Database access permission determination method and device, server, storage medium and program product

Abstract

The embodiment of the invention discloses a database access permission determination method and device, a server, a storage medium and a program product. The method is applied to a server side, the server side is applied in cooperation with a database, and the method can comprise the steps that in response to an access permission request initiated by an intelligent agent for the database, access permission features are determined; determining a target access permission of the intelligent agent to the database according to the access permission feature; wherein the access permission feature comprises at least one of the following items: the target credibility of the agent, a preset security level for the first data accessed by the agent for the database request, an operation type adopted when the agent accesses the first data to operate the first data, and a context environment, corresponding to the access permission request, of the agent. The technical scheme of the embodiment of the invention can reduce the risk that the intelligent agent has unauthorized access to the database.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of database security, and in particular to a database access permission determination method and device, a server, a storage medium and a program product. BACKGROUND

[0002] In the field of database security, in order to ensure database security, a database access permission control system has emerged. The system implements control of database access permissions based on a role-based access control (RBAC) model. Specifically, access permissions are assigned to roles, and then users indirectly obtain corresponding access permissions by being bound to a role.

[0003] In recent years, with the rapid development of artificial intelligence (AI) technology, AI agents have emerged. Users can interact with agents and entrust the agents to perform tasks, at which time the user is no longer accessing the database, but the agent is, and the agent can indirectly obtain corresponding access permissions by being assigned to a role.

[0004] In the process of implementing the present application, the inventors have found that the prior art has the following technical problems: there is a risk of unauthorized access, which needs to be addressed. SUMMARY

[0005] Embodiments of the present application provide a database access permission determination method and device, a server, a storage medium and a program product to reduce the risk of unauthorized access by an agent.

[0006] According to an aspect of the present application, a database access permission determination method is provided, which is applied to a server cooperating with a database. The method comprises:

[0007] In response to an access permission request initiated by an agent for the database, determining an access permission feature;

[0008] According to the access permission feature, determining a target access permission of the agent for the database;

[0009] The access permission feature comprises at least one of the following:

[0010] The target credibility of the agent, a security level preset for a first data requested by the agent for the database, an operation type adopted by the agent when accessing the first data to operate the first data, and a context environment of the agent corresponding to the access permission request.

[0011] According to another aspect of the present application, there is provided a database access permission determination apparatus, configured on a server cooperating with a database, comprising:

[0012] An access permission feature determination module, configured to determine an access permission feature in response to an access permission request initiated by an agent to the database;

[0013] A target access permission determination module, configured to determine a target access permission of the agent to the database according to the access permission feature;

[0014] The access permission feature comprises at least one of the following:

[0015] The target trustworthiness of the agent, a security level preset for the agent to request access to first data of the database, an operation type adopted by the agent when accessing the first data to operate the first data, and a context environment of the agent corresponding to the access permission request.

[0016] According to another aspect of the present application, there is provided a server, which can comprise:

[0017] At least one processor; and

[0018] A memory in communication connection with the at least one processor; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to implement the database access permission determination method provided by any of the embodiments of the present application.

[0020] According to another aspect of the present application, there is provided a computer readable storage medium, which stores computer instructions for enabling a processor to implement the database access permission determination method provided by any of the embodiments of the present application.

[0021] According to another aspect of the present application, there is provided a computer program product, which stores a computer program executable by a processor to implement the database access permission determination method provided by any of the embodiments of the present application.

[0022] The technical solution of the embodiments of the present application determines an access permission feature in response to an access permission request initiated by an agent to a database, and then determines a target access permission of the agent to the database according to the access permission feature. The above technical solution dynamically grants a target access permission to the agent by using the access permission feature, which can significantly reduce the risk of the agent accessing the database beyond the authority, thereby adapting to the dynamic authorization demand in the AI era and better ensuring the security of the database.

[0023] It is to be understood that the details set forth herein do not limit the scope of the embodiments of the application to the specific embodiments described. Rather, the scope of the embodiments of the application is to be defined by the appended claims. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0025] Figure 1 is a flow chart of a database access permission determination method according to an embodiment of the present application;

[0026] Figure 2 is a timing diagram of token generation and verification in a database access permission determination method according to an embodiment of the present application;

[0027] Figure 3 is a flow chart of another database access permission determination method according to an embodiment of the present application;

[0028] Figure 4 is a flow chart of still another database access permission determination method according to an embodiment of the present application;

[0029] Figure 5 is a schematic diagram of access permission feature and risk score calculation in still another database access permission determination method according to an embodiment of the present application;

[0030] Figure 6a is a schematic diagram of system core components in still another database access permission determination method according to an embodiment of the present application;

[0031] Figure 6b is a schematic diagram of the interaction between data plane and control plane in still another database access permission determination method according to an embodiment of the present application;

[0032] Figure 6c is a flow chart of an optional example in still another database access permission determination method according to an embodiment of the present application;

[0033] Figure 7 is a structural block diagram of a database access permission determination apparatus according to an embodiment of the present application;

[0034] Figure 8 is a structural block diagram of a server implementing a database access permission determination method according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the personnel in the technical field better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the protection scope of the present application.

[0036] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. The case of "target", "original" and the like is similar, which will not be repeated here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0037] Before introducing the embodiments of the present application, the causes of the problem of the risk of unauthorized access when assigning access rights to agents using the RBAC model will be exemplarily described, so as to better understand how the embodiments of the present application solve this problem and reduce the risk of unauthorized access of agents to databases.

[0038] Exemplarily, the application scenarios and access behaviors of agents are usually dynamically changed, which leads to the risk of unauthorized access when assigning access rights to agents using the RBAC model. The access rights are static (i.e. fixed) access rights, which means that part of the access rights is not the current access rights of the agent, at this time, there is a risk of unauthorized access.

[0039] Figure 1 is a flowchart of a database access right determination method provided by the embodiments of the present application. The present embodiment can be applied to the case of determining the target access rights of agents to databases, and is especially suitable for dynamically determining the target access rights of agents to databases. The method can be executed by a database access right determination device provided by the embodiments of the present application. The device can be realized by software and / or hardware, and can be configured on a server, which can also be called an authorization server. The server is applied in cooperation with a database, and the server and the database can be integrated on the same or different servers.

[0040] Referring to Figure 1 The method of the embodiment of the present application specifically comprises the following steps:

[0041] S110, in response to the access permission request initiated by the agent for the database, determining an access permission feature;

[0042] The access permission feature comprises at least one of the following:

[0043] The target credibility of the agent, the security level preset for the first data requested by the agent to access the database, the operation type adopted by the agent when accessing the first data to operate the first data, and the context environment of the agent corresponding to the access permission request.

[0044] The agent can be understood as a system with the ability of autonomous perception of the environment, decision-making and action, in the embodiment of the present application, it can be specifically understood as an agent that accesses the database to perform a corresponding task, for example, it can be an intelligent customer service robot or a virtual assistant, etc., which is related to the actual situation and is not specifically limited here.

[0045] The access permission request can be understood as a request initiated by the agent to request the permission to access the database. In response to the request, the access permission feature is determined, which can be understood as a feature applied to determine the access permission available to the agent for the database, in the embodiment of the present application, the feature can be at least one of the target credibility, the security level, the operation type and the context environment.

[0046] Specifically, the target credibility can be understood as the current (i.e. real-time) credibility of the agent, which can be represented by a trust score or a trust level, etc., which is not specifically limited here. It should be noted that the credibility is not fixed, but dynamic, in the embodiment of the present application, it can be optionally dynamically changed according to the number of accesses and / or the amount of time elapsed, etc. To determine the credibility, for example, the identity of the agent can be extracted from the access permission request, and then the credibility can be determined according to the identity.

[0047] The first data can be understood as data requested by the agent to access the database, i.e., data required by the agent to request access permission this time, which can be data already in the database or data not in the database (which usually occurs in the case of writing data), which is related to the actual situation and is not specifically limited herein. The security level can be understood as a level preset for the data in terms of security, i.e., the level can represent the level at which the data is protected to avoid the data being accessed by an agent that cannot access the data, thereby causing data leakage. In the embodiments of the present application, optionally, the level can be preset according to at least one of the importance, sensitivity, and confidentiality of the data.

[0048] The agent accessing the first data can be understood as the agent interacting with the first data to achieve reading, writing, executing, or deriving operations on the first data, and the operation type can represent the operation mode of the agent operating the first data, such as reading, writing, executing, or deriving, which is related to the actual situation and is not specifically limited herein. In the embodiments of the present application, optionally, the operation type can be directly read from the permission access request.

[0049] The context environment can be understood as the context environment of the agent, and further can be understood as the context environment of the agent corresponding to the access permission request. For example, it can be time, such as the initiation time of the access permission request and / or the historical access time for the first data; it can be a location, such as the location of a user interacting with the agent; it can also be an associated task, such as a task associated with the task to be completed by the access permission request this time; and the like, which is not specifically limited herein. In the embodiments of the present application, optionally, the context environment can be extracted from the multiple interactions with the agent.

[0050] On this basis, it needs to be explained that because the RBAC model lacks the ability to perceive the context environment, it leads to the inability to achieve fine-grained authorization when using the RBAC model to authorize, such as authorizing the agent to access the data table A, but in fact the agent can only be authorized to access part of the fields in the data table A, at which time there is also a risk of unauthorized access. Therefore, the embodiments of the present application can further reduce the risk of unauthorized access by considering the context environment.

[0051] As can be seen from the above, these access permission features can be directly extracted from the access permission request, or can be obtained by analyzing some information in the access permission request, which is not specifically limited herein.

[0052] S120, determining the target access permission of the agent to the database according to the access permission feature.

[0053] The target access permission can be understood as an access permission currently available to the agent for the database, and the target access permission is determined according to the access permission feature. For example, if it is determined according to the access permission feature that the agent can access the database, the target access permission is determined. For example, the initial access permission pre-assigned to the agent is adjusted according to the access permission feature to obtain the target access permission. For another example, the risk data of the agent when accessing the database is determined according to the access permission feature, and then the target access permission is determined according to the risk data. And the like, which is not limited here.

[0054] It should be noted that the access permission feature of the agent is dynamically changed, and therefore the finally determined target access permission is also dynamically changed, that is, the target access permission is more matched to the current situation of the agent, which can significantly reduce the risk of the agent accessing the database beyond the authority compared with the static access permission assigned to the agent by the RBAC model.

[0055] The technical scheme of the embodiment of the application determines the access permission feature in response to the access permission request initiated by the agent for the database, and then determines the target access permission of the agent for the database according to the access permission feature. The above technical scheme dynamically grants the target access permission to the agent by using the access permission feature, which can significantly reduce the risk of the agent accessing the database beyond the authority, thereby adapting to the dynamic authorization demand in the AI era and better ensuring the security of the database.

[0056] An optional technical scheme, the access permission feature includes a security level, and the security level is determined by the following way:

[0057] Obtain metadata for describing the first data from the database, and read the permission tag from the metadata;

[0058] Determine the security level according to the permission tag.

[0059] Before introducing the above technical scheme, the application scenario thereof is exemplarily described. For example, taking the first data as a data object, when the first data is persisted in the database, the permission tag injection layer can be used to automatically add a permission tag to the first data in the database storage engine layer. The permission tag can at least represent information related to the security level of the first data, such as security level, sensitivity or confidentiality, and the like. On this basis, it can also represent which level of agent can access the first data. Then, the permission tag is stored in the metadata corresponding to the first data (i.e. the data for describing the first data), so that the access permission control of the first data can be performed according to the permission tag in the subsequent data access and management process.

[0060] Therefore, the metadata can be obtained from the database, and then the permission tag can be read from the metadata, and then the security level can be determined according to the permission tag, so that the accurate determination of the security level is realized.

[0061] Another optional technical solution, after determining the target access permission of the intelligent agent to the database, the above-mentioned database access permission determination method further comprises:

[0062] generating a token corresponding to the access permission request, and returning the token to the intelligent agent, and storing the token and the target access permission in pairs;

[0063] In response to the access request initiated by the intelligent agent carrying the token, determining the second data requested by the intelligent agent to access the database, and verifying whether the intelligent agent can access the second data based on the stored target access permission corresponding to the token, and allowing or rejecting the intelligent agent to access the second data according to the verification result.

[0064] Wherein, generating a token corresponding to the access permission request, it can be understood that, because the token corresponds to the access permission request, it is a dynamic token (i.e. temporary token), not a permanent token, which helps to better ensure the security of the database.

[0065] On this basis, for the token generation process, it can be realized based on various ways, for example, generating a token based on the identity of the intelligent agent; for example, the token can be generated in combination with the identity tag and the current timestamp or the initiation time, so as to ensure the uniqueness of the token; and the like. Here is an example of token generation, see Figure 2 , using the identity and the current timestamp accurate to seconds to combine a string, and then performing hash operation on the string through hash algorithm to obtain an N-bit hash value as entropy value, so as to ensure the uniqueness and randomness of the entropy value. Further, the first M bytes are intercepted from the generated N-bit entropy value, and the M bytes of data are converted into readable string by using preset coding, and the string is the generated token, so that the token can be stored and transmitted in a suitable format.

[0066] Further, the generated token is returned to the intelligent agent, so that the intelligent agent can access the database carrying the token; and the token and the determined target access permission are stored in pairs, which is taken as an example of key-value storage, the token can be stored as a key and the access policy (AccessPolicy) is stored as a value in a preset format, and on this basis, the expiration time of the key-value pair can be set (for example Figure 2 300s in the above), which can be the expiration time specified in the target access permission, so as to facilitate the subsequent management and verification of the token.

[0067] Further, in response to the access request, which can be understood as a request for accessing the database initiated by the agent carrying the token, the second data requested by the agent for the database is determined, which can be the same data as the first data described above, can be part of the first data, for example, the first data is table A, and the second data is the data under some fields in table A, or can be different data, which can occur in the case of token hijacking by other objects to access the database. Then, in the case of storing the corresponding token, the agent can be verified whether it can access the second data based on the target access permission corresponding to the token, and the agent is allowed or refused to access the second data according to the obtained verification result. For example, taking the above A example, it is assumed that the target access permission indicates that the agent is only allowed to access the data under X field and Y field in table A, and the second data is the data under Z field, and the verification result is refusal at this time. On this basis, in addition to the target access permission, the agent can be further verified whether it can access the second data in combination with the expiration time, further ensuring the security of the database. Figure 2

[0068] The above technical solution uses the token to verify the access permission of the database, which can effectively ensure the security of the database.

[0069] Figure 3 is a flowchart of another database access permission determination method provided in the embodiments of the present application. The present embodiment is optimized on the basis of the above technical solutions. In the present embodiment, the access permission feature can include a target credibility, and the determination of the access permission feature includes: obtaining an initial credibility of the agent; determining a behavior feature of a historical access behavior, wherein the historical access behavior is an access behavior of the agent to the database before initiating the access permission request; and adjusting the initial credibility according to the behavior feature to determine the target credibility. Wherein, the same or corresponding terms as in the above embodiments are not repeated here.

[0070] Referring to Figure 3 , the method of the present embodiment can specifically include the following steps:

[0071] S210, in response to the access permission request initiated by the agent to the database, obtaining an initial credibility of the agent.

[0072] The initial credibility can be understood as the initial credibility given to the agent, which can be dynamic or static, which can be set according to actual needs, and is not limited here.

[0073] S220, determining a behavior feature of a historical access behavior, wherein the historical access behavior is an access behavior of the agent to the database before initiating the access permission request.​

[0074] The historical access behavior can be understood as an access behavior of the agent to the database before initiating the access permission request, and can represent at least one of information such as when the agent accesses the database, what data in the database is accessed, whether the database is successfully accessed, and when the database is logged in for accessing the database, which is related to the actual situation and is not specifically limited here.

[0075] The behavior feature is extracted from the historical access behavior. On this basis, in combination with the application scenarios that the embodiments of the present application can involve, the behavior feature can be the access frequency and / or the time elapse amount, and of course can also be other behavior features, which are not specifically limited here.

[0076] S230, adjusting the initial credibility according to the behavior feature to determine the target credibility of the agent.

[0077] The behavior feature determined in the above step can represent the adjustment direction of the initial credibility, such as increasing, maintaining or decreasing, and further can represent information such as adjustment amplitude or adjustment ratio, so that the initial credibility can be adjusted according to the behavior feature to obtain the target credibility.

[0078] On this basis, the above S220 and S230 can be based on a learning model of the historical access behavior pre-trained, so that the target credibility can be obtained according to the output result of the learning model.

[0079] S240, determining the target access permission of the agent to the database according to the access permission feature, wherein the access permission feature includes the target credibility, and at least one of the following three items:

[0080] The security level preset for the first data requested by the agent to access the database, the operation type used by the agent when accessing the first data to operate the first data, and the context environment of the agent corresponding to the access permission request.

[0081] The technical scheme of the embodiments of the present application analyzes the historical access behavior of the agent to obtain the behavior feature, and then adjusts the initial credibility of the agent according to the behavior feature, thereby realizing accurate determination of the target credibility accurately matched with the current situation of the agent.

[0082] An optional technical scheme adjusts the initial credibility according to the behavior feature to determine the target credibility, comprising:

[0083] Adjusting the initial credibility according to the access frequency penalty term and / or the time decay term to determine the target credibility;

[0084] The access frequency penalty term is determined according to the access times in the behavior characteristics, and the access times are the number of times of accessing the database by the agent in a historical time window corresponding to the access permission request;

[0085] The time decay term is determined according to the time elapse in the behavior characteristics, and the time elapse is the time difference between the login authentication time and the initiation time of the access permission request, and the login authentication time is the stored time of authenticating the historical login behavior, and the historical login behavior is the behavior of the agent logging into the database for accessing the database before the initiation time.

[0086] The behavior characteristics can include the access times and / or the time elapse. Specifically,

[0087] The historical time window can be understood as a time window corresponding to the access permission request, and the time in the time window is earlier than the initiation time of the access permission request. For example, the time window can be 1 hour or 2 hours, etc. compared to the initiation time, which can be set according to actual needs, and is not limited here. The access times can be understood as the number of times of accessing the database by the agent in the time window. Generally, the more the number of times, the more frequently the agent accesses the database, and the greater the probability of abnormal access behavior, and the lower the credibility of the agent.

[0088] The historical login behavior can be understood as the login behavior of the agent logging into the database for accessing the database before the initiation time, especially the login behavior whose corresponding login authentication time is stored. For example, the agent requests to log into the database, and authenticates the login behavior this time, and if the authentication is successful, the agent is allowed to log into the database to access the database. On this basis, the agent may have accessed the database multiple times before the initiation time, which means that the agent may have logged into the database multiple times, and further means that the agent may have performed multiple authentication operations, but it should be noted that not every authentication operation will store the authentication time, only part of the authentication time, especially one authentication time, will be stored. The stored authentication time is the login authentication time described in the present technical solution, and the login behavior corresponding to the login authentication time is the historical login behavior described in the present technical solution. In the present technical solution, optionally, the first login authentication time can be stored, and the first login authentication time can be the login authentication time triggered by the agent for the first time in a preset time period, etc. which can be set according to actual needs, and is not limited here. The time elapse can be understood as the time difference between the login authentication time and the initiation time. Generally, the greater the time difference, the lower the credibility of the agent.

[0089] The access frequency penalty term is determined according to the access frequency, and according to the foregoing, the more the access frequency, the greater the penalty degree of the initial credibility represented by the access frequency penalty term. In addition, the time decay term is determined according to the time difference, and according to the foregoing, the greater the time difference, the greater the decay degree of the initial credibility represented by the time decay term. Further, the initial credibility can be adjusted according to the access frequency penalty term and / or the time decay term to obtain the target credibility.

[0090] The above technical solution proposes a credibility adjustment method with time decay and / or access frequency penalty, thereby realizing dynamic adjustment of credibility and further realizing dynamic authorization.

[0091] On this basis, optionally, the access frequency penalty term is determined by the following steps:

[0092] A preset access frequency penalty coefficient is obtained, and an access frequency penalty term is determined according to the access frequency and the access frequency penalty coefficient; wherein the access frequency penalty coefficient is related to the proportion of the initial credibility decayed each time the agent accesses the database within the historical time window.

[0093] The access frequency penalty coefficient can be understood as a coefficient related to the penalty degree associated with the access frequency; alternatively, it can also be understood as a coefficient related to the proportion of the initial credibility decayed each time the agent accesses the database within the historical time window. In the present technical solution, optionally, the coefficient is greater than 0, and the greater the coefficient, the heavier the penalty, that is, the faster the decay. For example, if the access frequency penalty coefficient is 0.05, it means that the weight is reduced by 5% each time the database is accessed.

[0094] The access frequency penalty term is determined according to the access frequency and the access frequency penalty coefficient, for example, the product of the access frequency and the access frequency penalty coefficient can be used as the access frequency penalty term; alternatively, the sum of the product and a first preset value can be used as the access frequency penalty term; alternatively, the division result of a second preset value and the sum can be used as the access frequency penalty term; and the like, which is not limited herein.

[0095] The above technical solution realizes accurate determination of the access frequency penalty term by using the access frequency penalty coefficient in combination with the access frequency.

[0096] Alternatively, the time decay term is determined by the following steps:

[0097] A preset time decay factor is obtained, and a time decay term is determined according to the time difference and the time decay factor; wherein the time decay factor is related to the proportion of the initial credibility decayed per unit time, and the unit time and the time difference have the same time unit.

[0098] Here, "unit time" can be understood as a fixed-length time period, such as 1 minute, 1 hour, or 1 day, which can be set according to actual needs and is not specifically limited here. Typically, the unit time can be preset, and then the time difference can be calculated based on this unit time. For example, taking 1 hour as the unit time, the time difference can be calculated using hours as the time unit. Based on this, the time decay factor can be understood as a factor related to the proportion by which the initial confidence decays over each unit of time. In this technical solution, optionally, this factor is greater than 0; the larger the factor, the faster the decay. For example, taking 1 hour as the time unit, if the time decay factor is 0.1, it means that it decays by 10% every hour.

[0099] The time decay term is determined based on the amount of time elapsed and the time decay factor. For example, the product of the amount of time elapsed and the time decay factor, 1, can be used as the time decay term; the product of the third preset value and the product 1, 2, can be used as the time decay term; the product 2 can be used as an exponent for power operation, and the result of the power operation can be used as the time decay term; and so on. No specific limitation is made here.

[0100] The above technical solution achieves accurate determination of the time decay term by utilizing the time decay factor and combining it with the amount of time elapsed.

[0101] To provide a more vivid understanding of the various technical solutions in the embodiments of this invention, specific examples are provided below. For instance, the target credibility can be determined using the following mathematical model:

[0102] P(t)=P0·e^(-λt)·1 / (1+k·N);

[0103] Where P(t) represents the target credibility, P0 represents the initial credibility, λ represents the time decay factor, t represents the time elapsed, k represents the access frequency penalty coefficient, and N represents the number of accesses.

[0104] Based on this, for example, P0 = 0.9, λ = 0.15 / hour, t = 4 hours, k = 0.03, N = 8 times, then the time decay term is e^(-0.15×4) = e^(-0.6) ≈ 0.5488, the frequency penalty term is 1 / (1+0.03×8) = 1 / 1.24 ≈ 0.8065, and thus P(t) = 0.9×0.5488×0.8065 ≈ 0.397.

[0105] The technical solution described above describes access authority, specifically a mathematical model of initial trust degree naturally decaying over time and access frequency, and accurate determination of target trust degree based on the mathematical model.

[0106] Figure 4 is a flowchart of another database access authority determination method provided in the embodiments of the present application. The present embodiment is optimized based on the above technical solutions. In the present embodiment, optionally, according to the access authority feature, the target access authority of the agent to the database is determined, including: according to the access authority feature, determining the risk data of the agent accessing the database; in the case of determining that the agent can access the database according to the risk data, determining the target access authority of the agent to the database according to the access authority request. Wherein, the same or corresponding terms in the above embodiments are not repeated here.

[0107] Referring to Figure 4 , the method of the present embodiment can specifically include the following steps:

[0108] S310, in response to the access authority request initiated by the agent to the database, determining the access authority feature;

[0109] Wherein, the access authority feature includes at least one of the following:

[0110] The target trust degree of the agent, the security level preset for the first data requested by the agent to access the database, the operation type adopted by the agent when accessing the first data to operate the first data, and the context environment of the agent corresponding to the access authority request.

[0111] S320, according to the access authority feature, determining the risk data of the agent accessing the database.

[0112] Wherein, the risk data can represent the risk level brought to the database when the agent accesses the database, in the embodiments of the present application, optionally, the data can be represented in various ways, for example, it can be a score or a level, etc., which can be set according to actual needs, which is not limited here. For example, referring to Figure 5 , here, taking the risk score as an example to represent the risk data, the access authority feature (i.e. Figure 5 four-dimensional authority feature in ) can include (D_{cls},A_{trust},O_{type},C_{ctx}), wherein D_{cls} represents the security level, A_{trust} represents the target trust degree (i.e. Figure 5wherein A_{trust} represents the AI trust score, O_{type} represents the operation type, and C_{ctx} represents the context environment, the risk score can be calculated by the risk evaluator as f(D_{cls}, A_{trust}, O_{type}, C_{ctx}).

[0113] In the case where it is determined according to the risk data that the agent can access the database, the target access right of the agent to the database is determined according to the access right request.

[0114] In the case where it is determined according to the risk data that the agent can access the database, the target access right of the agent to the database is determined according to the access right request.

[0115] In the case where it is determined according to the risk data that the agent can access the database, the target access right of the agent to the database is determined according to the access right request.

[0116] The technical scheme of the embodiment of the application assesses the risk data of the agent accessing the database through the access right feature, and then determines the target access right according to the access right request in the case where it is determined according to the risk data that the agent can access the database, thereby further ensuring the safe access of the database.

[0117] An optional technical scheme, the access right feature comprises a context environment, the risk data of the agent accessing the database is determined according to the access right feature, comprising:

[0118] A context policy corresponding to the access right request is obtained.

[0119] The compliance of the database access process corresponding to the context environment is determined according to the context policy, and the risk data of the agent accessing the database is determined according to the compliance.

[0120] The context policy corresponds to the access permission request and can represent whether the database access process performed in the context environment corresponding to the access permission request is compliant. For example, assuming that the context policy limits that the database can only be accessed during working hours, the compliance of the database access process performed in the time represented by the context environment can be determined according to the context policy.

[0121] Therefore, the context policy can be obtained, and the compliance of the database access process corresponding to the context environment can be determined according to the context policy, and then the risk data can be determined according to the compliance.

[0122] The above technical solution can determine whether the corresponding database access process is compliant by combining the context policy and the context environment, and can accurately determine the risk data based on this.

[0123] Another optional technical solution determines the target access permission of the agent to the database according to the access permission request, including:

[0124] Determining an initial access permission corresponding to the access permission request;

[0125] Adjusting the initial access permission according to the risk data to determine the target access permission of the agent to the database.

[0126] The initial access permission can be understood as the access permission corresponding to the access permission request. For example, the access permission requested by the access permission request can be taken as the initial access permission; for another example, an identity can be extracted from the access permission request, and then a role previously bound to the agent (i.e., the agent initiating the access permission request) with the identity is obtained, and the access permission to the database previously assigned to the role is taken as the initial access permission, which is similar to the access permission assignment mode of the RBAC model; of course, the initial access permission can also be determined based on other modes, which can be selected according to actual needs, and is not limited here.

[0127] Further, since the initial access permission is the access permission initially assigned to the agent, and the risk data can represent the risk level of the agent accessing the database, the initial access permission can be adjusted according to the risk data, for example, the initial access permission is expanded or recovered to obtain the target access permission, so as to ensure the safe access of the database.

[0128] The above technical solution determines the initial access permission, and adjusts the initial access permission according to the risk data, so that the target access permission obtained can effectively ensure the safe access of the database.

[0129] On this basis, in order to better understand the above-mentioned various technical solutions as a whole, the following will be exemplarily described from the perspective of a system that can implement the above-mentioned database access permission determination method.

[0130] Exemplarily, referring to Figure 6a , the system has a data plane and a control plane, the data plane has a permission label injection layer, a request interceptor and a dynamic authorization gateway deployed thereon, and the control plane has a policy loader, a credential manager and a risk evaluator deployed thereon, which are described in detail in Figure 6c .

[0131] Further, the data plane and the control plane can have Figure 6b the interaction relationship shown. Specifically, the control plane issues a policy to the data plane, which can be a context policy or other policies required to be applied in the database access permission determination process, such as an RBAC policy, etc., which is related to the actual situation and is not specifically limited here. Then, the data plane reports a state, which can be the state of the database itself and / or the agent accessing the database, etc., which is related to the actual situation and is not specifically limited here. Then, the control plane can update and issue the policy according to the state, and the cycle is repeated, thereby ensuring the matching between the policy and the state, and further ensuring the accuracy of the target access permission determination.

[0132] On this basis, referring to Figure 6c , the AI agent (i.e., the intelligent agent) carries an AI credential (i.e., an identity) to initiate an access permission request to the dynamic authorization gateway. Then, the dynamic authorization gateway parses the AI credential in the access permission request, and in the case of verifying that the AI credential is compliant by using the credential manager, the four-dimensional permission features (i.e., security level, AI trust score, operation type and context environment) are extracted by responding to the access permission request, thereby realizing the application of multi-modal features.

[0133] Further, the policy decision engine loads the context policy corresponding to the access permission request by using the policy loader, and determines the compliance of the context environment by using the context policy; and adjusts the AI trust score (equivalent to the initial trust score described above) to obtain a target trust score by using the permission decay model (i.e., the mathematical model described above). Further, the risk score can be calculated according to the security level, the target trust score, the operation type and the compliance by using the permission calculation engine (i.e., the risk evaluator), to obtain a risk level. If the risk level is low risk, a token is directly generated; if the risk level is medium risk, an approval flow can be triggered for manual review, and a token is generated after the review is passed; if the risk level is high risk, the access permission request can be directly intercepted by using the request interceptor, and an alarm is given.

[0134] In the case of generating a token, the token is returned to the AI agent, so that the AI agent can carry the token to initiate an access request to the database. At this time, the target access permission corresponding to the token can be obtained in response to the access request, and the data accessed by the agent (i.e., the second data in the above) is verified according to the target access permission and the permission label injected and stored into the metadata by the permission label injection layer in advance, specifically, fine-grained verification can be performed by using the corresponding column-level permission and / or row-level permission, thereby realizing fine-grained access control. Finally, according to the verification result, the agent is allowed or refused to access the data.

[0135] The above examples realize dynamic authorization effect by using four-dimensional permission features and a permission attenuation model.

[0136] Figure 7 The structure block diagram of the database access permission determination apparatus provided by the embodiments of the present application is provided, and the apparatus is used to execute the database access permission determination method provided by any of the above embodiments. The apparatus and the database access permission determination method of each of the above embodiments belong to the same inventive concept, and the details not described in the embodiments of the database access permission determination apparatus can be referred to the embodiments of the database access permission determination method. Referring to Figure 7 The apparatus is configured on a server, and the server is applied in cooperation with a database. The apparatus comprises an access permission feature determination module 410 and a target access permission determination module 420.

[0137] The access permission feature determination module 410 can be used to determine the access permission feature in response to an access permission request initiated by an agent to a database.

[0138] The target access permission determination module 420 can be used to determine the target access permission of the agent to the database according to the access permission feature. The access permission feature comprises at least one of the following:

[0139] The target credibility of the agent, the security level preset for the first data requested by the agent to the database, the operation type adopted by the agent when operating the first data by accessing the first data, and the context environment of the agent corresponding to the access permission request.

[0140] Optionally, the access permission feature comprises the target credibility, and the access permission feature determination module 410 can comprise:

[0141] An initial credibility acquisition sub-module is configured to acquire the initial credibility of the agent.

[0142] A behavior feature determination sub-module is configured to determine the behavior feature of the historical access behavior, wherein the historical access behavior is the access behavior of the agent to the database before the access permission request is initiated.

[0143] a target credibility determination sub-module, configured to adjust the initial credibility according to the behavior feature to determine a target credibility.

[0144] On this basis, the target credibility determination sub-module can comprise:

[0145] a target credibility determination unit, configured to adjust the initial credibility according to the access frequency penalty term and / or the time decay term to determine the target credibility.

[0146] The access frequency penalty term is determined according to the number of accesses in the behavior feature, and the number of accesses is the number of accesses of the agent to the database within a historical time window corresponding to the access permission request.

[0147] The time decay term is determined according to the time elapsed in the behavior feature, and the time elapsed is the time difference between the login authentication time and the initiation time of the access permission request, and the login authentication time is the stored time for authenticating the historical login behavior, and the historical login behavior is the behavior of the agent logging into the database for accessing the database before the initiation time.

[0148] On this basis, an optional access frequency penalty term is determined by the following unit:

[0149] an access frequency penalty term determination unit, configured to obtain a preset access frequency penalty coefficient, and determine the access frequency penalty term according to the number of accesses and the access frequency penalty coefficient.

[0150] The access frequency penalty coefficient is related to the proportion of the initial credibility decayed by the agent accessing the database once within the historical time window.

[0151] Another optional time decay term is determined by the following unit:

[0152] a time decay term determination unit, configured to obtain a preset time decay factor, and determine the time decay term according to the time elapsed and the time decay factor.

[0153] The time decay factor is related to the proportion of the initial credibility decayed per unit time, and the unit time and the time difference have the same time unit.

[0154] Optionally, the target access permission determination module 420 can comprise:

[0155] a risk data determination sub-module, configured to determine risk data of the agent accessing the database according to the access permission feature.

[0156] The target access permission determination submodule is configured to, in a case where it is determined according to the risk data that the agent can access the database, determine, according to the access permission request, a target access permission of the agent to the database.

[0157] On this basis, optionally, the access permission feature comprises a context environment, and the risk data determination submodule comprises:

[0158] The context policy acquisition unit is configured to acquire a context policy corresponding to the access permission request.

[0159] The risk data determination unit is configured to determine, according to the context policy, compliance of a database access process corresponding to the context environment, and determine, according to the compliance, risk data of the agent accessing the database.

[0160] Optionally, the target access permission determination submodule comprises:

[0161] The initial access permission determination unit is configured to determine an initial access permission corresponding to the access permission request.

[0162] The target access permission determination unit is configured to adjust the initial access permission according to the risk data, to determine the target access permission of the agent to the database.

[0163] On this basis, optionally, the initial access permission determination unit is specifically configured to:

[0164] extract an identity from the access permission request, and acquire a role that is bound in advance to the agent with the identity;

[0165] determine, as the initial access permission, an access permission to the database that is assigned to the role.

[0166] Optionally, the database access permission determination apparatus can further comprise:

[0167] The target access permission storage module is configured to, after determining the target access permission of the agent to the database, generate a token corresponding to the access permission request, return the token to the agent, and store the token and the target access permission in pairs.

[0168] The second data access module is configured to, in response to an access request initiated by the agent carrying the token, determine second data requested by the agent to access the database, verify whether the agent can access the second data based on the stored target access permission corresponding to the token, and allow or reject the agent to access the second data according to a verification result obtained.

[0169] The database access permission determination apparatus provided by the embodiment of the present application determines the access permission feature in response to the access permission request initiated by the agent for the database through the access permission feature determination module, and then determines the target access permission of the agent for the database according to the access permission feature through the target access permission determination module. The above apparatus dynamically grants the target access permission to the agent by using the access permission feature, and the dynamic authorization process can significantly reduce the risk of the agent accessing the database beyond the authority, thereby adapting to the dynamic authorization demand in the AI era and better ensuring the security of the database.

[0170] The database access permission determination apparatus provided by the embodiment of the present application can execute the database access permission determination method provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of executing the method.

[0171] It should be noted that, in the embodiments of the above-mentioned database access permission determination apparatus, each unit and module included is only divided according to the function logic, but is not limited to the above-mentioned division, as long as the corresponding function can be realized; in addition, the specific name of each functional unit is only for the convenience of mutual differentiation, and does not serve to limit the protection scope of the present application.

[0172] Figure 8 A structural schematic diagram of a server 10 that can be used to implement embodiments of the present application is shown. The server is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The server can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headgear, eyewear, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0173] As shown in Figure 8 The server 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the server 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0174] A plurality of components in the server 10 are connected to the I / O interface 15, which can include: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the server 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0175] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the database access permission determination method.

[0176] In some embodiments, the database access permission determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the server 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the database access permission determination method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the database access permission determination method by any other appropriate means, such as by means of firmware.

[0177] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0178] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be implemented on general purpose computers, special purpose computers, or other programmable data processing apparatus to produce the functions / acts specified in the flow diagrams and / or block diagrams. Computer programs can be applied to input data to perform the functions of the present application and to generate output information. The output information can be applied to one or more output devices such as a display screen, printer, storage, etc. These functions / acts performed by the computer programs are referred to as being computer-executed. Computer programs, also referred to as programs, software, software applications, applications, components, or code, can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed by a host machine, a server, a client, or other computing device.

[0179] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal form, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0180] To provide for interaction with a user, the systems and techniques described here can be implemented on a server having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the server. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0181] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0182] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0183] In particular, the processes described above with reference to the flow charts can be implemented in a computer software program in accordance with embodiments of the application. For example, embodiments of the application include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program comprising program code for executing the methods illustrated by the flow charts. In such embodiments, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-described functions defined in the methods of embodiments of the application are performed.

[0184] It should be understood that various forms of flow shown above can be used, with steps reordered, added, or removed. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, without limitation herein, as long as the desired results of the claimed technical solutions are achieved.

[0185] The specific embodiments described above are not intended to be limiting, but rather to illustrate the general principles of the application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and alternatives can be made and equivalents employed without departing from the spirit and scope of the application. Any modification, equivalent substitution, and improvement made within the spirit and principle of the application shall fall within the scope of the application.

Claims

1. A database access authority determination method characterized by comprising: The method is applied to a server cooperating with a database, and the method comprises: In response to an access permission request initiated by an agent for the database, determining an access permission feature; According to the access permission feature, determining a target access permission of the agent for the database; The access permission feature comprises at least one of the following: The target trustworthiness of the agent, a security level preset for first data requested by the agent to access the database, an operation type adopted by the agent when accessing the first data to operate the first data, and a context environment of the agent corresponding to the access permission request.

2. The method of claim 1, wherein, The access permission feature comprises the target trustworthiness, and the determination of the access permission feature comprises: Obtaining an initial trustworthiness of the agent; Determining a behavior feature of a historical access behavior, wherein the historical access behavior is an access behavior of the agent for the database before the access permission request is initiated; According to the behavior feature, adjusting the initial trustworthiness to determine a target trustworthiness.

3. The method of claim 2, wherein, The adjustment of the initial trustworthiness according to the behavior feature to determine the target trustworthiness comprises: According to an access frequency penalty term and / or a time decay term, adjusting the initial trustworthiness to determine the target trustworthiness; The access frequency penalty term is determined according to the number of accesses in the behavior feature, and the number of accesses is the number of accesses of the agent to the database within a historical time window corresponding to the access permission request; The time decay term is determined according to the time elapsed in the behavior feature, and the time elapsed is the time difference between the login authentication time and the initiation time of the access permission request, and the login authentication time is the time stored for authenticating a historical login behavior, and the historical login behavior is the behavior of the agent logging into the database to access the database before the initiation time.

4. The method of claim 3, wherein, The access frequency penalty term is determined by the following steps: Obtaining a preset access frequency penalty coefficient, and determining the access frequency penalty term according to the number of accesses and the access frequency penalty coefficient; The access frequency penalty coefficient is related to the proportion of the initial trustworthiness decayed each time the agent accesses the database within the historical time window.

5. The method of claim 3, wherein, The time decay term is determined by the following steps: Obtaining a preset time decay factor, and determining the time decay term according to the time elapsed and the time decay factor; The time decay factor is related to the proportion of the initial trustworthiness decayed per unit time, and the unit time and the time difference have the same time unit.

6. The method of claim 1, wherein, The security level is determined by the following method: Obtaining metadata for describing the first data from the database, and reading a permission tag from the metadata; According to the permission tag, determining the security level.

7. The method of claim 1, wherein, According to the access permission feature, determining a target access permission of the agent for the database, comprises: According to the access permission feature, risk data of the intelligent agent accessing the database is determined; In a case where it is determined according to the risk data that the intelligent agent can access the database, target access permission of the intelligent agent for the database is determined according to the access permission request.

8. The method of claim 7, wherein, The access permission feature includes the context environment, and the determination of the risk data of the intelligent agent accessing the database according to the access permission feature includes: An context policy corresponding to the access permission request is acquired; According to the context policy, compliance of a database access process corresponding to the context environment is determined, and risk data of the intelligent agent accessing the database is determined according to the compliance.

9. The method of claim 7, wherein, The determination of the target access permission of the intelligent agent for the database according to the access permission request includes: An initial access permission corresponding to the access permission request is determined; According to the risk data, the initial access permission is adjusted to determine the target access permission of the intelligent agent for the database.

10. The method of claim 9, wherein, The determination of the initial access permission corresponding to the access permission request includes: An identity is extracted from the access permission request, and a role bound in advance for the intelligent agent with the identity is acquired; Access permission for the database assigned to the role is taken as the initial access permission.

11. The method of claim 1, wherein, After the determination of the target access permission of the intelligent agent for the database, the following further includes: A token corresponding to the access permission request is generated, and the token is returned to the intelligent agent, and the token and the target access permission are stored in pairs; In response to an access request initiated by the intelligent agent carrying the token, second data requested by the intelligent agent for the database is determined, whether the intelligent agent can access the second data is verified based on the target access permission stored corresponding to the token, and the intelligent agent is allowed or refused to access the second data according to a verification result obtained.

12. A database access right determination apparatus characterized by comprising: Configured on a service end, the service end is applied in cooperation with a database, and the device includes: An access permission feature determination module configured to determine an access permission feature in response to an access permission request initiated by an intelligent agent for the database; A target access permission determination module configured to determine target access permission of the intelligent agent for the database according to the access permission feature. The access permission feature includes at least one of the following: Target credibility of the intelligent agent, a security level preset for first data requested by the intelligent agent for the database, an operation type adopted by the intelligent agent when operating the first data by accessing the first data, and a context environment of the intelligent agent corresponding to the access permission request.

13. A server, characterized by The device includes: At least one processor; And A memory connected in communication with the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the database access permission determination method according to any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling a processor to perform the database access permission determination method according to any one of claims 1-11 when executed by the processor.

15. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program enables the database access permission determination method according to any one of claims 1-11 when executed by a processor.

Images