risk indicator at a first level based on the data risk indicators and medical data characteristics of the medical data security text; Traversing the data risk indicators of the general data security text and the data risk indicators in the preset data risk assessment indicator system, determining at least one general data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level; Traversing the data risk indicators of the medical data security text, and determining at least one medical data risk indicator at each level corresponding to each of the at least one medical data risk indicator at the first level; Based on the at least one general data risk indicator at the first level, the at least one general data risk indicator at the first level, the at least one medical data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level, and the at least one medical data risk indicator at each level corresponding to each of the at least one general data risk indicator at the first level, a plurality of data risk indicators are composed.

3. The method according to claim 2, characterized in that The determining, based on the necessity of existence of the multiple data risk indicators, multiple target data risk indicators existing in the target medical data risk assessment indicator system and the levels to which the multiple target data risk indicators belong includes: Acquire a plurality of existence weights corresponding to each of the at least one data risk indicator at the first level, wherein the existence weights are used to evaluate the probability of each data risk indicator existing in the target medical data risk assessment indicator system; Calculating the necessity of existence of the at least one data risk indicator at the first level based on a plurality of existence weights corresponding to each of the at least one data risk indicator at the first level; Determine the data risk indicator whose existence necessity is greater than the first necessity threshold as the second target data risk indicator at the first level, and obtain multiple second target data risk indicators; Traversing the data risk indicators at multiple levels corresponding to each of the multiple second target data risk indicators in hierarchical order, and obtaining the existence necessity degree of the data risk indicators at multiple levels corresponding to each of the multiple second target data risk indicators; Determine the data risk indicators whose existence necessity is greater than the second existence necessity threshold as third target data risk indicators at each level, and obtain multiple third target data risk indicators; A plurality of target data risk indicators are composed based on the plurality of second target data risk indicators and the plurality of third target data risk indicators.

4. The method according to claim 1, characterized in that The calculating of the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels includes: Determining subjective weight values ​​of the plurality of target data risk indicators at their respective levels based on the importance scale values ​​corresponding to the plurality of target data risk indicators; Determining objective weight values ​​of the multiple target data risk indicators at their respective levels based on the assessment weight values ​​corresponding to the multiple target data risk indicators; Based on the subjective weight values ​​of the multiple target data risk indicators at the levels to which they belong and the objective weight values ​​of the multiple target data risk indicators at the levels to which they belong, the comprehensive weight values ​​of the multiple target data risk indicators at the levels to which they belong are determined.

5. The method according to any one of claims 1 to 4, characterized in that: Each evaluation content includes multiple evaluation levels, uncertainties corresponding to each of the multiple evaluation levels, and membership function centers corresponding to each of the multiple evaluation levels. The determining of target membership function values ​​of multiple first target data risk indicators at different evaluation levels based on initial membership function values ​​of each first target data risk indicator at the final level at different evaluation levels under multiple evaluation contents includes: Based on the multiple evaluation levels corresponding to the multiple evaluation contents corresponding to each of the first target data risk indicators, the uncertainties corresponding to the multiple evaluation levels, and the membership function centers corresponding to the multiple evaluation levels, calculating the initial membership function values ​​of each of the first target data risk indicators at different evaluation levels under different evaluation contents; Determine the conflict coefficient and probability distance between different evaluation contents of each first target data risk indicator based on the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents; Based on the conflict coefficient and probability distance between different evaluation contents of each first target data risk indicator, the target membership function value of each first target data risk indicator at different evaluation levels is calculated.

6. The method according to claim 5, characterized in that The calculating the target membership function value of each first target data risk indicator at different evaluation levels based on the conflict coefficient and probability distance between different evaluation contents of each first target data risk indicator includes: According to the corresponding relationship between the conflict coefficient and the probability distance between different evaluation contents of each first target data risk indicator, a correlation matrix between the conflict coefficient and the probability distance is established; Determine, based on the association matrix, the weight corresponding to the initial membership function value of each first target data risk indicator under different evaluation contents and different evaluation levels; Determine the updated membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents based on the weight corresponding to the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents and the initial membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents; Based on the updated membership function value of each first target data risk indicator at different evaluation levels under different evaluation contents and the conflict coefficient between each first target data risk indicator at different evaluation contents, the target membership function value of each first target data risk indicator at different evaluation levels is determined.

7. The method according to claim 3, characterized in that The step of determining the data risk level of the target medical data risk assessment indicator system based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels and the target membership function values ​​of the multiple first target data risk indicators at different assessment levels includes: Determine a target comprehensive weight value corresponding to each of the first target data risk indicators based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels; Determine the comprehensive membership function value of each first target data risk indicator at different evaluation levels based on the target comprehensive weight value corresponding to each first target data risk indicator and the target membership function value of each first target data risk indicator at different evaluation levels; Determining the comprehensive membership function value of each of the second target data risk indicators at different evaluation levels based on the comprehensive membership function value of each of the first target data risk indicators at different evaluation levels; The evaluation level with the highest comprehensive membership function value corresponding to each of the second target data risk indicators is determined as the data risk level corresponding to each of the second target data risk indicators.

8. A risk assessment device, characterized in that: include: A determination module, for determining a plurality of data risk indicators based on a data security text, a preset data risk assessment indicator system and medical data characteristics, wherein the medical data characteristics include at least one of medical network environment characteristics and medical data characteristics; The determination module is further used to determine multiple target data risk indicators existing in the target medical data risk assessment indicator system and the levels to which the multiple target data risk indicators belong based on the necessity of existence of the multiple data risk indicators; A calculation module, used to calculate the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels; The determination module is further used to determine target membership function values ​​of multiple first target data risk indicators at different evaluation levels based on the initial membership function value of each first target data risk indicator at the final level at different evaluation levels under multiple evaluation contents; The determination module is also used to determine the data risk level of the target medical data risk assessment indicator system based on the comprehensive weight values ​​of the multiple target data risk indicators at their respective levels and the target membership function values ​​of the multiple first target data risk indicators at different assessment levels.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor runs the computer program to implement the method according to any one of claims 1 to 7.

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

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