Abstract:
A method of generating a test database from a deployed database by “sanitizing”, or removing sensitive data, is employed for recreating problem scenarios from a customer site, verifying compatibility of customer data with database version updates, and for performance testing using actual, rather than generated, database structures. In database management operations, in can be beneficial to use data generated from actual operational scenarios rather than artificially generated data created from a test pattern. Data generated from actual operation, such has at a customer site, assures compatibility with the relation patterns and record volumes employed by the customer (user). However, databases often contain sensitive information that would be inappropriate, illegal, or vulnerable in a testing environment. Accordingly, the generated test database overwrites sensitive data values with benign, or generic values while preserving the structure and relations of the data stored.

Description:
RELATED APPLICATIONS 
       [0001]    This application is related to and claims the benefit under 35 U.S.C. §119 of India Patent Application Serial Number 1534/CHE/2008, filed on Jun. 24, 2008, and entitled “GENERIC DATABASE SANITIZER,” the entire teachings of which are incorporated herein by this reference. 
       BACKGROUND 
       [0002]    Modern trends in information processing are continually storing increasing quantities of sensitive data in electronic form. With the increase in sensitive, confidential and/or personal data stored in databases adapted for remote access, the risk of intrusion to such databases presents more serious concerns. As more and more enterprises employ electronic transfer of information in lieu of more traditional paper means, however, ability to conduct electronic transactions becomes more crucial to business viability. Increased media attention to breaches of commercial databases containing sensitive information has heightened public awareness of vulnerabilities of electronic storage of such sensitive information. The result is that holders of sensitive information recognize a need to safeguard sensitive information entrusted to them, and to guard against inadvertent dissemination of sensitive information stored in computer databases under their control. 
         [0003]    In an information processing environment, therefore, there is a need to avoid inappropriate and/or inadvertent dissemination of sensitive information. However, the databases storing this information are subject to operational and maintenance activities that may inadvertently or unintentionally expose the data stored thereby. Databases are prone to defragmentation, software updates, structural and format changes, and other activities that directly manipulate database contents, and that may require transfer of the database contents to a third party. 
       SUMMARY 
       [0004]    In an information processing environment, databases often contain sensitive information, which may include confidential, sensitive, personal, or other private information of business associates such as customer, clients, vendors and others. In a database management system, it is sometimes necessary or beneficial to perform testing or interrogation of database entities. Such database management systems may undergo intrusive activities related to occurrences such as software revisions, troubleshooting operations, and performance evaluation. These intrusive activities are often more effective if employed with the actual data patterns from the database. However, the actual data often contains sensitive data that is inappropriate for use in a testing environment. For example, testing may be performed in another country where the sensitive data would be subject to different privacy laws, or the data may be observable by test operators to whom dissemination of the data would be inappropriate. 
         [0005]    However, modern database management systems that maintain the data in such databases are often complex arrangements of tables, fields and indexes. Conventional identification and overwriting of the sensitive data involves manual identification of the data and resulting patterns of relations to other database entities. Testing using the database may be performed for a variety of reasons, such as version upgrades, export, performance and scalability testing. Test databases may include either actual or generated entries for testing the database and applications that access it. 
         [0006]    In each of these scenarios, it is beneficial to employ actual data patterns generated from processing the actual data, rather than attempting to emulate data records via a test generation tool. Such synthetically generated databases may not accurately define the complex patterns of relations between database objects, and/or require substantial manual intervention to define. 
         [0007]    Conventional methodologies suffer from the shortcoming that there is no efficient way to effectively purge sensitive information from the database tables without disrupting the structure and relations of the data stored thereby. Such conventional approaches suffer from the shortcoming that lengthy and time consuming manual updates need be performed on fields of sensitive information, and are particularly unwieldy when a large database having many entries is required. Alternatively, synthetically generated “test” databases result in a scenario that does not replicate the original configuration of data objects that the testing is directed, and thus may fail to emulate certain scenarios from the “real world” database. 
         [0008]    Accordingly, configurations herein substantially overcome the shortcomings presented by conventional manual approaches by providing a generic method for eliminating sensitive or private information from databases while maintaining data integrity. Those skilled in the art may refer to the removal of sensitive information as “scrubbing” the database or data stream. Disclosed configurations include a method and implementation of a mechanism to remove sensitive data such as customer-specific information from an exported database dump, and substitute it with generic data while maintaining full data integrity and consistency. This enables usage of the database for research and development and at the same time ensures privacy of the customer data. Sensitive data values are replaced using benign values correlated to a mapping of relations to maintain the same relations with benign “scrubbed” key fields. The replaced fields are enumerated in a listing of tables and columns applicable in a generic manner to any suitable database by identifying the affected tables and columns. 
         [0009]    Configurations include a method of generating a test database from a deployed database for recreating problem scenarios from a customer site, verifying compatibility of customer data with database version updates, and performance testing using actual, rather than generated, database structures. In database management operations, in can often be beneficial to use data generated from actual operational scenarios rather than artificially generated data created from a test pattern. Data generated from actual operation, such has at a customer site, assures compatibility with the relation patterns and record volumes employed by the customer (user). However, databases often contain sensitive information that would be inappropriate, illegal, or vulnerable in a testing environment. Accordingly, the generated test database overwrites sensitive data values with benign, or generic values while preserving the structure and relations of the data stored. The generated test database is therefore “scrubbed” to allow scenarios based on the actual structure of the database without compromising sensitive data values. 
         [0010]    Certain testing and maintenance activities require customer database dumps for troubleshooting and reproducing issues in our development and testing labs. Scrubbed or sanitized customer databases are also useful to perform regression, scalability and performance tests. These customer databases are not only used by software development teams but also the related product teams for corresponding integrated products. While certain contexts permit the use of customer databases for troubleshooting and resolving customer issues, in other circumstances it is not proper to retain these databases for research and development by all these product teams due to legal reasons. This issue becomes even more acute when software development activities are outsourced to a foreign vendor such that the databases are to be shared with the vendor. There are no known tools available to remove the customer sensitive information in a satisfactory manner as describe above. Other options are to allocate few resources to manually inspect the data in various tables and update the customer specific information with generic ones, however, this approach is time consuming and resource intensive with a large installed customer base. 
         [0011]    Alternate configurations of the invention include a multiprogramming or multiprocessing computerized device such as a workstation, handheld or laptop computer or dedicated computing device or the like configured with software and/or circuitry (e.g., a processor as summarized above) to process any or all of the method operations disclosed herein as embodiments of the invention. Still other embodiments of the invention include software programs such as a Java Virtual Machine and/or an operating system that can operate alone or in conjunction with each other with a multiprocessing computerized device to perform the method embodiment steps and operations summarized above and disclosed in detail below. One such embodiment comprises a computer program product that has a computer-readable storage medium including computer program logic encoded thereon that, when performed in a multiprocessing computerized device having a coupling of a memory and a processor, programs the processor to perform the operations disclosed herein as embodiments of the invention to carry out data access requests. Such arrangements of the invention are typically provided as software, code and/or other data (e.g., data structures) arranged or encoded on a computer readable medium such as an optical medium (e.g., CD-ROM), floppy or hard disk or other medium such as firmware or microcode in one or more ROM, RAM or PROM chips, field programmable gate arrays (FPGAs) or as an Application Specific Integrated Circuit (ASIC). The software or firmware or other such configurations can be installed onto the computerized device (e.g., during operating system execution or during environment installation) to cause the computerized device to perform the techniques explained herein as embodiments of the invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]    The foregoing and other objects, features and advantages of the invention will be apparent from the following description of particular embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. 
           [0013]      FIG. 1  is a context diagram of a managed information environment suitable for use with the present invention; 
           [0014]      FIG. 2  is a flowchart of removing sensitive data from a database in the environment of  FIG. 1 ; 
           [0015]      FIG. 3  is a block diagram of processing database entities for removing sensitive data according to the flowchart of  FIG. 2   
           [0016]      FIG. 4  is a scrubbing configuration table employed for generating scripts for performing processing according to  FIG. 3 ; and 
           [0017]      FIGS. 5-8  are a flowchart of employing the table of  FIG. 4  for scrubbing the database entities as depicted in  FIG. 3 . 
       
    
    
     DETAILED DESCRIPTION 
       [0018]    The disclosed database sanitizing approach is illustrated on a configuration database of a storage area network, however the disclosed generic approach may be applied to any suitable database having tables of column-based entries, as discussed below. In the example arrangement shown, a sanitizing application operates as a database tool responsive to a user request and executed in conjunction with a database management system (DBMS) for executing a script generated by the tool. The tool employs metadata in the form of a scrubbing configuration, discussed further below, to store the tables and columns that could potentially have customer sensitive data. This tool automatically scans all the tables and columns from the metadata and generates all the necessary update scripts on demand for the given customer database. The tool executes these dynamically generated scripts and substitutes the customer sensitive data with generated benign data. This tool also keeps exception and success logs such that invalid records can be re-processed after fixing the issues. The tool thus ensures that integrity of the data is maintained by replacing sensitive field with benign information while leaving relations and keys undisturbed. 
         [0019]      FIG. 1  is a context diagram of a managed information environment suitable for use with the present invention. Referring to  FIG. 1 , in a managed information system such as a storage area network (SAN), an interconnection of nodes, or manageable entities, provides an example of a network configuration adaptable to a test database. The database  130  stores configuration information representative of the manageable entries in the SAN; a periodic discovery operation traverses the SAN and identifies the manageable entities interconnected in the SAN along with corresponding attributes. The database  130  is a relational database responsive to a database management system (DBMS)  122  for storing tables  132  indicative of the attributes of the manageable entries (stored as columns, or fields, of the manageable entities) and also storing relations  134  indicative of the relationships between the manageable entities. 
         [0020]    In the example SAN  100 , a set of storage arrays  102 - 1  . . .  102 - 5  ( 102  generally) connect to switches  104 - 1  . . .  104 - 3  ( 104  generally), and are accessible from hosts  106 - 1  . . .  106 - 2  ( 106  generally). The hosts  106 - 1  . . .  106 - 2  execute agents  110 - 1  . . .  110 - 2  ( 110  generally). The agents  110  communicate to a server  120  via a network interconnection  112 , such as the Internet. The server  120  receives configuration information  124  from the agents  110 , and stores the configuration information  124  in the database  130 . The database  130  has a set of tables  132 - 1  . . .  132 - 3  for storing the configuration information, each having one or more entries, or rows  133  ( FIG. 3 , below), of fields (columns)  136 . The database  130  also identifies the relations  134 - 1  . . .  134 - 2  ( 134  generally) between the tables  132 . The relations  134  identify corresponding rows  133  in other tables  132 , typically via a key field denoting a matching field value between the related rows  133  (entries). 
         [0021]    The disclosed sanitizing of the database  130  is performed by a sanitizing application  127  operable in conjunction with the DBMS  122  for accessing the database  130 . In the example configuration, the sanitizing application  127  generates a script  128  based on a scrubbing configuration  150 . The scrubbing configuration  150  specifies the tables  132 , columns  136  ( FIG. 3 , below), and operation to change sensitive data with fabricated data, and contains a scrubbing entry, or row, for each column  136  to be scrubbed. The operation may indicate that the data should be overwritten with fabricated, benign data, or that the entry be nullified, or truncated if the value is not needed. The DBMS  122  is responsive to the generated SQL script  128  or other database command sequence  125  for performing the sanitizing operations on the database  130 . 
         [0022]      FIG. 2  is a flowchart of removing sensitive data from a database  130  in the environment of  FIG. 1 . Referring to  FIGS. 1 and 2 , the example configuration shows the disclosed method for sanitizing a customer specific database  130  by identifying tables  132  of sensitive data, as depicted at step  200 , and identifying, columns  136 , or fields of entries in the identified tables  132  having sensitive entries, as shown at step  201 . Each field  136  having sensitive data results in a column specific update to remove sensitive data from that field  132  for all entries. In the example arrangement, a tabular form such as a spreadsheet defines the information in the scrubbing configuration  150 , shown below in  FIG. 4 , and results in a SQL script specific to each column  136 . Alternate configurations may provide other mechanisms for defining the scrubbing updates. 
         [0023]    Accordingly, for each identified column  136  of sensitive data in the identified tables  132 , the method defines a scrubbing operation for overwriting sensitive data in the respective column  136 , as depicted at step  202 , typically by replacing the value with a fabricated (computed) value, discussed further below. A common scrubbing operation is replacement of the sensitive value with a fabricated benign value, however other mechanisms such as simple truncation may be employed. The DBMS  122  in the server  120  identifies relations  134  between the identified tables  132 , such that the relations  134  identify corresponding values between different tables  132 , as disclosed at step  203 . The relations  134  define the types of keys between the tables, such as foreign keys and internal keys, and are employed to compute an ordering of the columns, such that the ordering indicative of dependencies between the tables defined by the relations, as depicted at step  204 . The ordering therefore defines a priority to ensure that foreign keys are updated prior to the tables to which they refer. The DBMS  122  then performs, for each identified column, the defined scrubbing operation on the entries  133  in the identified column  136 , the scrubbing operations performed according to the computed ordering, as depicted at step  205 . 
         [0024]      FIG. 3  is a block diagram of processing database entities for removing sensitive data according to the flowchart of  FIG. 2 . In conventional database sanitization, or scrubbing, techniques, manual substitution of sensitive data with safe, or generic data may disrupt relations  134  between the tables  132  when fields are employed as key values into another table  132 . Each table  132  has columns, or fields  136 , in arranged in rows, or entries  133 . In particular, configurations herein substitute values in key, or referring tables, before scrubbing referred tables into which the values refer. In this manner, the referred tables retain the relations  134  from the referring tables by updating the referred tables with the same corresponding values as the referring table.  FIG. 3  depicts this ordering of the relations. 
         [0025]    Referring to  FIG. 3 , the relations  134  denoting the key fields are identified by key fields between the entries. Matching key fields between tables  132  denote relations between records, and may either be via matching of data values, as a so-called foreign key, or by database internal identifiers, or indices, to related entries  133 , discussed further below. In the example database  100 , a host table  132 - 11  has a relation  134 - 11  to a file system table  132 - 12  and a relation  134 - 12  to an alert table  132 - 13 . The host table  132 - 11  includes fields  136  for host name  136 - 11 , host ID  136 - 12 , IP address  136 - 13  and operating system OS  136 - 14 . Similarly, the file system table  132 - 12  has fields  136  for host ID  136 - 21 , host type  136 - 22  and root directory  136 - 23 , and the alert table includes host name  132 - 31 , alert type  136 - 32  and alert description  136 - 33 . 
         [0026]    Host table  132 - 11  has a relation  134 - 11  to the file system table  132 - 12 . The relation is defined from the referring host ID field  136 - 12  to the referred field  136 - 21 , and is an internal database identifier, typically in integer value or index. The host table  132 - 11  also has a relation  134 - 12  to the alert table  132 - 13  via fields host name  136 - 11  and alert name  136 - 31  (AL_NAME). Since the host name  136 - 31  has actual data, rather than a database generated index, it is a foreign key meaning that the actual data values operate as a key field. Since the host name  136 - 11 , 136 - 31  is considered sensitive data, it will be overwritten with generated replacement values. A priority ordering, discussed further below, ensures that the referring field host name  136 - 11  is replaced before the referred field  136 - 31  so that referential integrity between the tables  132 - 11  and  132 - 13  is maintained. 
         [0027]    For each sensitive value, a replacement value overwrites a replaced value of sensitive data. A old name mapping table  142  of replaced values  145  and corresponding replacement values  147  is indexed by a substitution table  144  of fields  143  for which updates were performed. The mapping table  142  of replaced names is employed in successive updates to determine a foreign key value in the replaced value  145  now residing in the replacement value  147 . 
         [0028]    An old name mapping table  142  correlates replaced values to their benign counterparts, and is indexed from a substitution table  140  that specifies a concatenation base name  141  for each field (column)  143  that requires a sensitive data substitution. The replaced field names  143  are obtained from the respective field names in the table  132 - 11 ,  132 - 13 , as shown by arrows  143 ′ and  143 ″ respectively. The replacement name is computed from a concatenation of the base name  142  and a counter increment. 
         [0029]      FIG. 4  is a scrubbing configuration  150  depicted as a table employed for generating scripts for performing processing according to  FIG. 3 . In the example arrangement, each column  136  having sensitive data defines a row entry  154 - 1  . . .  154 - 10  ( 154  generally) in a set of updates  152  arranged in a tabular form in a scrubbing configuration  150 , such as in a spreadsheet. The scrubbing configuration  150  in the spreadsheet includes, for each row entry  154 , a table name  156 - 1  indicating the table having sensitive data, a cname  156 - 2  denoting the field containing the sensitive data, and a prefix  156 - 3  indicating the replacement name, or string, that will be concatenated with an incremental value to form the replacement name. The spreadsheet also includes a priority field  156 - 4  specifying the order that the fields are to be updated, and a scrubbing function  156 - 5  to indicate the operation to occur to remove the sensitive data, such as replacement or truncation. 
         [0030]      FIGS. 5-8  are a flowchart of employing the table of  FIG. 4  for scrubbing the database entities as depicted in  FIG. 3 . Referring to  FIGS. 3-8 , the disclosed method of sanitizing a customer specific database includes, in the example configuration shown, identifying tables of sensitive data, as depicted at step  300 , and identifying columns  136  of entries in the identified tables  132  having sensitive entries, as shown at step  301 . The columns  136  correspond to fields, in which the fields are fields of database entries  133  in the relational database  130 , such that each of the fields  136  is responsive to the scrubbing operation  256 - 5  for overwriting sensitive data in the field  136 , as shown at step  302 . As is known in the art, relational databases  130  are expressible as tabular two dimensional tables including rows, or entries  133  of fields, denoted as columns  136 . When a field includes sensitive data, the replacement operation, discussed further below, is applied to the entire column  136  to purge all entries  133  of sensitive data for that particular field. 
         [0031]    The sanitizing application  127  defines, for each column  136  in the identified columns, a scrubbing operation  156 - 5  for overwriting sensitive data in the respective column  136 , as depicted at step  303 . Defining the scrubbing operation may include fabricating a particular benign value  156 - 3  with which to overwrite the sensitive data. At step  304 , for each identified column, the sanitizing application  127  determines a scrubbing operation, and determines if the entries in a particular one of the identified columns  136  are control values employed in successive computations, as shown at step  305 . A check is performed for computing, based on the determining, a scrubbing operation for the particular column, as shown at step  306 . Based on the check, the sanitizing application specifies, if the entries are control values, a benign value for overwriting sensitive data in the respective column, as shown at step  307 , or truncates, if the entries  136  are not control values, the sensitive data, as depicted at step  308 . Alternate scrubbing operations may be employed, such as blanking the field, encrypting the field, or any suitable field modification. Therefore, a typical scrubbing operation is replacement of the sensitive data with a generated benign value, however truncation and nullification may also be employed, particularly if the data in the field is not employed for subsequent control. 
         [0032]    The method identifies relations  134  between the identified tables  132 , such that the relations  134  identify corresponding values between different tables  132 , as shown at step  309 . The relations  134  are indicative of master keys and foreign keys employed for accessing corresponding entries  133 , as depicted at step  310 . The foreign key relations employ the actual table values as keys (foreign keys) in to another (referred) table  132 . When table values referring to other tables as foreign key values, the corresponding key values in the referred table need to be changed to the same value as the foreign key in the referring table  132 . Accordingly, a check is performed, for each table  132 , to determine if the table has a foreign key or a master key, as depicted at step  311 . 
         [0033]    The method determines, if the table  132  has a foreign key, the reference table  132  having the corresponding master key, as depicted at step  312 . Since the master key points to, or refers to, the same foreign key value in the referred table, the replacement values for each are the same. The resulting computed ordering  156 - 4  is based on identification of a referring table  132 - 11  having a foreign key  136 - 11  to at least one reference table  132 - 13 , as shown at step  313 . This includes identifying the referenced tables to which the foreign keys refer, as shown at step  314 , and defining the ordering to be indicative of replacing values for a master key before replacing values for corresponding foreign keys, as depicted at step  315 . The resulting ordering defines the priority such that scrubbing operations performed on referring tables precede scrubbing operations on reference tables, as disclosed at step  315 . In the example arrangement, the scrubbing configuration  150  employs the priority  156 - 4  for computing an ordering of the columns  136 , such that the ordering is indicative of dependencies between the tables defined by the relations  134 , as depicted at step  317 . Thus, the replacement values are applied to referring tables before the referenced tables to which the foreign keys refer. 
         [0034]    Having computed the pertinent values for the scrubbing configuration  150 , the method generates a scrubbing entry  154  for each identified column  136 , such that the scrubbing entry  154  is indicative of a scrubbing operation, replacement value, and ordering of the scrubbing entry, as shown at step  318 . In the example arrangement, a spreadsheet is employed for storing the scrubbing entry and the determined scrubbing operation, as shown at step  319 , however alternate arrangements, such as another database table, may be employed for storing the scrubbing configuration  150 . 
         [0035]    The sanitizing application  127  receives the scrubbing configuration  150 , and generates, for each generated entry  154  in the scrubbing configuration, a SQL script  128  for performing the defined scrubbing operation  156 - 5 . Executing the generated script updates each of the identified columns  136  with a benign value, such that the scripts  128  perform updates according to the computed order, as shown at step  320 . The scripts  128  may be a single set of SQL instructions, or a series of individual scripts  128 , as shown at table I, however collectively the script  128  includes a column update for each of the identified columns, executing each column update in the executed order, as disclosed at step  321 . The script  128  therefore accesses the identified table  132 , as shown at step  322 , and accesses the identified column  136  in the table  132 , as depicted at step  323 . The script contains SQL commands  125  to perform, for each identified column  136 , the defined scrubbing operation  156 - 5  on the entries  133  in each identified column  136 , in which the scrubbing operations  156 - 5  are performed according to the computed ordering  156 - 4 , as disclosed at step  324 . In the example arrangement, executing of the script  128  invokes the DBMS  122  for issuing commands  125  to the database  130  according to the script  128 , however alternate arrangements for accessing the sensitive data may be performed. After accessing the tables  132 , the sanitizing application  127  generates a mapping of replaced values to replacement values, as depicted at step  325 . The mapping of values may take the form of a database mapping table  142 , as shown in the example, or other suitable association. The mapping of values is employed below to identify the replacement values corresponding to particular “real” values for entities such as node names. The entries in the old name mapping table  142  are indexed from a substitution table  140  of prefixes employed for generating the benign names. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE I 
               
               
                   
               
               
                 SQL for base table: 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 sql_stmt:=‘ Update ’|| REC_TABLELIST.TNAME|| 
               
               
                   
                   ‘ set ’|| REC_COLUMN.CNAME|| 
               
               
                   
                    ‘=’||“”||REC_COLUMN.prefix||“”||‘ ||rownum’|| 
               
               
                   
                   ‘ where ‘||REC_COLUMN.CNAME||’ is not null ’; 
               
               
                   
                   
               
             
          
         
       
     
         [0036]    After generation of the scripts  128 , the DBMS  122  performs the scrubbing operation by executing the scripts  128  corresponding to each particular field (column  136 ) of the fields in the database  130 , as depicted at step  326 . Executing the script  128  generates a replacement value computed from an incremental value and the benign value  141 , as shown at step  327 . The DBMS  122  then performs the determined scrubbing operation  156 - 5 , in which the scrubbing operations include at least one of replacement, overwriting, and truncating, as depicted at step  328  (Note that some scrubbing operations, such as truncation, may not require a generated replacement value). 
         [0037]    Following the scrubbing operation, the DBMS  122  identifies canonical values in the database, such that the canonical values are used to identify the manageable entities in a particular storage area network configuration, as depicted at step  329 . This may be performed incrementally, however is typically performed after the replacement values have been written. The canonical values represent “real world” labels of items such as manageable entities in the SAN  100 . Accordingly, the DBMS employs the old name mapping  142  for applying the replacement values to canonical fields storing the replaced values, as disclosed at step  330 . 
         [0038]    Further updates include a scenario involving a parallel historical database, in which the tables of sensitive data include online tables in a primary database and historical tables in a history database, the historical tables paralleling corresponding online tables. In such a scenario, the history database including previous updates to the online database and the scripts  128  further perform updates to the historical database, as shown at step  331 . 
         [0039]    Those skilled in the art should readily appreciate that the programs and methods for sanitizing a database as defined herein are deliverable to a processing device in many forms, including but not limited to a) information permanently stored on non-writeable storage media such as ROM devices, b) information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media, or c) information conveyed to a computer through communication media, for example as in an electronic network such as the Internet or telephone modem lines. Such delivery may be in the form of a computer program product having a computer readable storage medium operable to store computer program logic embodied in computer program code encoded thereon, for example. The operations and methods may be implemented in a software executable object or as a set of instructions embedded in an addressable memory element. Alternatively, the operations and methods disclosed herein may be embodied in whole or in part using hardware components, such as Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software, and firmware components. 
         [0040]    While the system and method for sanitizing a database has been particularly shown and described with references to embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.