Cloud data security management method and system based on multi-tenant architecture

Abstract

The present invention discloses a cloud data security management method and system based on a multi-tenant architecture, which relates to data management related fields. The method includes: obtaining tenant data; reading activity logs, performing activity evaluation, establishing activity evaluation results, performing storage space division, and establishing storage space division results; reading account data, sending to a tenant storage level reconstruction model, and generating a reconstructed data level; performing account data importance evaluation, and generating a first important evaluation result; performing level analysis, reconstructing level standards, performing data importance evaluation, and generating a second important evaluation result; performing storage constraints, calling storage space division results, and executing partition storage. The method solves the technical problem of high security risks in existing multi-tenant architecture cloud data security management, and achieves the technical effect of reducing security risks between tenant data and protecting the security and privacy of tenant data.

Description

Technical Field

[0001] The present application relates to data management related fields, and in particular to a cloud data security management method and system based on a multi-tenant architecture. Background Art

[0002] With the rapid development of cloud computing technology, multi-tenant architecture has become an architectural model widely adopted by cloud service providers. Multi-tenant architecture allows multiple tenants to share the same set of hardware and software resources, thereby improving resource utilization and reducing costs. However, this architectural model also brings challenges in data security management. Since the data of different tenants may overlap physically or logically, how to ensure the security, integrity and privacy of each tenant's data has become an urgent problem to be solved. In the existing multi-tenant architecture cloud data security management method, a unified security policy is usually used to manage all tenant data. Since tenant data may overlap physically or logically, this method may threaten the data security of other tenants once a security incident occurs in the data of a tenant.

[0003] Among the current related technologies, multi-tenant architecture cloud data security management has technical issues with high security risks. Summary of the invention

[0004] This application provides a cloud data security management method and system based on a multi-tenant architecture, and adopts technical means such as obtaining tenant data, performing activity evaluation, dividing storage space, performing importance evaluation, and performing storage constraints on account data, thereby achieving the technical effect of reducing security risks between tenant data and protecting the security and privacy of tenant data.

[0005] This application provides a cloud data security management method based on a multi-tenant architecture, including:

[0006] Obtain tenant data of the architecture, the tenant data including the number of tenants and tenant authority levels;

[0007] Reading the activity log of the architecture, evaluating the activity of each tenant based on the activity log, establishing an activity evaluation result, dividing the storage space of the architecture with the activity evaluation result and the tenant data, and establishing a storage space division result, wherein the storage space division result includes a shared storage space division result and a physically isolated storage space division result;

[0008] Read the account data corresponding to each tenant, send the account data and the tenant authority level to the tenant storage level reconstruction model, and generate a reconstructed data level of the account data;

[0009] Performing an importance evaluation of the account data based on the reconstructed data level to generate a first importance evaluation result;

[0010] Performing a hierarchical analysis on all account data, reconstructing the hierarchical standards of the architecture, and performing a data importance evaluation on each account data based on the hierarchical standards to generate a second importance evaluation result;

[0011] The storage constraints of the account data are performed by using the first important evaluation result and the second important evaluation result, and the storage space division result is called to execute the partition storage of the shared storage space division result and the physically isolated storage space division result.

[0012] In a possible implementation, the storage space of the architecture is divided based on the activity evaluation result and the tenant data, a storage space division result is established, and the following processing is performed:

[0013] Calling tenant permissions in the tenant data, performing permission rating based on the tenant permissions, and generating a fuzzy division ratio of a shared storage space division result and a physically isolated storage space division result according to the permission rating result;

[0014] Based on the activity evaluation result, active storage data is called to establish a level data storage mean;

[0015] The fuzzy partition ratio is adjusted by the hierarchical data storage mean value, and the storage space division is completed according to the adjustment result.

[0016] In a possible implementation, the following processing is performed:

[0017] Performing permission granularity division on the tenant permission level, establishing permission granularity division results, and configuring a mapping verification strategy with the permission granularity division results, wherein the mapping verification strategy includes a trust authentication verification strategy and a location scenario verification strategy;

[0018] When a tenant logs in to an account, a multi-dimensional data trust verification is performed to generate a trust verification result.

[0019] Acquire the location scenario data of the tenant, and perform mapping verification strategy matching with the trust verification result and the location scenario data;

[0020] Configure the granular permissions for the current tenant login based on the mapping verification policy matching results.

[0021] In a possible implementation, when the tenant performs account login, the multi-dimensional data trust verification of the login is performed, a trust verification result is generated, and the following processing is performed:

[0022] Establishing a historical interaction success rate evaluation index, performing trust authentication on the account through the historical interaction success rate evaluation index, and generating a first-dimensional trust verification result;

[0023] Obtaining current login authentication data of the account, performing login trusted authentication based on the login authentication data, and generating a second-dimensional trust verification result;

[0024] Establishing an authentication window, authenticating the behavior and activities of the account in the window through the authentication window, and establishing a third-dimensional trust verification result;

[0025] A trust weight factor is established, and weighted calculation of the first-dimensional trust verification result, the second-dimensional trust verification result, and the third-dimensional trust verification result is performed using the trust weight factor, and multi-dimensional data trust verification is completed according to the weighted calculation result.

[0026] In a possible implementation, the following processing is performed:

[0027] Establishing an activity feature set for the tenant based on the authentication window;

[0028] After the tenant logs in through the corresponding account, the tenant's time series activity anomaly monitoring is performed based on the activity feature set to generate a time series anomaly value;

[0029] Dynamic fine-tuning granularity permissions are established through the time series anomaly values, and granularity permissions for the current tenant login are updated through the dynamic fine-tuning granularity permissions.

[0030] In a possible implementation, the following processing is performed:

[0031] Determine whether the current tenant's usage permissions exceed the updated granular permissions;

[0032] If it exceeds, an auxiliary certification application will be generated;

[0033] Perform dual authentication of account login and auxiliary authentication of the associated tenant through the auxiliary authentication application;

[0034] Update the granular permissions again based on the two-factor authentication results.

[0035] In a possible implementation, the location scenario data of the tenant is obtained, a mapping verification strategy is matched by the trust verification result and the location scenario data, and the following processing is performed:

[0036] Conduct login location scenario statistics for tenants and establish location scenario statistics results with login trust identifiers;

[0037] Clustering the location scenario statistics results into location scenarios, and generating an initial trust strategy according to the cluster size of the location scenarios;

[0038] Obtaining a trust accumulation value of the location scenario clustering result, compensating the initial trust strategy by the trust accumulation value, and establishing a location scenario verification strategy;

[0039] Adaptation and matching are performed based on the location scenario data and the location scenario verification strategy.

[0040] This application also provides a cloud data security management system based on a multi-tenant architecture, including:

[0041] A tenant data acquisition module, which is used to acquire tenant data of the architecture, wherein the tenant data includes the number of tenants and the tenant authority level;

[0042] A storage space partitioning module, the storage space partitioning module is used to read the activity log of the architecture, evaluate the activity of each tenant based on the activity log, establish the activity evaluation result, and partition the storage space of the architecture with the activity evaluation result and the tenant data to establish the storage space partition result, wherein the storage space partition result includes a shared storage space partition result and a physically isolated storage space partition result;

[0043] A reconstruction data level generation module, the reconstruction data level generation module is used to read the account data corresponding to each tenant, send the account data and the tenant authority level to the tenant storage level reconstruction model, and generate the reconstruction data level of the account data;

[0044] A first important evaluation result generating module, the first important evaluation result generating module is used to perform an important evaluation of the account data based on the reconstructed data level to generate a first important evaluation result;

[0045] A second important evaluation result generating module, the second important evaluation result generating module is used to perform a grade analysis on all account data, reconstruct the grade standard of the architecture, perform a data importance evaluation on each account data based on the grade standard, and generate a second important evaluation result;

[0046] A partition storage execution module is used to perform storage constraints on account data through the first important evaluation result and the second important evaluation result, and call the storage space division result to execute partition storage of shared storage space division results and physically isolated storage space division results.

[0047] The cloud data security management method and system based on the multi-tenant architecture proposed in this application first obtain the tenant data of the architecture, the tenant data includes the number of tenants and the tenant authority level, then read the activity log of the architecture, evaluate the activity of each tenant based on the activity log, establish the activity evaluation result, divide the storage space of the architecture with the activity evaluation result and the tenant data, and establish the storage space division result, wherein the storage space division result includes the shared storage space division result and the physically isolated storage space division result, then read the account data corresponding to each tenant, send the account data and the tenant authority level to the tenant storage level reconstruction model, generate the reconstructed data level of the account data, then perform the account data importance evaluation based on the reconstructed data level, generate the first important evaluation result, then perform the level analysis on all the account data, reconstruct the level standard of the architecture, perform the data importance evaluation on each account data based on the level standard, generate the second important evaluation result, finally perform the storage constraint of the account data through the first important evaluation result and the second important evaluation result, call the storage space division result, execute the partition storage of the shared storage space division result and the physically isolated storage space division result, thereby achieving the technical effect of reducing the security risk between tenant data and protecting the security and privacy of tenant data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0049] Figure 1 A flowchart of a cloud data security management method based on a multi-tenant architecture provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the structure of a cloud data security management system based on a multi-tenant architecture provided in an embodiment of the present application.

[0051] Explanation of the reference numerals: tenant data acquisition module 10 , storage space division module 20 , reconstruction data level generation module 30 , first important evaluation result generation module 40 , second important evaluation result generation module 50 , partition storage execution module 60 . DETAILED DESCRIPTION

[0052] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0054] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0055] The present application embodiment provides a cloud data security management method based on a multi-tenant architecture, such as Figure 1 As shown, the method includes:

[0056] Step S100, obtain the tenant data of the architecture, and the tenant data includes the number of tenants and the tenant permission level. Specifically, start the data collection process, ensure that the data collection tools and interfaces are available, connect to the system or database storing the tenant data through the preset API or database interface, and retrieve and extract the tenant data related to the number of tenants and the tenant permission level. Among them, in a multi-tenant architecture, a tenant refers to an independent organization or user that uses the services or resources provided by a cloud service provider, and each tenant has its own data, applications and users. The number of tenants refers to the total number of tenants registered and used in the multi-tenant architecture. The tenant permission level refers to the level of permission that different tenants enjoy in the system to access, operate or manage data. The tenant permission level is set according to the tenant's business needs, security requirements and contractual agreements. For example, some tenants may only have read-only permissions, while other tenants may have permissions to read, write, delete or manage other tenants' data.

[0057] Step S200, read the activity log of the architecture, evaluate the activity of each tenant based on the activity log, establish the activity evaluation result, divide the storage space of the architecture based on the activity evaluation result and the tenant data, and establish the storage space division result, wherein the storage space division result includes the shared storage space division result and the physically isolated storage space division result. Specifically, access and read the activity log database or file of the architecture, the activity log is a log that records all activities in the system, and extract log entries related to tenant activities, such as login events, data access events, operation events, etc. Analyze the extracted activity log data, calculate the activity index for each tenant, including login frequency, number of operations, data access volume, online time, etc., and generate the activity evaluation result for each tenant according to the preset activity evaluation algorithm or rules. Combine tenant data and activity evaluation results to formulate a storage space division strategy. According to the strategy, determine which tenants should be allocated shared storage space (logical isolation) and which tenants should be allocated physically isolated storage space. Among them, shared storage space (physical or logical storage space shared by multiple tenants, ensuring the independence of tenant data through logical isolation) is suitable for tenants with low activity or low data importance, and physically isolated storage space (physical storage space allocated separately for each tenant to ensure complete isolation and security of data at the physical level) is suitable for tenants with high activity or high data importance. Configure storage space division settings in the system, allocate corresponding storage space to different tenants, update tenant storage space information, and notify relevant tenants.

[0058] In a possible implementation, the storage space division is structured based on the activity evaluation result and the tenant data to establish the storage space division result, and step S200 further includes step S210, calling the tenant permissions in the tenant data, performing permission rating based on the tenant permissions, and generating a fuzzy division ratio of the shared storage space division result and the physically isolated storage space division result according to the permission rating result. Specifically, the tenant permission information in the tenant data is read, including data access rights, modification rights, management rights, etc., and the tenant permissions are rated according to preset permission rating rules, wherein the permission rating rules can be based on factors such as the type, scope, and sensitivity of the permissions. Based on the permission rating results, a fuzzy division ratio of the shared storage space and the physically isolated storage space is generated for each tenant, and the fuzzy division ratio is a fuzzy range or probability distribution. Step S220, calling the active storage data based on the activity evaluation result, and establishing the hierarchical data storage mean. Specifically, read the storage data (data frequently accessed, modified or used) marked as active in the activity evaluation result, perform statistical analysis on these active storage data, calculate their proportion and distribution in the entire storage data, and obtain a hierarchical data storage mean, which reflects the overall situation of active data in the storage system. Step S230, adjust the fuzzy partition ratio by the hierarchical data storage mean, and complete the storage space division according to the adjustment result. Specifically, adjust the fuzzy partition ratio generated in step S210 according to the hierarchical data storage mean. For example, if the proportion of active data is large, increase the partition ratio of shared storage space; conversely, if the proportion of active data is small, increase the partition ratio of physically isolated storage space. According to the adjusted fuzzy partition ratio, allocate specific storage space to tenants. This implementation method uses the fuzzy partition ratio to make the partition of storage space have a certain degree of flexibility and adaptability, and can better cope with the actual needs of different tenants and data, achieving the technical effect of making the storage space partition more reasonable and flexible.

[0059] Step S300, read the account data corresponding to each tenant, send the account data and tenant authority level to the tenant storage level reconstruction model, and generate the reconstructed data level of the account data. Specifically, traverse all tenants and access the tenant account data management system or database. For each tenant, extract the corresponding account data. The account data is a collection of all data related to the tenant account, including user information, access records, operation logs, etc. The tenant authority level obtained from the tenant data management system is associated with the account data, wherein the tenant authority level includes access rights, data modification rights, management rights, etc., which is one of the bases for evaluating the importance of account data. The tenant's account data and permission level information are taken as input and sent to the tenant storage level reconstruction model. The tenant storage level reconstruction model is a decision system or algorithm based on machine learning, rule engine or custom algorithm, which is used to generate the reconstruction data level of account data according to the account data and permission level information. The tenant storage level reconstruction model evaluates the importance and sensitivity of each account data based on the input account data and permission level information, and generates a reconstruction data level for each account data based on factors such as account activity, data access frequency, data modification history, data size, and data type. The reconstruction data level can be a value, label or classification, which is used to indicate the importance and security requirements of the data.

[0060] In a possible implementation, step S300 further includes step S310, performing permission granularity division on the tenant permission level, establishing the permission granularity division result, and configuring a mapping verification strategy with the permission granularity division result, wherein the mapping verification strategy includes a trust authentication verification strategy and a location scenario verification strategy. Specifically, the tenant permission level is divided into a finer granularity. For example, originally there may be only two permission levels of "senior administrator" and "ordinary user", which are now subdivided into multiple more specific permission levels such as "system administrator", "data administrator", and "read-only user". These refined permission levels and corresponding permission ranges are recorded to form a permission granularity division result. Then, for each permission granularity division result, a corresponding mapping verification strategy is configured (dynamically matching and executing the corresponding verification strategy according to the different attributes and behaviors of the tenant), including a trust authentication verification strategy and a location scenario verification strategy, wherein the trust authentication verification strategy includes password complexity, number of login attempts, device trust, etc.; the location scenario verification strategy includes factors such as the geographical location and time of the tenant login. Step S320, when the tenant performs account login, the multi-dimensional data trust verification of the login is performed to generate a trust verification result. Specifically, when a tenant attempts to log in, its login data, such as user name, password, login device information, etc., are collected. According to the configured trust authentication verification strategy, these login data are verified in multiple dimensions, such as password strength, login device history, etc. Based on the verification result, a trust verification result is generated. The trust verification result can be a score or grade, which is used to indicate the credibility of the tenant's login. Step S330, the location scene data of the tenant is obtained, and the mapping verification strategy is matched through the trust verification result and the location scene data. Specifically, the current location scene data of the tenant is obtained by IP address resolution, mobile device positioning, etc., and the corresponding mapping verification strategy is matched and executed according to the tenant's permission granularity, trust verification result and location scene data. For example, if the tenant is a high-authority user, but logs in during non-working hours or non-office locations, a stricter verification strategy needs to be executed. Step S340, the granular permissions for the current tenant login are configured according to the mapping verification strategy matching result. Specifically, based on the mapping verification strategy matching result, the permission level that the current tenant should obtain when logging in is determined, and according to the determined permission level, the specific granular permissions are configured for the tenant, that is, the scope of data and functions that the tenant can access and operate after logging in. This implementation method makes permission management more flexible and precise by refining permission granularity, configuring mapping verification strategies, and performing multi-dimensional data trust verification and location scenario verification when tenants log in. It can dynamically configure appropriate permission ranges for tenants based on their different attributes and behaviors, thereby reducing the risk of permission abuse and misoperation, and achieving the technical effect of improving data security.

[0061] In a possible implementation, step S320 further includes step S321, establishing a historical interaction success rate evaluation index, performing trust authentication on the account through the historical interaction success rate evaluation index, and generating a first-dimensional trust verification result. Specifically, the interaction data between the account and the system in the past period of time, including the number of logins, the number of successful logins, the number of failed logins, etc., is collected, and a historical interaction success rate evaluation index is calculated based on the collected historical interaction data. The historical interaction success rate evaluation index is a percentage, which indicates the proportion of the number of successful logins of the account in the past period of time to the total number of logins. The account is trusted and authenticated using the historical interaction success rate evaluation index. If the historical interaction success rate is high, the credibility of the account is correspondingly high. Based on the result of the trust authentication, a first-dimensional trust verification result is generated. The first-dimensional trust verification result can be a trust score or grade. Step S322, obtain the current login authentication data of the account, perform login trusted authentication based on the login authentication data, and generate a second-dimensional trust verification result. Specifically, when an account attempts to log in, the login authentication data provided by the account is obtained, that is, the data used to verify its identity, such as user name, password, biometrics, etc., and the login authentication data is verified using preset verification rules to check whether it is correct, complete and valid. According to the result of the login trusted authentication, a second-dimensional trust verification result is generated, and the second-dimensional trust verification result can also be a trust score or grade. Step S323, establish an authentication window, and authenticate the behavior activities of the account in the window through the authentication window to establish a third-dimensional trust verification result. Specifically, a time window (i.e., authentication window) is set to observe and analyze the behavior activities of the account within a specific time to determine its security. Within the authentication window, the behavior activities of the account are monitored, such as click behavior, page browsing, input operations, etc., and the behavior activities of the account within the authentication window are analyzed to see whether they meet the expected or normal mode. For example, check whether the account frequently attempts to log in and fails, whether it accesses a large number of sensitive pages in a short time, etc. Based on the result of the behavior activity authentication, a third-dimensional trust verification result is generated, and the third-dimensional trust verification result can also be a trust score or grade. Step S324, establish a trust weight factor, perform weighted calculation of the first dimension trust verification result, the second dimension trust verification result and the third dimension trust verification result by the trust weight factor, and complete the multi-dimensional data trust verification according to the weighted calculation result. Specifically, a trust weight factor is assigned to each dimension trust verification result to indicate the importance of the dimension in the overall trust assessment, and the trust weight factor is used to perform weighted calculation on the first dimension, the second dimension and the third dimension trust verification results to obtain a comprehensive trust verification result. Based on the weighted calculation result, the multi-dimensional data trust verification is completed, and it is decided whether to allow account login or further operation.This implementation method accurately determines whether an account is safe and trustworthy by comprehensively considering the historical interaction success rate, the accuracy of the current login authentication data, and whether the account's behavior after login is in line with expectations, and comprehensively evaluates the credibility of the account, thereby achieving the technical effect of improving the security and reliability of the system and reducing the risks caused by account impersonation or illegal access.

[0062] In a possible implementation, step S340 further includes step S341, establishing an activity feature set of the tenant based on the authentication window. Specifically, all activity data of the tenant in the authentication window, including but not limited to the pages visited, the operations performed, the timestamps of the operations, etc., are collected, key features such as access frequency, operation type, operation sequence, etc. are extracted from the collected activity data, and the activity feature set of the tenant is constructed, and the activity feature set is used to describe the behavior pattern of the tenant. Step S342, after the tenant logs in through the corresponding account, the time series activity anomaly monitoring of the tenant is performed based on the activity feature set, and a time series anomaly value is generated. Specifically, when the tenant logs in through the corresponding account, its activity data is collected in real time, and matched with the established activity feature set, and the matching degree of the current activity data with the historical activity feature set is analyzed to determine whether there is a significant difference, such as a sudden increase or decrease in the access frequency, the appearance of an abnormal operation sequence, etc. According to the results of the anomaly monitoring, a time series anomaly value is generated, and the time series anomaly value can be a numerical value or a level, which is used to quantify the degree of abnormality of the current activity. Step S343, dynamically fine-tuning granular permissions is established through the time series anomaly value, and the granular permissions of the current tenant login are updated through the dynamic fine-tuning granular permissions. Specifically, the size and change trend of the time series anomaly value are analyzed to determine whether the current tenant's activities belong to security risks. According to the analysis results of the time series anomaly value, the granular permissions of the current tenant are dynamically configured or adjusted. For example, if the time series anomaly value is high, the tenant's permission level is reduced or its access scope is restricted; if the time series anomaly value is low or normal, the tenant's permission level is maintained or increased. The adjusted granular permissions are applied to the login session of the current tenant so that its operation in the system complies with the new permission configuration. This implementation method enables the system to evaluate the security of the tenant's behavior after login in real time by monitoring the abnormality of the tenant's time series activities and dynamically updating permissions based on the activity feature set, and dynamically adjusts its permission configuration according to the evaluation results, thereby achieving the technical effect of improving the flexibility and security of the system, enabling the system to respond to potential security risks more quickly, and taking corresponding measures to protect the security of data and systems.

[0063] In a possible implementation, step S340 further includes step S344, determining whether the current tenant's usage rights exceed the updated granular rights. Specifically, the current tenant's operation behavior in the system is monitored in real time, and the permissions required for the operation that the current tenant is trying to perform are compared with the updated granular rights to determine whether the current tenant's operation rights exceed the updated granular rights. Step S345, if exceeded, an auxiliary authentication application is generated. Specifically, if it is determined that the current tenant's usage rights exceed the updated granular rights, the auxiliary authentication (an additional authentication measure taken in addition to the conventional authentication method to further improve security) application process is triggered, and an auxiliary authentication application is automatically generated and sent to the relevant manager or the tenant himself for confirmation. Step S346, the account login and auxiliary authentication of the associated tenant are executed through the auxiliary authentication application. Specifically, the relevant manager or the tenant himself receives and confirms the auxiliary authentication application, first completes the normal account login process, and after the login is successful, performs additional auxiliary authentication measures, such as receiving a verification code, answering security questions, etc., and determines whether the tenant has passed the dual authentication based on the results of the account login and auxiliary authentication. Step S347, update the granular permissions again according to the double authentication result. Specifically, based on the result of the double authentication, determine whether the tenant has successfully passed the authentication. If the tenant has successfully passed the double authentication, update the tenant's granular permissions as needed, allow the tenant to perform operations beyond the original permission range, feedback the results of the permission update to the tenant, and notify the tenant that it can continue with subsequent operations. This implementation method triggers an auxiliary authentication application when the tenant attempts to perform an operation beyond its current granular permissions, requiring the tenant to perform additional authentication. This double authentication method effectively prevents unauthorized access and operations, and achieves the technical effect of further improving the security of the system.

[0064] In a possible implementation, step S330 further includes step S331, performing login location scenario statistics on tenants, and establishing location scenario statistics with login trust identifiers. Specifically, the location information (such as IP address, longitude and latitude, etc.) is recorded each time the tenant logs in, and based on the collected location information, the tenant's historical login location is counted to analyze common login locations and scenarios. A login trust identifier (which can be a value, level or label) is assigned to each location scenario to identify the degree of trust of the location scenario. For example, a company office or home address has a higher degree of trust, and an unknown public network has a lower degree of trust. The location scenario statistics with the login trust identifier are stored in a database. Step S332, location scenario clustering is performed on the location scenario statistics results, and an initial trust policy is generated based on the cluster size of the location scenario. Specifically, a clustering algorithm is used to cluster the location scenario statistics results, similar location scenarios are classified into one category, and the size of each cluster is evaluated, that is, the number of logins or the number of tenants contained therein. According to the size of the cluster, an initial trust policy, that is, a preliminary trust rule or setting, is generated for each cluster. The larger the cluster (that is, the more login times or tenants it contains), the looser its trust policy; conversely, the smaller the cluster, the stricter the trust policy. Step S333, obtain the trust cumulative value of the location scene clustering result, compensate the initial trust policy with the trust cumulative value, and establish a location scene verification strategy. Specifically, according to historical data, based on factors such as historical login success rate and security incident incidence rate, the trust cumulative value of each location scene cluster is calculated. The initial trust policy is compensated or adjusted using the trust cumulative value. For example, if the trust cumulative value of a cluster is low (that is, the historical performance of the location scene is poor), its trust policy needs to be tightened and additional verification steps need to be added. Based on the adjusted initial trust policy, a corresponding verification strategy is established for each location scene cluster, including whether secondary verification is required, the method and frequency of verification, etc. Step S334, adaptation and matching is performed based on the location scene data and the location scene verification strategy. Specifically, when a tenant attempts to log in, its real-time location data is obtained, and the real-time location data is matched with the location scenario data to determine the tenant's current location scenario. According to the matched location scenario and the corresponding location scenario verification strategy, the corresponding verification operation is performed on the tenant. For example, if the tenant is in a location scenario with a low degree of trust, the system will require additional identity authentication. This implementation method more accurately evaluates the security of tenant login behavior by establishing a tenant trust strategy based on location scenarios, and formulates corresponding verification strategies according to the risk level of different location scenarios, thereby achieving the technical effect of improving the security of the system and user experience, reducing unnecessary verification operations, and effectively preventing potential security risks.

[0065] Step S400, based on the reconstructed data level, the importance of the account data is evaluated and a first importance evaluation result is generated. Specifically, the reconstructed data level information generated in step S300 is read. The reconstructed data level information performs a preliminary evaluation of the importance and sensitivity of the account data. According to business needs and security requirements, specific criteria for the importance evaluation of account data are formulated, including the correspondence between data level and importance, special processing requirements for specific data, etc. The reconstructed data level of each account is compared with the formulated evaluation criteria. According to the comparison results, an importance evaluation value or grade is generated for the data of each account. This evaluation value or grade reflects the importance and sensitivity of the account data. The importance evaluation values ​​or grades of all accounts are summarized to form the first importance evaluation result.

[0066] Step S500: perform a grade analysis on all account data, reconstruct the grade standard of the architecture, and perform a data importance evaluation on each account data based on the grade standard to generate a second important evaluation result. Specifically, collect and analyze the detailed information of all account data, including data type, access frequency, modification history, data volume, etc., identify the correlation and difference between account data through data mining and statistical analysis technology, and assign a preliminary grade label to each account data based on these analysis results. Based on the grade analysis results of account data, review and adjust the existing architecture grade standard. The architecture grade standard includes multiple dimensions such as data importance, sensitivity, access rights, etc., and reconstructs according to business needs, security policies, compliance requirements, etc., and uses the reconstructed architecture grade standard to perform a detailed data importance evaluation on each account data, such as data value, business impact, security risk, etc. Based on the evaluation results, generate a comprehensive and specific data importance level for each account data, summarize the importance levels of all account data, and form a second important evaluation result.

[0067] Step S600, the storage constraints of the account data are performed through the first important evaluation result and the second important evaluation result, and the storage space division result is called to perform partition storage of the shared storage space division result and the physically isolated storage space division result. Specifically, the first important evaluation result and the second important evaluation result are obtained, and the two important evaluation results are comprehensively analyzed to determine the final importance and storage requirements of each account data. According to the final importance and storage requirements of the account data, a storage constraint strategy is formulated, including data backup frequency, storage location restrictions, access control strategy, etc. The storage space division result generated in step S200 is called, and according to the division result, which account data should be stored in the shared storage space and which should be stored in the physically isolated storage space are identified. For the account data that should be stored in the shared storage space, it is stored in a storage area that is logically isolated but physically possible to be shared; for the account data that should be stored in the physically isolated storage space, it is ensured that it is completely isolated from other data at the physical level to improve security. During the storage process, the storage constraint strategy is applied to ensure the security and accessibility of the data. The embodiments of the present application adopt technical means such as obtaining tenant data, performing activity evaluation, dividing storage space, performing importance evaluation, and performing storage constraints on account data, thereby achieving the technical effect of reducing security risks between tenant data and protecting the security and privacy of tenant data.

[0068] In the above, refer to Figure 1 The cloud data security management method based on a multi-tenant architecture according to an embodiment of the present invention is described in detail. Figure 2 A cloud data security management system based on a multi-tenant architecture according to an embodiment of the present invention is described.

[0069] The cloud data security management system based on a multi-tenant architecture according to an embodiment of the present invention is used to solve the technical problem of high security risk in the existing multi-tenant architecture cloud data security management, and achieve the technical effect of reducing the security risk between tenant data and protecting the security and privacy of tenant data. The cloud data security management system based on a multi-tenant architecture includes: a tenant data acquisition module 10, a storage space partitioning module 20, a reconstruction data level generation module 30, a first important evaluation result generation module 40, a second important evaluation result generation module 50, and a partition storage execution module 60.

[0070] The tenant data acquisition module 10 is used to acquire tenant data of the architecture, wherein the tenant data includes the number of tenants and the tenant authority level;

[0071] The storage space partitioning module 20 is used to read the activity log of the architecture, evaluate the activity of each tenant based on the activity log, establish the activity evaluation result, and partition the storage space of the architecture with the activity evaluation result and the tenant data to establish the storage space partition result, wherein the storage space partition result includes a shared storage space partition result and a physically isolated storage space partition result;

[0072] The reconstructed data level generation module 30 is used to read the account data corresponding to each tenant, send the account data and the tenant authority level to the tenant storage level reconstruction model, and generate the reconstructed data level of the account data;

[0073] The first important evaluation result generating module 40 is used to perform an important evaluation of the account data based on the reconstructed data level and generate a first important evaluation result;

[0074] The second important evaluation result generating module 50 is used to perform a grade analysis on all account data, reconstruct the grade standard of the architecture, perform a data importance evaluation on each account data based on the grade standard, and generate a second important evaluation result;

[0075] The partition storage execution module 60 is used to perform storage constraints on account data through the first important evaluation result and the second important evaluation result, and call the storage space division result to execute partition storage of the shared storage space division result and the physically isolated storage space division result.

[0076] The specific configuration of the storage space partitioning module 20 will be described in detail below. As described above, the storage space partitioning of the architecture is performed based on the activity evaluation result and the tenant data, and the storage space partitioning result is established. The storage space partitioning module 20 may further include: a fuzzy partitioning ratio generation unit is used to call the tenant permissions in the tenant data, perform permission rating based on the tenant permissions, and generate a fuzzy partitioning ratio of the shared storage space partitioning result and the physically isolated storage space partitioning result according to the permission rating result; a hierarchical data storage mean establishment unit is used to call the active storage data based on the activity evaluation result and establish a hierarchical data storage mean; an adjustment unit is used to adjust the fuzzy partitioning ratio through the hierarchical data storage mean, and complete the storage space partitioning according to the adjustment result.

[0077] The specific configuration of the reconstruction data level generation module 30 will be described in detail below. As described above, the reconstruction data level generation module 30 may further include: a mapping verification strategy configuration unit for performing permission granularity division on the tenant permission level, establishing permission granularity division results, and configuring a mapping verification strategy with the permission granularity division results, wherein the mapping verification strategy includes a trust authentication verification strategy and a location scenario verification strategy; a multidimensional data trust verification unit for performing multidimensional data trust verification of login when the tenant performs account login, and generating a trust verification result; a mapping verification strategy matching unit for obtaining the tenant's location scenario data, and performing mapping verification strategy matching through the trust verification result and the location scenario data; a granular permission configuration unit for configuring the granular permissions for the current tenant login according to the mapping verification strategy matching results.

[0078] Among them, when the tenant performs account login, the multi-dimensional data trust verification of the login is performed to generate a trust verification result. The multi-dimensional data trust verification unit may further include: a trust verification result generation subunit is used to establish a historical interaction success rate evaluation index, trust authentication is performed on the account through the historical interaction success rate evaluation index, a first-dimensional trust verification result is generated, the current login authentication data of the account is obtained, a login trusted authentication is performed based on the login authentication data, a second-dimensional trust verification result is generated, an authentication window is established, and the behavior activities of the account in the window are authenticated through the authentication window to establish a third-dimensional trust verification result; a weighted calculation subunit is used to establish a trust weight factor, and weighted calculation of the first-dimensional trust verification result, the second-dimensional trust verification result and the third-dimensional trust verification result are performed through the trust weight factor, and the multi-dimensional data trust verification is completed according to the weighted calculation result.

[0079] Among them, the granular permission configuration unit may further include: an activity feature set establishment subunit for establishing an activity feature set of the tenant based on the authentication window; a time series activity anomaly monitoring subunit for performing time series activity anomaly monitoring of the tenant based on the activity feature set after the tenant logs in through the corresponding account, and generating a time series anomaly value; a granular permission update subunit for establishing dynamically fine-tuned granular permissions through the time series anomaly value, and updating the granular permissions of the current tenant login through the dynamically fine-tuned granular permissions.

[0080] Among them, the granular permission configuration unit may further include: a judgment subunit for judging whether the current tenant's usage rights exceed the updated granular permissions; an auxiliary authentication application generation subunit for generating an auxiliary authentication application if it exceeds; a dual authentication execution subunit for executing dual authentication of the associated tenant's account login and auxiliary authentication through the auxiliary authentication application; and a granular permission re-update subunit for re-updating the granular permissions according to the dual authentication results.

[0081] Among them, the location scenario data of the tenant is obtained, and a mapping verification policy is matched through the trust verification result and the location scenario data. The mapping verification policy matching unit may further include: a login location scenario statistics subunit is used to perform login location scenario statistics on the tenant, and establish a location scenario statistics result with a login trust identifier; an initial trust policy generation subunit is used to cluster the location scenario statistics result, and generate an initial trust policy according to the cluster size of the location scenario; a location scenario verification policy establishment subunit is used to obtain the trust accumulation value of the location scenario clustering result, compensate the initial trust policy through the trust accumulation value, and establish a location scenario verification policy; an adaptation and matching subunit is used to perform adaptation and matching based on the location scenario data and the location scenario verification policy.

[0082] The cloud data security management system based on a multi-tenant architecture provided in an embodiment of the present invention can execute the cloud data security management method based on a multi-tenant architecture provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0083] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0084] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A cloud data security management method based on a multi-tenant architecture, characterized in that: The method comprises: Obtain tenant data of the architecture, the tenant data including the number of tenants and tenant authority levels; Reading the activity log of the architecture, evaluating the activity of each tenant based on the activity log, establishing an activity evaluation result, dividing the storage space of the architecture with the activity evaluation result and the tenant data, and establishing a storage space division result, wherein the storage space division result includes a shared storage space division result and a physically isolated storage space division result; Read the account data corresponding to each tenant, send the account data and the tenant authority level to the tenant storage level reconstruction model, and generate a reconstructed data level of the account data; Performing an importance evaluation of the account data based on the reconstructed data level to generate a first importance evaluation result; Performing a hierarchical analysis on all account data, reconstructing the hierarchical standards of the architecture, and performing a data importance evaluation on each account data based on the hierarchical standards to generate a second importance evaluation result; The storage constraints of the account data are performed by using the first important evaluation result and the second important evaluation result, and the storage space division result is called to execute the partition storage of the shared storage space division result and the physically isolated storage space division result.

2. The cloud data security management method based on multi-tenant architecture according to claim 1, characterized in that: The partitioning of the storage space of the architecture based on the activity evaluation result and the tenant data to establish the storage space partition result further includes: Calling tenant permissions in the tenant data, performing permission rating based on the tenant permissions, and generating a fuzzy division ratio of a shared storage space division result and a physically isolated storage space division result according to the permission rating result; Based on the activity evaluation result, active storage data is called to establish a level data storage mean; The fuzzy partition ratio is adjusted by the hierarchical data storage mean value, and the storage space division is completed according to the adjustment result.

3. The cloud data security management method based on multi-tenant architecture according to claim 1, characterized in that: The method further comprises: Performing permission granularity division on the tenant permission level, establishing permission granularity division results, and configuring a mapping verification strategy corresponding to the permission granularity division results, wherein the mapping verification strategy includes a trust authentication verification strategy and a location scenario verification strategy; When a tenant logs in to an account, a multi-dimensional data trust verification is performed to generate a trust verification result. Acquire the location scenario data of the tenant, and perform mapping verification strategy matching with the trust verification result and the location scenario data; Configure the granular permissions for the current tenant login based on the mapping verification policy matching results.

4. The cloud data security management method based on multi-tenant architecture according to claim 3, characterized in that: When the tenant performs account login, the multi-dimensional data trust verification of the login is performed to generate a trust verification result, and further includes: Establishing a historical interaction success rate evaluation index, performing trust authentication on the account through the historical interaction success rate evaluation index, and generating a first-dimensional trust verification result; Obtaining current login authentication data of the account, performing login trusted authentication based on the login authentication data, and generating a second-dimensional trust verification result; Establishing an authentication window, authenticating the behavior and activities of the account in the window through the authentication window, and establishing a third-dimensional trust verification result; A trust weight factor is established, and weighted calculation of the first-dimensional trust verification result, the second-dimensional trust verification result, and the third-dimensional trust verification result is performed using the trust weight factor, and multi-dimensional data trust verification is completed according to the weighted calculation result.

5. The cloud data security management method based on multi-tenant architecture according to claim 4, characterized in that: The method further comprises: Establishing an activity feature set for the tenant based on the authentication window; After the tenant logs in through the corresponding account, the tenant's time series activity anomaly monitoring is performed based on the activity feature set to generate a time series anomaly value; Dynamic fine-tuning granularity permissions are established through the time series anomaly values, and granularity permissions for the current tenant login are updated through the dynamic fine-tuning granularity permissions.

6. The cloud data security management method based on multi-tenant architecture according to claim 5, characterized in that: The method further comprises: Determine whether the current tenant's usage permissions exceed the updated granular permissions; If it exceeds, an auxiliary certification application will be generated; Perform dual authentication of account login and auxiliary authentication of the associated tenant through the auxiliary authentication application; Update the granular permissions again based on the two-factor authentication results.

7. The cloud data security management method based on multi-tenant architecture according to claim 3, characterized in that: The acquiring of the location scenario data of the tenant and performing mapping verification strategy matching with the trust verification result and the location scenario data further includes: Conduct login location scenario statistics for tenants and establish location scenario statistics results with login trust identifiers; Clustering the location scenario statistics results into location scenarios, and generating an initial trust strategy according to the cluster size of the location scenarios; Obtaining a trust accumulation value of the location scenario clustering result, compensating the initial trust strategy by the trust accumulation value, and establishing a location scenario verification strategy; Adaptation and matching are performed based on the location scenario data and the location scenario verification strategy.

8. A cloud data security management system based on a multi-tenant architecture, characterized in that: The system is used to implement the cloud data security management method based on a multi-tenant architecture according to any one of claims 1 to 7, and the system includes: A tenant data acquisition module, which is used to acquire tenant data of the architecture, wherein the tenant data includes the number of tenants and the tenant authority level; A storage space partitioning module, the storage space partitioning module is used to read the activity log of the architecture, evaluate the activity of each tenant based on the activity log, establish the activity evaluation result, and partition the storage space of the architecture with the activity evaluation result and the tenant data to establish the storage space partition result, wherein the storage space partition result includes a shared storage space partition result and a physically isolated storage space partition result; A reconstruction data level generation module, the reconstruction data level generation module is used to read the account data corresponding to each tenant, send the account data and the tenant authority level to the tenant storage level reconstruction model, and generate the reconstruction data level of the account data; A first important evaluation result generating module, the first important evaluation result generating module is used to perform an important evaluation of the account data based on the reconstructed data level to generate a first important evaluation result; A second important evaluation result generating module, the second important evaluation result generating module is used to perform a grade analysis on all account data, reconstruct the grade standard of the architecture, perform a data importance evaluation on each account data based on the grade standard, and generate a second important evaluation result; A partition storage execution module is used to perform storage constraints on account data through the first important evaluation result and the second important evaluation result, and call the storage space division result to execute partition storage of shared storage space division results and physically isolated storage space division results.

Images