Patent Abstract:
An authorisation privilege for an access request is inferred when no explicit privilege exists. The inference can be performed by way of mining occurrence patterns or derived from user hierarchy, profile, click history, transaction history or role. For any access request, the respective explicit privilege or inferred privilege is verified by the database or security administrator before the access request is permitted. Conditions expressed in an access policy are evaluated on the occurrence of predefined events. The events extend beyond user access requests, and include external events, composite events and access of a referential type. The access policy is framed in ‘event, condition, access enforcement’ terminology. The access control rules can be parameterised and can be instantiated by data obtained from inference rules associated with the conditions of the policy. The conditions have an evaluation component and an inference component. The access privileges supported are: read, write and indirect read. An indirect read operation typically allows a user qualified access to one or more portions of a database, but not the entire database.

Full Description:
FIELD OF THE INVENTION 
   The invention relates the execution of event-based database access requests. 
   BACKGROUND 
   Access control functionality is one of the most crucial parts of any database application. Access control privileges are generally statically defined in terms of rules. An example of such an approach is taught in a paper entitled “History-based Access Control for Mobile Code”, by Guy Edjlati, Anurag Acharya and Vipin Chaudhary, published as proceedings of the ACM Conference on Computer and Communications Security 1998: 28–48. The key idea behind history-based access control is to maintain a selective history to improve the differentiation between safe and potentially dangerous requests. An example is a policy that allows a user to connect to a remote site if and only if it has neither tried to open a local file that it has not created, nor tried to modify a file it has created, nor tried to create a sub-process. 
   Because these rules (or rights), exemplified by Edjlali et al are fixed, they are not reactive to the changing state of the database. Such functionality can be added using an event-based access control system. In an event-based access control system, contingent access policies are used. Key to an event-based access control system is the idea of multiple states. Events trigger state transitions. The events can be, for example, the arrival of an object, the completion of a process, or the passage of time (such as a deadline). In each state, users may have different access rights to the system: at one state time User A has read and write access, but at another state time may only have read access. 
   A particular problem arises when an access request does not have a recognisable authorisation (or permission). Such a request may lead to refusal from the viewpoint of a database administrator. 
   A known arrangement for dealing with a similar problem is taught in U.S. Pat. No. 6,061,684 (Glasser et al), issued on May 9, 2000. Glasser et al teaches that in the absence of a relevant access control list, a nearest (“proximate”) ancestor element is located, and the control list of that ancestor is inherited. Specifically Glasser et al uses the example of a file system hierarchy having folders whose access permissions can be set. Each folder can, but need not, have an Access Control List. A folder&#39;s access permissions can be inherited by its descendants in the hierarchy, however—in the example given—inheritance does not proceed beyond the nearest ancestor having an ACL. If no ancestor is present, then it is possible that the access request will be refused. 
   Another issue relates to the dynamic modification of access rights. In conventional access control systems having statically defined rules, dynamism is achieved by associating conditions with the access control policy (eg. Edjlali et al). These conditions are evaluated when the user makes an access request. For an events-based access control system, on the other hand, the access rights are changed on the occurrence of predefined events. By way of example, users are ascribed roles. Roles, in turn, are mapped to rights (or privileges) according to a series of rules that are contingent upon events. An example of an event not related to a user request is data being inserted or modified in a database. 
   The conditions associated with the policy are evaluated on the occurrence of events beyond user access requests. Therefore, when a user makes repeated data access requests, that request is tested against the policy in force at the particular instant of time. The policy may have changed in the period between subsequent requests. 
   A known arrangement of this type is taught in Technical Report No. 1998.05, entitled “Fine-Grained Event-Based Access Control”, by Kenneth K. Pang, published by Lotus Development Corporation, 55 Cambridge Parkway, Cambridge, Mass. 02142. Pang describes an arrangement suited to a process having the properties of user rights depending on time and the roles the users assume. The roles depend upon the requested object (eg. a document or file). Pang has only a limited set of events that can impact on a user&#39;s rights at any one time. Specifically, it is the role the user has assumed for the document (ie. object) it wishes to access, and a time function. 
   SUMMARY 
   The invention rests on the idea of inferring an authorisation privilege for an access request when no explicit privilege exists. The inference can be performed by way of mining occurrence patterns or derived for user hierarchy, profile, click history, transaction history or role. For any access request, the respective explicit privilege or inferred privilege is verified by the database or security administrator before the access request is permitted. 
   The invention is further concerned with evaluating conditions expressed in an access policy on the occurrence of predefined events. The events extend beyond user access requests, and include external events, composite events and access of a referential type. The policy is framed in terms of: ‘event, condition, access enforcement’ terminology. 
   The access control rules can be parameterised and can be instantiated by data obtained from inference rules associated with the conditions of the policy. The conditions have an evaluation component and an inference component. 
   The access privileges supported are: read, write and indirect read. An indirect read operation typically allows a user qualified access to one or more portions of a database, but not the entire database. 

   
     DESCRIPTION OF DRAWINGS 
       FIG. 1  is a diagrammatic representation of an access control policy model. 
       FIG. 2  is a logic flow diagram of handling of a database access request. 
       FIG. 3  is a schematic diagram of a database architecture embodying the invention. 
       FIG. 4  is a logic flow diagram of one example of the inference of privileges. 
       FIG. 5  is a schematic representation of a computer system suitable for performing the techniques described with reference to  FIGS. 1 to 4 . 
   

   DETAILED DESCRIPTION 
   A method, a computer system and a computer program product are described hereinafter for the execution of event-based access control with support for inference of access rights. 
   Access Control Policy Model 
   As shown in  FIG. 1 , an access control policy  10  consists of three parts:
         Event: The event part  12  represents the condition that triggers the enforcement of the access control privileges specified in the access enforcement part  20  of the policy, subject to condition that the conditions attached with the policy hold true. Different kinds of events are supported, including temporal events, database events and events external to the system. Event expiration times dependent on the occurrence of other events in the access control system are also supported.   Condition: The condition part  14  of the policy definition can be divided into two sub-parts—
           Condition Evaluation: The condition evaluation  16  defines the conditions that need to hold true after the event occurrence for the access enforcement  20  to be executed. These conditions can be defined on the database state, system information, or it can also include user-defined Boolean functions.   Inference Rule: Each condition can be coupled with a set of inference rules  18 . These rules can be applied on data related to access control, user hierarchy, user profile etc. These access control rules are executed only if the associated condition evaluates to true. The results of these rules can be used in the access enforcement part  20  of the policy.   
           Access Enforcement: The access enforcement part  20  represents the access control actions (ie. enabling/disabling/modification/definition of access rights) that are executed if an event occurs and the associated conditions evaluate to true. The access enforcement part of the policy has parameters than can be instantiated by values obtained from the inference rules  18 . These parameters can also be instantiated by other entities like database values, external system values etc.       

   The events, evaluation process, inference rules and enforcement actions will be described further below. Table 1 gives a simplified example of an access policy. 
                               TABLE 1                   Condition   Inference   Access       Event   Evaluation   Rule   Enforcement                   There is an   The user is of type   No inference   Grant access to the       insertion in the   “GOLD” &amp; the total   Rule   stock analysis data       transaction   transaction amount       for the last six       database   done by the user       months to the gold           within last 2 months       customer           is greater than           $10,000       If there is an   No Condition/   Find the total   If the parameter       insertion in the   TRUE   business   value &gt; $1000 grant       customer       done by the   the user access to       transaction       customer and   the stock analysis       database       return it as a   data for the last 1               parameter   month                   If the parameter                   value &gt; $5000 then                   grant the user access                   to the stock analysis                   data for the last 6                   months.                    
Database Access Execution
 
     FIG. 2  is a flow diagram demonstrating, in broad terms, how database access execution is performed. The process commences with step  22 . In step  23 , an access request is made by a user. The user&#39;s request will be in relation to a specific action (eg. insertion, deletion, modification and reading of data). In step  24 , a check is made of whether an authorisation privilege is present in the access control list for the requested action. From step  25  the flow progresses to step  26  if the authorisation privilege is present, in which case the user is granted access. Otherwise, the flow proceeds to step  27  and a check is made of whether the event is defined contingent upon the execution of an inference algorithm. From step  28  the flow proceeds to step  29  to deny the user access if not so defined, otherwise the flow proceeds to step  30  for execution of the inference algorithm. In step  31  it is tested whether the access right can be inferred. If No, then, in step  32 , the access request is denied. If, however, the access right is inferred, then in step  33  the access request is sent to the DBA for approval. In step  34  the DBA approvalis checked. If the approval is not given then the flow proceeds once again to step  32  where access is denied. Otherwise, if the DBA gives approval then the flow proceeds to step  35  where access is granted. 
   Specific Database Access System Embodiment 
   A database system  40  embodying the invention is shown in  FIG. 3 . The system  40  consists of three parts—the Definition and Deployment Model  42 , the Execution Model  44  and the Access Validation Model  46 . 
   The Definition and Deployment Model  42  is used for the definition of the access control policy  10  and its deployment as a component  48  within the database  50 . The Execution Model  44  detects the occurrence of events. It also checks the truth-value of the conditions attached to the policies and depending on the truth-value, it executes the inference rules  18  as well as the access enforcement part  20  of the policy  10 . The Access Validation model  46 , provides an interface to the end-users  52  to access data from the underlying databases or information repositories  54 . 
   A Data Abstraction and Notification Layer  56  provides an interface to the underlying database  50 . It also helps in the detection of events defined on the databases. The models  42 ,  44 ,  46  use this layer for communication with the database  50 . The Data Abstraction and Notification Layer  56  also provides an interface for various external systems  58 . 
   A Database Administrator  60  has an interface with the Definition and Deployment Model  42 . The policies defined by the DBA  60  are stored in an Access Control Policy database  48 , whereas the policies that are to be enforced on the Application database  54  are stored in the “Access Control List”  62 . 
   The Policy Definition User Interface  64  of the Definition and Deployment Model  42  allows the DBA  60  to define policies in an event-condition-access enforcement format. It also allows the DBA  60  to define the inference rules  18  of the policy  10 . The system  40  supports the definition of referential access type, wherein access is allowed on a data object, if a user  52  (or the same user) has an access privileges on some other (or same) data object. Such policies can be represented by defining an event marked by insertion/modification of an access right in the access control list and checking the access rights of the user on the referred data object in the condition part  14  of the policy  10 . 
   The system  40  also allows the definition of “Indirect Read” access, which is an access privilege less than “Read” access. If a user  52  has Indirect Read access on an object he/she can read that object using functions defined with the access control system. Indirect Read access can be represented like any other access privilege within the access control policy  10 . The policy defined by the DBA  60  is fed to the policy deployer  66 , which converts it into an internal storage format and stores it in the Policy database  48  using the Data abstraction and Notification layer  56 . 
   The Execution Model  44  detects the occurrence of events, validates the conditions and performs the access enforcement part  20  of the access control policy  10 . The Data Abstraction and Notification layer  56  helps the Execution Model  44  to detect the database events. The Temporal/Database Event Detector  68  incorporates functionality to detect temporal events. Events from external systems  58  are notified to the Data Abstraction and Notification layer  56 , which communicates this information to the Execution Model  44 . If an event occurrence is detected, the Condition Evaluator  72  evaluates the conditions  14  associated with the policy  10 . If the condition evaluates to true, the Inference Algorithm  74  executes the inference rules  18  attached with the condition. The data returned by the Condition Evaluator  72  and the Inference Algorithm  74  is fed to the Access Enforcement engine  70 . If the action is parameterised, then the Access Enforcement Engine instantiates the parameters with the help of the data passed to it by the Condition Evaluator  72  and the Inference Algorithm  74 . The Access Enforcement Engine  70  can instantiate the parameters using values obtained from other sources such as database or system data. The Access Enforcement Engine  70  performs the access enforcement actions such as modification of privileges, definition of new privileges, and enabling/disabling of access privileges by making changes in the Access Control List  62 . 
   The Access Validation Model  46  validates all the user data access requests against the access privileges applicable for the user. The User Data Request Interface  76  allows the user  52  to access or query data from the underlying databases  54 . The Access Validation Engine  78  validates these access requests against the access privileges present in the Access Control List  62 . If an access request does not have an authorising privilege in the Access Control List  62 , then the Access Validation Engine  78  invokes the inference/discovery algorithm  80  to determine whether access can be granted by inferring or discovering some access privileges. The Inference/Discovery Algorithm  80  acts on the data provided to it (which can be users&#39; access privileges, employee hierarchy, users&#39; profile, users click history, users transaction history) to infer the access privileges for the user. This inference/discovery process will be described in greater detail below. 
   SPECIFIC EXAMPLES 
   Specific components of the general access control methodology just described will now be considered in greater detail. 
   Inferring an Authorisation Privilege 
   Paraphrasing what was described in  FIG. 2 , if the Inference/Discovery Algorithm  80  can successfully infer the access right for the requested action, then this access privilege is given to the access coordinator or DBA  60  for validation. Once the DBA  60  validates the access privilege, the user  52  is granted access on the requested data (ie. read, write or indirect read). The Access Validation Model  46  executes the user request on the underlying data using the Data Abstraction and Notification layer  56  and returns the data to the user  52 . If access is not granted, then the system flags an error to the user  52 . 
   Consider the following examples of the inferencing process: 
   Example 1 
   Consider two companies A and B, where company A acquires the company B. As a result the databases of both the companies are merged. The access privileges need to be defined for the employees of company A on the database of company B and vice versa. The input to the Inference Algorithm  80  will consist of the hierarchy of users of both the organisations and the mapping of the hierarchy of one organisation with the other. If role R of company A maps to role R′ of company B, then the algorithm can suggest to the DBA  60  that the access rights of role R can be given to the users belonging to role R′ and vice versa, if it does not lead to a conflict. The pseudo-code algorithm for this procedure is given below:
         Input: U A : {U A   1 ,U A   2 , . . . , U A   N } Set of users of Organisation A
           U B : {U B   1 ,U B   2 , . . . , U B   N } Set of users of Organisation B   R A : {R A   1 ,R A   2 , . . . , R A   Q } Set of roles of Organisation A   R B : {R B   1 ,R B   2 , . . . , R B   Q } Set of roles of Organisation B   U R   A   i : {U A   j ,U A   k , . . . , U A   p } Set of users who belong to role R A   i .   U R   B   i : {U B   j ,U B   k , . . . , U B   p } Set of users who belong to role R B   i .   R A   i : {ar A   1 ,ar A   2 , . . . , ar A   k } Set of access rights of role R A   i      R B   i : {ar B   1 ,ar B   2 , . . . , ar B   k } Set of access rights of role R B   i      M: {(R A   i ,R B   j ) | Role R A   i  of organization A maps to Role R B   j  of organization   B} Mapping between roles of the two organizations.
 
Algorithm:
   
           ∀R A   i εR A  do
           if ∃ (R A   i ,R B   k )εM do
               ∀ ar B   1 εR B   k  find if ar B   1  ∪ R A   i  leads to a conflict   If no add ar B   1  to R A   i      
               
           ∀ R B   k εR B  do
           if ∃ (R A   j ,R B   k )εM do
               ∀ ar A   1 εR A   j  find if ar A   1  ∪ R B   k  leads to a conflict   If no add ar A   1  to R B   k      
               
               

   Example 2 
   Consider a case where a new role R 1  is being added to the set of roles of an organisation. The set of access rights ar i εR 1  is not completely defined. The inference Algorithm  80  can mine the access rights database of the organisation using association rules mining algorithms. One such algorithm is taught by Rakesh Agarwal and Ramakrishnan Srikant, in “Fast Algorithms for Mining Association Rules” in the proceedings of 20 th  International Conference on Very Large Databases, 1994), the contents of which are incorporated herein by cross-reference. These algorithms infer association rules or inference rules from the database, which represent associations or patterns between the different access rights in the organisation. The patterns that the inference algorithm is interested in are of the form “If access right ark is granted to the users of role R then 90% of times the users also have access right ar p ”. Such patterns can be found out by scanning the data or by running standard association rule mining algorithms. 
   Formally, the association rules will be of the form (ar k ,ar p ,S) which signifies that if ar k εR f  then ar p εR f  with support “S”. The association rules represent patterns that appear in the access rights database of the organization. The inference rule is based on the logic that if a pattern like (ar k ,ar p ,S) is present in the access rights database, then the chances of such a pattern repeating itself in the new role is also high. Hence using these association rules and access rights ar i  that have been defined for role R 1 , the Inference Algorithm can suggest new access rights if there exists an association rule of the form (ar i ,ar j ,S) and if ar j ∪R i  does not lead to a conflict. The pseudo code algorithm for this procedure is given below—
         Input: New role R 1  to be added to the role hierarchy
           Partial set of access rights ar i εR 1      R: {R 1 ,R 2 , . . . , R N } Set of roles of the Organisation   R i : {ar 1 ,ar 2 , . . . , ar k } Set of access rights of role R i  
 
Algorithm:
   
           Using some standard association rules or mining algorithm mine the set of access rights of all roles   Let A={a 1 ,a 2 , . . . , a c } be the set of association rules returned by the algorithm where a i ={ar i ,ar j ,support} is an association rule   Let R′⊂R 1  be the set of access rights that match the association rule antecedent   For each ar i ′εR′ do
           Find if ar j ′∪R 1  leads to a conflict, where (ar i ,ar j ,support)εA   If no conflict is detected add ar j ′ to R 1  and   Check if ar j ′ matches any of the association rules&#39; antecedent   If yes add ar j ′ to R′   
               

   Example 3 
   A different scenario where the above procedure can be used is when the access rights of a new organisation are being defined. If a company wants to start a sister enterprise, then the access rights of the new company are generally modelled on the access rights of the parent company. The Inference Algorithm  80  can mine the access rights database of the parent company and generate association rules based on the occurrence pattern of access rights. The patterns that the inference algorithm is interested in are of the form “If access right ar k  is granted to the users of role R then 90% of times the users also have access right ar p ”. Such patterns can be found out by scanning the data or by running standard association rule mining algorithms. These association rules can be used to suggest access rights for the new organisation. The database administrator  60  can define a few access rights for the roles of the new organisation. Based on these access rights and the association rules, the inference algorithm can suggest new access rights to the database administrator  60  (as was done in the earlier case). The pseudo code algorithm is given below:
         Input: Let company A be the parent company and company B be the sister concern for whom the access rights are to be defined.
           R A : {R A   1 ,R A   2 , . . . , R A   k } Set of roles of company A   ar A   k : {ar A   1 ,ar A   2 , . . . , ar A   m } Set of access rights εR A   k      R B : {R B   1 ,R B   2 , . . . , R B   L } Set of roles of new organisation
 
Algorithm:
   
           Run the association rule mining algorithms on R A      Let A={a 1 ,a 2 , . . . , a c } be the set of association rules returned by the algorithm where
           a i ={ar A   j , ar A   1 , support} is an association rule   
           For each R B   j εR B  do
           For each ar B   p εR B   j  do
               If ∃a i ={ar A   k , ar A   o ,support} &amp; a i εA &amp; ar A   k =ar B   p  then
                   Find if ar A   o ∪R B   j  leads to a conflict   If there is no conflict suggest adding ar A   o  to R B   j      
                   
               
               

   The same algorithm can also be used when the user makes an access request that does not have an access privilege attached. The procedure for this scenario is given below:
         Step 1: The inference algorithm can mine the access rights of the organisation to find association rules. The format of the association rule will be: (ar i , ar j , S), which means that if a role R has access night ar i , then the role also has access right ar j  with support ‘S’.   Step 2: If a user belonging to role R makes an access request ar p  and the user does not have this access right in the set of rights belonging to his current role R, then the Inference Algorithm  80  can be used.       

   The Inference Algorithm is given below:
         Input: R: {ar 1 ,ar 2 , . . . , ar 1 } Set of access rights associated with a role
           A: {a 1 ,a 2 , . . . , a m } Set of association rules.   a k : (ar i ,ar 1 ,support) association rule where ar i , ar 1  are access rights.   ar p : The access right that the user wants.
 
Algorithm:
   
           For each access right ar k εR do   If (∃a j |a j εA &amp; a j =(ar k ,ar p ,support)) then   If ar p ∪R does not lead to a conflict   Suggest adding ar p  to R       

   Example 4 
   Consider an example from an online e-business enterprise. The users of an e-business are classified into different categories such as “gold”, “silver”, “platinum”, depending on the amount of business given to the enterprise. The access privileges of the customers are based on the customer rating given to the user. The inference engine can be of considerable help to the DBA  60  by suggesting new access rights for users (beyond those identified by their customer rating) as a special incentive for the customers. 
   Referring now to the flowchart of  FIG. 4 , the Inference Algorithm  80  scans through the customer click stream history (step  100 ) and identifies those access requests ‘r’ that are made frequently by the customer (say c′) and are denied by the access control system (step  102 ). The Algorithm then identifies those customers ‘C’ who have access privilege (step  104 ). The Algorithm then matches the transaction history, user profile and usage patterns like the business given to the enterprise by the customer, number of items bought, type of items bought, customers referred to the enterprise of the customer c′ and customer cεC (step  106 ). If the similarity between the two patterns is within a specified threshold (step  108 ), and if the r does not lead to a conflict between the access rights of customer c′ (step  112 ), then the Inference Algorithm  80  suggests the addition of access right r to those of customer c (step  112 ), and ends (step  114 ). 
   In terms of the access policy  10 , the components are: 
                               TABLE 2                   Condition   Inference   Access       Event   Evaluation   Rule   Enforcement                   If a user makes an   No   Execute the   If the DBA 60       unsuccessful   condition   inference algorithm   agrees to the       access request       and store the result   inference, grant the               returned by the   access right to the               DBA 60 as a   user in the policy               parameter.   database 48.                    
Changing Access Rights
 
   The occurrence of events affects a user&#39;s access rights, as explained previously. Consider the following examples. 
   Case 1 
   Stated Policy: When an employee goes on leave, his access rights to confidential documents should be disabled. 
   Corresponding ECA policy: 
   
     
       
             
             
             
             
           
         
             
               TABLE 3 
             
             
                 
             
             
                 
               Condition 
                 
               Access 
             
             
               Event 
               Evaluation 
               Inference Rule 
               Enforcement 
             
             
                 
             
           
           
             
               An entry is made in 
               No condition 
               None 
               Remove all access 
             
             
               the “Leave 
                 
                 
               privileges for the 
             
             
               Database”. 
                 
                 
               employee (whose 
             
             
                 
                 
                 
               entry is made in the 
             
             
                 
                 
                 
               employee database) 
             
             
                 
                 
                 
               of type confidential. 
             
             
                 
             
           
        
       
     
   
   Therefore, whenever any entry is made in the Leave Database automatically the above policy is executed and the access rights of the employee on confidential data are disabled. 
   Case 2 
   Stated Policy: A gold user is allowed to access the stock analysis data for the last six months, if the user has done transactions worth $10,000 in the last two months or if the cumulative sum of the transactions done by all the customers referred by the gold user is more than $ 50,000. 
   There will be two corresponding policies: 
   Policy 1 
                               TABLE 4                   Condition   Inference   Access       Event   Evaluation   Rule   Enforcement                   If there is an   The user is of   No infer-   Add an access right       insertion or updation   type “gold” &amp; the   ence rule   granting access to       in the transaction   total transaction       the stock analysis       database   amount within the       data for the last six           last 2 months is       months for the gold           greater than       customer.           $10,000                    
Policy 2
 
                               TABLE 5                   Condition   Inference   Access       Event   Evaluation   Rule   Enforcement                   If there is an   The user is referred   No inference   Add an access right       insertion or up-   by a gold customer   rule.   granting access to       dation in the   &amp; the total       the stock analysis       transaction   transaction amount       data for the last six       database   of all the customers       months for the gold           referred by the gold       customer.           customer is greater           than $50,000/−                    
Parameterised Actions
 
   It was earlier stated that database access control policies can be parameterised. Examples of this will now be described. 
   Stated Policy: A customer is given access to the stock analysis data based on the following contingent conditions:
         1) If the user has done business greater than $ 1,000 then he has access to stock analysis data for the last 1 month   2) If the user has done business greater than $ 10,000 then he has access to the stock analysis data for the last 6 months   3) If the user has done business greater then $ 50,000 then he has access to the stock analysis data for the last 1 year.
 
The ECA policy is:
       

                               TABLE 6                   Condition   Inference   Access       Event   Evaluation   Rule   Enforcement                   If there is an   No condition   Find the total   If the parameter       insertion in the   evaluation/TRUE   business   value &gt; $1,000 then       customer       done by the   give access to the       transaction       customer and   user to stock       database       return it as a   analysis data for the               parameter   last 1 month                   If the parameter                   value &gt; $10,000                   then give access to                   the user to stock                   analysis data for the                   last 6 months                   If the parameter                   value &gt; $50,000                   then give access to                   the user to stock                   analysis data for the                   last 1 year.                    
Access Privileges
 
   Also as previously described, the full set of access privileges is {read, write, indirect read}. Examples for each are now given. 
   Indirect Read 
   Stated Policy: A guest user is allowed indirect access on the stocks database (but not the entire database). 
   The functions defined in the system could include: top five gainers and top five losers&#39;. The guest user can use these functions on the stocks database and read the results, but the user cannot directly access the database. Thus a guest user can only view the top five gainers and losers&#39;, but not the entire database. Hence the access privilege of guest user is less than “read”. 
   Read 
   Stated Policy: A manager is allowed to read the employee name in the Leave database after a leave application is made by any of his direct reportees&#39;. 
                               TABLE 7                   Condition       Access       Event   Evaluation   Inference Rule   Enforcement                   An entry is   No condi-   Find the manager of   Grant read access to       made in the   tion/TRUE   the employee who   the employee name       leave database       has made the leave   field of the leave               application and pass   database to the               it as a parameter   manager passed as a                   parameter to the                   leave database.                    
Write
 
   Stated Policy: A manager is allowed to write/edit the approval status in the Leave database after a leave application is made by any of his direct reportees&#39;. 
                               TABLE 8                   Condition       Access       Event   Evaluation   Inference Rule   Enforcement                   An entry is   No cond-   Find the manager of   Grant write access       made in the   ition/TRUE   the employee who   to the approval       leave database       has made the leave   status field of the               application and pass   leave database to the               it as a parameter   manager passed as a                   parameter to the                   leave database.                    
Computer Hardware and Software—User
 
     FIG. 5  is a schematic representation of a computer system  200  that can be used to perform steps in a process that implement the techniques described herein. The computer system  200  is provided for executing computer software that is programmed to assist in performing the described techniques. This computer software executes under a suitable operating system installed on the computer system  200 . 
   The computer software involves a set of programmed logic instructions that are able to be interpreted by the computer system  200  for instructing the computer system  200  to perform predetermined functions specified by those instructions. The computer software can be an expression recorded in any language, code or notation, comprising a set of instructions intended to cause a compatible information processing system to perform particular functions, either directly or after conversion to another language, code or notation. 
   The computer software is programmed by a computer program comprising statements in an appropriate computer language. The computer program is processed using a compiler into computer software that has a binary format suitable for execution by the operating system. The computer software is programmed in a manner that involves various software components, or code means, that perform particular steps in the process of the described techniques. 
   The components of the computer system  200  include: a computer  220 , input devices  210 ,  215  and video display  290 . The computer  220  includes: processor  240 , memory module  250 , input/output (I/O) interfaces  260 ,  265 , video interface  245 , and storage device  255 . 
   The processor  240  is a central processing unit (CPU) that executes the operating system and the computer software executing under the operating system. The memory module  250  includes random access memory (RAM) and read-only memory (ROM), and is used under direction of the processor  240 . 
   The video interface  245  is connected to video display  290  and provides video signals for display on the video display  290 . User input to operate the computer  220  is provided from input devices  210 ,  215  consisting of keyboard  210  and mouse  215 . The storage device  255  can include a disk drive or any other suitable non-volatile storage medium. 
   Each of the components of the computer  220  is connected to a bus  230  that includes data, address, and control buses, to allow these components to communicate with each other via the bus  230 . 
   The computer system  200  can be connected to one or more other similar computers via a input/output (I/O) interface  265  using a communication channel  285  to a network  280 , represented as the Internet. 
   The computer software program may be provided as a computer program product, and recorded on a portable storage medium. In this case, the computer software program is accessed by the computer system  200  from the storage device  255 . Alternatively, the computer software can be accessed directly from the network  280  by the computer  220 . In either case, a user can interact with the computer system  200  using the keyboard  210  and mouse  215  to operate the programmed computer software executing on the computer  220 . 
   The computer system  200  is described for illustrative purposes: other configurations or types of computer systems can be equally well used to implement the described techniques. The foregoing is only an example of a particular type of computer system suitable for implementing the described techniques. 
   CONCLUSION 
   A method, a computer system and computer software are described herein in the context of executing event-based database access requests. The problem of an access request not having an explicit authorisation privilege is addressed by inferring a privilege. The inference can be performed by mining or derived from data associated with the requesting user. 
   A particular advantage of inferring access privileges is that the task of a database administrator is simplified. Another advantage flows to users, in that access requests are less likely to be refused. This is very useful in large-scale database systems, having many users who change regularly. 
   The proposal of an event-based access control system with the ability to infer access rights allows a database administrator to grant the correct access privileges to a user at the correct point in time, thereby avoiding the need to grant superfluous access rights. Specifying the access control policy in terms of a ‘event-condition-access enforcement rules’ model better capture intended policies. By associating the inference process with the condition part of the access control policy representation and, depending upon the truth-value of the condition, enabling parameterised actions, it is possible to achieve intelligent policies not available in the prior art. 
   Various alterations and modifications can be made to the techniques and arrangements described herein, as would be apparent to one skilled in the relevant art.

Technology Classification (CPC): 8