Patent Publication Number: US-10762239-B2

Title: Apparatus and method for data matching and anonymization

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     The present application is a divisional of U.S. patent application Ser. No. 14/642,041, filed Mar. 9, 2015, entitled “APPARATUS AND METHOD FOR DATA MATCHING AND ANONYMIZATION”, which is a continuation of U.S. patent application Ser. No. 14/449,831, filed Aug. 1, 2014, entitled “APPARATUS AND METHOD FOR DATA MATCHING AND ANONYMIZATION,” the entire contents for each which are incorporated herein by reference for all purposes. 
    
    
     BACKGROUND 
     Some embodiments described herein relate generally to methods and apparatus for data matching and anonymization. 
     In digital media, information managed and exchanged between business entities is often a combination of confidential information, that cannot be shared (e.g., home address information of customers), and other business information that is to be shared (e.g., purchasing activity). When confidential and business information is inextricably linked by a common identifier, it is challenging to effectively isolate, anonymize, and/or otherwise reversibly and/or irreversibly dissociate the information to be transmitted vs. the information to be protectively retained. 
     A need exists, therefore, for methods and apparatus for data matching and anonymization using one or more key maps. 
     SUMMARY 
     In some embodiments, a method includes receiving a plurality of data sets. Each data set includes a customer identifier field that specifies a unique customer identifier associated with each entry in each data set. The plurality of data sets includes a first group of data sets and a second group of data sets, each data set associable with each other data set based on the customer identifier. The method further includes storing the plurality of data sets, and generating a key map including the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes unique customer identifiers of the first group of data sets of the plurality of data sets, and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier. The method further includes storing the key map, and rendering each data set of the second group of data sets as unassociable with each data set of the first group of data sets by replacing, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier. The method further includes rendering each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier. 
     In some embodiments, a method includes receiving a reference data set including a customer identifier field. The customer identifier field of the reference data set specifies a unique customer identifier associated with each entry in the reference data set. The method further includes receiving a first data set including the customer identifier field. The customer identifier field for the first data set specifies a unique customer identifier associated with each entry in the first data set, the first data set associable with the reference data set based on the customer identifier field. The method further includes storing the reference data set and the first data set. The method further includes rendering the first data set unassociable with the reference data set by replacing each unique customer identifier of the second data set with a corresponding anonymous identifier based on a key map. The key map includes the customer identifier field and an anonymous identifier field, the customer identifier field for the key map including the unique customer identifiers of the second data set, the anonymous identifier field including unique anonymous identifiers, each anonymous identifier corresponding to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier. The method further includes receiving one or more additional data sets, each additional data set including the customer identifier field. The one or more additional data sets is associable with the reference data set based on the customer identifier field and unassociable with the first data set. The method further includes rendering the one or more additional data sets as unassociable with the reference data set by replacing each unique customer identifier in each additional data set with its corresponding anonymous identifier, based on the key map. The rendering includes rendering the one or more additional data sets as associable with the first data set based on the anonymous identifier field. 
     In some embodiments, an apparatus includes a data module configured to receive a plurality of data sets, each data set including a customer identifier field. The customer identifier field specifies a unique customer identifier associated with each entry in each data set. The plurality of data sets includes a first group of data sets and a second group of data sets, each data set associable with each other data set based on the customer identifier. The data module is further configured to store the plurality of data sets. The apparatus further includes a key module configured to generate a key map including the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes unique customer identifiers of the first group of data sets of the plurality of data sets, and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier. The data module is further configured to store the key map, and the key module is further configured to render each data set of the second group of data sets as unassociable with each data set of the first group of data sets by replacing, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier. The rendering includes rendering each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier. 
     In some embodiments, a method includes receiving a first data set including a customer identifier field, the customer identifier field specifying a unique customer identifier associated with each entry in the first data set. The method further includes storing the first data set and replacing each unique customer identifier of the first data set with a corresponding anonymous identifier based on a key map. The key map includes the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes the unique customer identifiers of the first data set, and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, each anonymous identifier being different from its corresponding customer identifier. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an apparatus, according to an embodiment. 
         FIG. 2  is a flow chart illustrating a method, according to an embodiment. 
         FIG. 3  is a flow chart illustrating another method, according to an embodiment. 
         FIG. 4  is a flow chart illustrating yet another method according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In some embodiments, a method includes receiving a plurality of data sets. Each data set includes a customer identifier field that specifies a unique customer identifier associated with each entry in each data set. The plurality of data sets includes a first group of data sets and a second group of data sets, each data set associable with each other data set based on the customer identifier. The method further includes storing the plurality of data sets, and generating a key map including the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes unique customer identifiers of the first group of data sets of the plurality of data sets, and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier. The method further includes storing the key map, and rendering each data set of the second group of data sets as unassociable with each data set of the first group of data sets by replacing, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier. The method further includes rendering each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier. 
     In some embodiments, the key map is stored in a different location than the plurality of data sets. In some embodiments, the method further includes deleting the key map. In some embodiments, the method further includes encrypting the key map with an encryption scheme such as, but not limited to, one of the following: a public-key encryption scheme and a symmetric key encryption scheme. In some embodiments, the first group of data sets includes demographic information for a plurality of households, and the second group of data sets includes transaction information for the plurality of households. 
     In some embodiments, a method includes receiving a reference data set including a customer identifier field. The customer identifier field of the reference data set specifies a unique customer identifier associated with each entry in the reference data set. The method further includes receiving a first data set including the customer identifier field. The customer identifier field for the first data set specifies a unique customer identifier associated with each entry in the first data set, the first data set associable with the reference data set based on the customer identifier field. The method further includes storing the reference data set and the first data set. The method further includes rendering the first data set unassociable with the reference data set by replacing each unique customer identifier of the second data set with a corresponding anonymous identifier based on a key map. The key map includes the customer identifier field and an anonymous identifier field, the customer identifier field for the key map including the unique customer identifiers of the second data set, the anonymous identifier field including unique anonymous identifiers, each anonymous identifier corresponding to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier. The method further includes receiving one or more additional data sets, each additional data set including the customer identifier field. The one or more additional data sets are associable with the reference data set based on the customer identifier field and unassociable with the first data set. The method further includes rendering the one or more additional data sets as unassociable with the reference data set by replacing each unique customer identifier in each additional data set with its corresponding anonymous identifier, based on the key map. The rendering includes rendering the one or more additional data sets as associable with the first data set based on the anonymous identifier field. 
     In some embodiments, the reference data set includes demographic information for a plurality of households, and at least one of the first data set and the additional data sets includes transaction information for the plurality of households. 
     In some embodiments, an apparatus includes a data module configured to receive a plurality of data sets, each data set including a customer identifier field. The customer identifier field specifies a unique customer identifier associated with each entry in each data set. The plurality of data sets includes a first group of data sets and a second group of data sets, each data set associable with each other data set based on the customer identifier. The data module is further configured to store the plurality of data sets. The apparatus further includes a key module configured to generate a key map including the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes unique customer identifiers of the first group of data sets of the plurality of data sets, and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, each anonymous identifier different from its corresponding customer identifier. The data module is further configured to store the key map, and the key module is further configured to render each data set of the second group of data sets as unassociable with each data set of the first group of data sets by replacing, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier. The rendering includes rendering each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier. 
     In some embodiments, the data module is further configured to delete the key map. In some embodiments, the apparatus further includes an encryption module configured to encrypt the key map with an encryption scheme including, but not limited to, one of the following: a public-key encryption scheme and a symmetric key encryption scheme. 
     In some embodiments, a method includes receiving a first data set including a customer identifier field, the customer identifier field specifying a unique customer identifier associated with each entry in the first data set. The method further includes storing the first data set and replacing each unique customer identifier of the first data set with a corresponding anonymous identifier based on a key map. The key map includes the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes the unique customer identifiers of the first data set, and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, each anonymous identifier being different from its corresponding customer identifier. 
     For ease of explanation of the embodiments described herein, applicants offer the following exemplary, non-limiting scenario. Consider a set of data tables (“data sets”), joined to and/or otherwise associable with one another by one or more key columns serving as identifier. Explained herein with reference to a single key column/identifier for simplicity, in an example, in data sets related to transaction information at the household level, a household identifier or HHID can also be the identifier for a table of the household&#39;s demographic data, a table of the household&#39;s transaction data, a table of the household&#39;s segment membership, and/or the like. Aspects of the disclosure are operable to “break the link”, un-join, anonymize, and/or render unassociable, such sensitive information data sets to other datasets, such as other data sets containing household identification information, containing non-sensitive household information, and/or the like. In this example, this is accomplished by replacing the HHID with an anonymous identifier, and generating a key map that contains each distinct HHID and its corresponding anonymous identifier. In this manner, by virtue of the key map, aspects of the disclosure still allowing the data sets rendered unassociable to have correct referential integrity, as will be described in more detail herein. All data sets having the anonymous identifier will join and/or be associable to one another correctly, but can not be linked and/or be unassociable with any other data set still having the HHID, such as a reference data set. 
     Key maps can be considered to play a “gatekeeper” role for the data sets that have the anonymous identifier field. To add or update a data set that includes the anonymous identifier field, the appropriate key map must be employed. To restore a data set having the anonymous identifier field to its original HHID field, the appropriate key map must be employed. If the key map is deleted, the data sets with the anonymous identifier field are referentially isolated from the data sets with the HHID field, such that no data can be added or removed from the data sets with the anonymous identifier field with mere knowledge of the HHID identifiers alone. If access to the key map is restricted, such as by encryption via a split-key encryption scheme, the data sets with the anonymous identifier field are effectively, referentially isolated from the data sets with the HHID field for any entity that does not have access to the key map, and can require (in this example) mutual consent from holders of both keys of the split-key encryption scheme to accurately access and/or modify the data sets with the anonymous identifier field. 
     In some embodiments, the data sets with the anonymous identifier field can be created as follows. All data sets that need to be rendered unassociable with other data sets (but remain associable with each other) are scanned to make a list of all identifier fields (similar to the HHID field described above). For each HHID field, a key map is generated that can contain a random, non-repeating anonymous identifier corresponding to each distinct HHID. For each data set that needs to be rendered unassociable, the HHID is replaced with the corresponding anonymous identifier based on the key map. 
     As an example, consider a data set of 19 individuals that have a unique ID (“id” field) that identifies them in the database. See Table 1. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 id 
                 fname 
                 lname 
                 address 
                 city 
                 zip 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 8661 
                 Kiona 
                 Wright 
                 320-6324 Suspendisse Ave 
                 Wanzele 
                 21518 
               
               
                 360 
                 Jada 
                 Wynn 
                 P.O. Box 223, 4388 At, Ave 
                 Cranbrook 
                 60205 
               
               
                 7514 
                 Bell 
                 Orr 
                 Ap #226-3919 Sit Av. 
                 Grandrieu 
                 48443 
               
               
                 2458 
                 Genevieve 
                 Lester 
                 290-6627 Sapien. Road 
                 Santa Caterina 
                 42285 
               
               
                   
                   
                   
                   
                 Villarmosa 
                   
               
               
                 4784 
                 Jessica 
                 Levy 
                 3779 Nunc. Street 
                 Pergola 
                 N4V 3G1 
               
               
                 2244 
                 Melvin 
                 Howell 
                 483 Cras Av. 
                 Macquenoise 
                 0538VQ 
               
               
                 9125 
                 Hayfa 
                 Browning 
                 856-9645 Sem Ave 
                 Vancouver 
                 8187 
               
               
                 2600 
                 Tatyana 
                 Frost 
                 P.O. Box 338, 6298 
                 Compiegne 
                 55068 
               
               
                   
                   
                   
                 Consectetuer Ave 
                   
                   
               
               
                 3443 
                 Unity 
                 Ross 
                 2398 Quis Ave 
                 Langenburg 
                 44477 
               
               
                 1378 
                 Thomas 
                 Ware 
                 P.O. Box 437, 9238 Sagittis 
                 Burnie 
                 Xxxx 
               
               
                   
                   
                   
                 Road 
                   
                   
               
               
                 7930 
                 Jolene 
                 Wiley 
                 Ap #544-1568 Eu Avenue 
                 Pietrain 
                 5195 
               
               
                 5991 
                 Hector 
                 Waters 
                 P.O. Box 347, 636 Eleifend 
                 Uppingham. 
                 48201 
               
               
                   
                   
                   
                 St. 
                 Cottesmore 
                   
               
               
                 2658 
                 Katell 
                 Delgado 
                 6024 Nonummy St. 
                 Southaven 
                 5790 
               
               
                 8909 
                 Elmo 
                 Mercado 
                 9278 Molestie Rd. 
                 San Francisco 
                 3423 
               
               
                 5422 
                 Keith 
                 Snider 
                 6154 Eu, Ave 
                 Nieuwmunster 
                 9289 
               
               
                 3784 
                 Nelle 
                 Day 
                 9411 Accumsan Av. 
                 Ussassai 
                 T1L 3R2 
               
               
                 1491 
                 Hamish 
                 Carr 
                 P.O. Box 617, 7601 Nulla 
                 Colorado Springs 
                 91989 
               
               
                   
                   
                   
                 Ave 
                   
                   
               
               
                 3105 
                 Michael 
                 Bates 
                 560-4259 Rhoncus Av. 
                 Nemi 
                 W3 2WN 
               
               
                 1368 
                 Nash 
                 Lambert 
                 Ap #628-1501 Amet, Road 
                 Raymond 
                 1209AD 
               
               
                   
               
            
           
         
       
     
     Now consider two additional data sets that employ the same “id” field as Table 1; Table 2, which lists online page views, and Table 3, which lists sales information. 
     
       
         
           
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                   
                 id 
                 numberOfWebImpressions 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 8661 
                 187 
               
               
                   
                 360 
                 114 
               
               
                   
                 7514 
                 22 
               
               
                   
                 2458 
                 176 
               
               
                   
                 4784 
                 155 
               
               
                   
                 2244 
                 140 
               
               
                   
                 9125 
                 28 
               
               
                   
                 2600 
                 176 
               
               
                   
                 3443 
                 115 
               
               
                   
                 1378 
                 200 
               
               
                   
                 7930 
                 129 
               
               
                   
                 5991 
                 47 
               
               
                   
                 2658 
                 68 
               
               
                   
                 8909 
                 12 
               
               
                   
                 5422 
                 139 
               
               
                   
                 3784 
                 118 
               
               
                   
                 1491 
                 194 
               
               
                   
                 3105 
                 85 
               
               
                   
                 1368 
                 18 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                 id 
                 totalSales 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 8661 
                  $99.00 
               
               
                   
                 360 
                 $166.00 
               
               
                   
                 7514 
                 $102.00 
               
               
                   
                 2458 
                  $14.00 
               
               
                   
                 4784 
                  $85.00 
               
               
                   
                 2244 
                 $189.00 
               
               
                   
                 9125 
                 $169.00 
               
               
                   
                 2600 
                 $131.00 
               
               
                   
                 3443 
                 $186.00 
               
               
                   
                 1378 
                  $64.00 
               
               
                   
                 7930 
                  $22.00 
               
               
                   
                 5991 
                  $92.00 
               
               
                   
                 2658 
                 $123.00 
               
               
                   
                 8909 
                 $195.00 
               
               
                   
                 5422 
                  $95.00 
               
               
                   
                 3784 
                  $7.00 
               
               
                   
                 1491 
                  $24.00 
               
               
                   
                 3105 
                  $3.00 
               
               
                   
                 1368 
                  $48.00 
               
               
                   
               
            
           
         
       
     
     In the current form of Tables 1-3, one could discover that ID 8661 belonged to Kiona Wright and that she had 187 web impressions and $99 in sales. Creating a key map that maps the “id” values to unique anonymous ID values prevent this association. One example for the key map could be as seen in Table 4, where the “anon_id” column corresponds to the anonymous ID values. 
     
       
         
           
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                   
                 id 
                 anon_id 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 8661 
                 53944 
               
               
                   
                 360 
                 89885 
               
               
                   
                 7514 
                 82459 
               
               
                   
                 2458 
                 352 
               
               
                   
                 4784 
                 49540 
               
               
                   
                 2244 
                 85370 
               
               
                   
                 9125 
                 54719 
               
               
                   
                 2600 
                 95324 
               
               
                   
                 3443 
                 68596 
               
               
                   
                 1378 
                 68953 
               
               
                   
                 7930 
                 87587 
               
               
                   
                 5991 
                 8724 
               
               
                   
                 2658 
                 28934 
               
               
                   
                 8909 
                 21529 
               
               
                   
                 5422 
                 85877 
               
               
                   
                 3784 
                 76751 
               
               
                   
                 1491 
                 31013 
               
               
                   
                 3105 
                 15316 
               
               
                   
                 1368 
                 95764 
               
               
                   
               
            
           
         
       
     
     Once the key map has been generated, Tables 2-3 can be anonymized and/or otherwise rendered unassociable with Table 1. The modified version of Tables 2, 3 are seen in Tables 5, 6, respectively: 
     
       
         
           
               
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                   
                 anon_id 
                 numberOfWebImpressions 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 53944 
                 187 
               
               
                   
                 89885 
                 114 
               
               
                   
                 82459 
                 22 
               
               
                   
                 352 
                 176 
               
               
                   
                 49540 
                 155 
               
               
                   
                 85370 
                 140 
               
               
                   
                 54719 
                 28 
               
               
                   
                 95324 
                 176 
               
               
                   
                 68596 
                 115 
               
               
                   
                 68953 
                 200 
               
               
                   
                 87587 
                 129 
               
               
                   
                 8724 
                 47 
               
               
                   
                 28934 
                 68 
               
               
                   
                 21529 
                 12 
               
               
                   
                 85877 
                 139 
               
               
                   
                 76751 
                 118 
               
               
                   
                 31013 
                 194 
               
               
                   
                 15316 
                 85 
               
               
                   
                 95764 
                 18 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                 TABLE 6 
               
               
                   
               
               
                   
                 anon_id 
                 totalSales 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 53944 
                  $99.00 
               
               
                   
                 89885 
                 $166.00 
               
               
                   
                 82459 
                 $102.00 
               
               
                   
                 352 
                  $14.00 
               
               
                   
                 49540 
                  $85.00 
               
               
                   
                 85370 
                 $189.00 
               
               
                   
                 54719 
                 $169.00 
               
               
                   
                 95324 
                 $131.00 
               
               
                   
                 68596 
                 $186.00 
               
               
                   
                 68953 
                  $64.00 
               
               
                   
                 87587 
                  $22.00 
               
               
                   
                 8724 
                  $92.00 
               
               
                   
                 28934 
                 $123.00 
               
               
                   
                 21529 
                 $195.00 
               
               
                   
                 85877 
                  $95.00 
               
               
                   
                 76751 
                  $7.00 
               
               
                   
                 31013 
                  $24.00 
               
               
                   
                 15316 
                  $3.00 
               
               
                   
                 95764 
                  $48.00 
               
               
                   
               
            
           
         
       
     
     Impression and sales analysis can be run on the tables 5-6, which cannot be associated with table 1 without the use of the key map (table 4). In this manner, all of the data in tables 5-6 is still associable across these tables, and is further associable with other tables having the “anon_id” field for analysis. For example one can observe that anon_id 95764 had 18 web impressions and $48 of sales. If the key map is deleted, there is no way to associate such information back to the corresponding id in table 1 (Nash Lambert). Further, to update the sales value for in table 6 for Nash Lambert from $48 to $88, the key map (table 4) is required to determine that Nash Lambert corresponds to anon_id 95764, which in turn can be used to access table 6 and update the $48 value corresponding to anon_id 95764 to $88. 
     As used herein, a module can be, for example, any assembly and/or set of operatively-coupled electrical components, and can include, for example, a memory, a processor, electrical traces, optical connectors, software (executing in hardware), and/or the like. As used herein, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, the term “a database” is intended to mean a single database or a set of databases. 
       FIG. 1  is a schematic illustration of apparatus  100  within which aspects of the invention can be implemented, according to an embodiment. In some embodiments, the apparatus  100  is configured for data matching and anonymization, as described in more detail below. The apparatus  100  includes a processor  122  and a memory  124 . The processor  122  includes at least a data module  128  and a key module  132 . In some embodiments, the processor  122  includes an encryption module  134 . The processor  122  can also include a communications module  136  for establishing and managing network connectivity of the apparatus  100 , such as with, for example, one or more data sources (not shown) and/or a remote user (not shown) of the apparatus. The processor  122  can also include a control module  130  for manipulating aspects of the apparatus  100  and/or any of the other modules described here, such as by a local and/or remote user (not shown). It is to be understood that each of the modules may be in seamless communication with each other module. 
     The apparatus  100  can further include a database  140  distinct from the memory  224 . In other embodiments, the memory  124  and the database  140  can be the same, and in yet another embodiment, the database  140  can be external to the apparatus  100 . In some embodiments, a database module  126  of the processor  122  is employed to manipulate the database  140 . 
     The apparatus  100  can be in communication with other entities, such as data sources and/or a remote user, via a network, which can be any type of network (e.g., a local area network or LAN, a wide area network or WAN, a virtual network, a telecommunications network, and/or the internet), implemented as a wired network and/or a wireless network. Any or all communications can be secured (e.g., encrypted) or unsecured, as is known in the art. The apparatus  100  can encompass a personal computer, a server, a database, a work station, a mobile device, a cloud computing environment, an application or a module running on any of these platforms, and/or the like. 
     In some embodiments, the database  140  and/or the memory  124  can hold data for purposes of data matching and anonymization, such as one or more data sets, including a reference set, a key map, and/or the like. In some embodiments, the data sets can include advertising data (e.g., see table 2) and/or transaction data (e.g., see table 3) for one or more promoted entities such as a product, at the household level. In some embodiments, the database  140  and/or the memory  124  can be populated with data sets for test groups and control groups in a manner similar to as described in related application Ser. No. 13/771,627 (“the &#39;627 application”) titled “SYSTEM AND METHOD FOR MEASURING ADVERTISING EFFECTIVENESS”, filed on Feb. 20, 2013, the disclosure of which is incorporated herein by reference in its entirety. 
     In some embodiments, the data module  128  is configured to receive a plurality of data sets (e.g., similar to tables 1-3). The data sets can be any collection of related data in a structured format, such as, for example, a table consisting of one or more columns, and one or more rows. In some embodiments, each data set including an identifier field, such as, for example, the “id” column in tables 1-4. In some embodiments, the identifier field is a customer identifier field. In some embodiments, the customer identifier field specifies a unique customer identifier associated with each entry in each data set. Each data set is associable with each other data set based on the customer identifier field. 
     In some embodiments, the plurality of data sets including a first group of data sets and a second group of data sets. In some embodiments, the first group of data sets can include information that need not be anonymized with respect to the customer identifier field, while the second group of data sets includes information to be anonymized with respect to the customer identifier field. For example, in some embodiments, the first group of data sets includes demographic information for a plurality of households (e.g., see table 1), and second group of data sets includes transaction information for the plurality of households (e.g., see table 3). 
     The key module  132  is configured to generate a key map that includes the customer identifier field and an anonymous identifier field (e.g., see table 4). The customer identifier field for the key map includes unique customer identifiers of at least the first group of data sets, and in some embodiments, the entire plurality of data sets. In this manner, the key map can be selectively generated. 
     The anonymous identifier field of the key map (e.g., the “anon_id” column of table 4) includes unique anonymous identifiers, each anonymous identifier corresponding to a customer identifier of the key map. Each anonymous identifier is different from its corresponding customer identifier. In some embodiments, the key module  132  is configured to generate the anonymous identifiers of the key map in any suitable manner including, but not limited to, using a random string generator, as a non-repeating sequence of random numbers, replacement of one or more characters in the corresponding customer identifier, omission of one or more characters in the corresponding customer identifier, replacing the corresponding customer identifier with a generalized version (e.g., street name, or zip code, etc.), making random changes to one or more characters in the corresponding customer identifier, and/or the like. In some embodiments, a non-repeating sequence of random numbers is employed. In other embodiments, the database  140  and/or the memory  124  is configured to generate the anonymous identifiers in a manner similar to as described above. 
     Still describing the key module  132 , in some embodiments, the key module further configured to replace, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier. In this manner, the key module  132  renders each data set of the second group of data sets as unassociable with each data set of the first group of data sets, since the customer identifier is now absent from the second group of data sets but still present in the first group of data sets, while the anonymous identifier is now present in the second group of data sets but absent from the first group of data sets. Further, in this manner, the key module  132  renders each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier. Said another way, the associability between the data sets of the second group is maintained. 
     Returning to the data module  128 , the data module can be further configured to store the plurality of data sets, such as in the database  140  and/or the memory  124 , directly or via the database module  126 . The data module  128  can be further configured to store the key map. In some embodiments, the data module  128  is further configured to delete the key map. In some embodiments, the data module  128  is configured to store the key map in a different location than the plurality of data sets. For example, in some embodiments, the plurality of data sets is stored in the database  140  while the key map is stored in the memory  124 , or vice versa. In other embodiments, the key map is stored in a different, external database (not shown), while the plurality of data sets is stored in the database  140 . 
     Once the key map is generated and the second group of data sets is rendered unassociable with the first group of data sets, the data module  128 , in some embodiments, is further configured to correlate a first entry in a first data set of the second group of data sets with a second entry in a second data set of the second group of data sets such as when, per the example earlier, transaction information need to be updated for a particular customer. The data module  128  can be configured to perform this by identifying, in the first data set, the anonymous identifier associated with the first entry, and identifying the second entry in the second data set corresponding to the identified anonymous identifier (i.e., based on the key map entry corresponding to the identified anonymous identifier). The data module  128  can be further configured to return the second entry in the second data set as correlated to the first entry in the first data set. 
     In some embodiments, the data module  128  can be configured to correlate, without employing the first group of data sets or the key map, a first entry in a first data set of the second group of data sets with a second entry in a second data set of the second group of data sets based on the anonymous identifier associated with the first entry. For example, for tables 5-6, it can be determined that the household with anon_id 95764 spent $48 and had a total web impression count of 18, without discerning the true identity of the household (Nash Lambert from table 1). 
     In some embodiments, the encryption module  134  is configured to encrypt the key map with an encryption scheme such as, but not limited to a public-key encryption scheme and a symmetric key encryption scheme. Any suitable digital protection technique can be employed that restricts access to the contents of the key map. 
       FIG. 2  is a flow chart illustrating a method  200  according to an embodiment. In some embodiments, the method  200  is for data matching and anonymization using a key map. The method  200  can be performed by the apparatus  100 , or any apparatus structurally/functionally similar to the apparatus  100 . Particularly, instructions associated with performing the method  200  can be stored in a memory of the apparatus (e.g., the memory  124  of the apparatus  100  in  FIG. 1 ) and executed in a processor of the apparatus (e.g., the processor  122  of the apparatus  100  in  FIG. 1 ). 
     At  202 , the processor  122  can be configured to receive a plurality of data sets (e.g., similar to tables 1-3), each data set including a customer identifier field. The customer identifier field specifies a unique customer identifier associated with each entry in each data set. The plurality of data sets includes a first group of data sets and a second group of data sets, each data set associable with each other data set based on the customer identifier. In some embodiments, the first group of data sets include demographic information (e.g., similar to table 1) for a plurality of households, and the second group of data sets includes transaction information (e.g., similar to table 3) for the plurality of households. 
     At  204 , the processor  122  can be configured to store the plurality of data sets. At  206 , the processor  122  can be configured to generate a key map (e.g., similar to table 4) including the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes unique customer identifiers of the first group of data sets of the plurality of data sets, and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, and each anonymous identifier is different from its corresponding customer identifier. 
     At  208 , the processor  122  can be configured to store the key map. In some embodiments, the key map is stored in a different location than the plurality of data sets. 
     At  210 , the processor  122  can be configured to render each data set of the second group of data sets as unassociable with each data set of the first group of data sets by replacing, based on the key map, each unique customer identifier in the second group of data sets with the corresponding anonymous identifier. In this manner, the processor  122  renders each data set of the second group of data sets associable with each other data set of the second group of data sets based on the anonymous identifier. 
     In some embodiments, the method  200  further includes correlating, without employing the first group of data sets or the key map, a first entry in a first data set of the second group of data sets with a second entry in a second data set of the second group of data sets based on the anonymous identifier associated with the first entry. 
     In some embodiments, the method  200  further includes deleting the key map. In some embodiments, the method  200  further includes encrypting the key map with an encryption scheme selected from, but not limited to, the following: a public-key encryption scheme and a symmetric key encryption scheme. 
       FIG. 3  is a flow chart illustrating another method  300  according to an embodiment. In some embodiments, the method  300  is for data matching and anonymization using a key map. The method  300  can be performed by the apparatus  100 , or any apparatus structurally/functionally similar to the apparatus  100 . Particularly, instructions associated with performing the method  300  can be stored in a memory of the apparatus (e.g., the memory  124  of the apparatus  100  in  FIG. 1 ) and executed in a processor of the apparatus (e.g., the processor  122  of the apparatus  100  in  FIG. 1 ). 
     At  302 , the processor  122  can be configured to receive a reference data set (e.g., similar to table 1) including a customer identifier field. The customer identifier field of the reference data set specifies a unique customer identifier associated with each entry in the reference data set. 
     At  304 , the processor  122  can be configured to receive a first data set including the customer identifier field (e.g., table 2). The customer identifier field for the first data set specifying a unique customer identifier associated with each entry in the first data set, the first data set associable with the reference data set based on the customer identifier field. 
     At  306 , the processor  122  can be configured to store the reference data set and the first data set. At  308 , the processor  122  can be configured to render the first data set unassociable with the reference data set by replacing each unique customer identifier of the second data set with a corresponding anonymous identifier based on a key map. In some embodiments, the key map (e.g., similar to table 4) is stored in a different location than the reference data set and the first data set. 
     The key map includes the customer identifier field and an anonymous identifier field, with the customer identifier field for the key map including the unique customer identifiers of the second data set, and the anonymous identifier field including unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map, and each anonymous identifier different from its corresponding customer identifier. 
     At  308 , the processor  122  can be configured to receive one or more additional data sets (e.g., table 3), each additional data set including the customer identifier field. The one or more additional data sets are associable with the reference data set based on the customer identifier field and are unassociable with the first data set. 
     At  310 , the processor  122  can be configured to render the one or more additional data sets as unassociable with the reference data set by replacing each unique customer identifier in each additional data set with its corresponding anonymous identifier based on the key map. The rendering at step  310  includes rendering the one or more additional data sets as associable with the first data set based on the anonymous identifier field. 
     In some embodiments, the reference data set includes demographic information for a plurality of households, and at least one of the first data set and the additional data sets includes transaction information for the plurality of households. 
     In some embodiments, the method  300  further includes correlating a first entry in the reference data set with a second entry in the first data set by identifying, in the reference data set, the customer identifier associated with the first entry and identifying, in the key map, the anonymous identifier corresponding to the identified first identifier. In such embodiments, the method can further include identifying the second entry in the first data set corresponding to the identified anonymous identifier, and returning the second entry in the first data set as correlated to the first entry in the reference data set. 
     In some embodiments, the method  300  further includes encrypting the key map with an encryption scheme selected from, but not limited to, the following: a public-key encryption scheme and a symmetric key encryption scheme. In some embodiments, the method  300  further includes deleting the key map. 
     In some embodiments, the method  300  further includes correlating, without employing the reference data set or the key map, a first entry in the first data set with a second entry in one of the additional data sets based on the anonymous identifier associated with the first entry. 
     In some embodiments, the method  300  further includes generating the key map including the customer identifier field and the anonymous identifier field, and storing the key map in a different location than the reference data set, the first data sets, and the one or more additional data sets. 
       FIG. 4  is a flow chart illustrating another method  400  according to an embodiment. In some embodiments, the method  400  is for data matching and anonymization using a key map. The method  400  can be performed by the apparatus  100 , or any apparatus structurally/functionally similar to the apparatus  100 . Particularly, instructions associated with performing the method  400  can be stored in a memory of the apparatus (e.g., the memory  124  of the apparatus  100  in  FIG. 1 ) and executed in a processor of the apparatus (e.g., the processor  122  of the apparatus  100  in  FIG. 1 ) 
     At  402 , the processor  122  can be configured to receive a first data set (e.g., similar to table 2) including a customer identifier field. The customer identifier field specifies a unique customer identifier associated with each entry in the first data set. At  404 , the processor  122  can be configured to store the first data set. 
     At  406 , the processor  122  can be configured to replace each unique customer identifier of the first data set with a corresponding anonymous identifier based on a key map (e.g., similar to table 4). The key map includes the customer identifier field and an anonymous identifier field. The customer identifier field for the key map includes the unique customer identifiers of the first data set and the anonymous identifier field includes unique anonymous identifiers. Each anonymous identifier corresponds to a customer identifier of the key map. Each anonymous identifier is different from its corresponding customer identifier. 
     At  408 , the processor  122  can be configured to receive one or more additional data sets (e.g., similar to table 3). Each additional data set includes the customer identifier field, and the one or more additional data sets is unassociable with the first data set. 
     At  410 , the processor  122  can be configured to render the one or more additional data sets associable with the first data set based on the anonymous identifier by replacing each unique customer identifier in each additional data set with its corresponding anonymous identifier, based on the key map. 
     Example 1 
     As another non-limiting example, consider two data sets (tables 7-8 below) that need to be anonymized and/or rendered unassociable with respect to a customer identifier. Table 7 contains exposure date information for consumers exposed to an online advertisement, and table 8 contains demographic segment information for the consumers. 
     
       
         
           
               
               
             
               
                 TABLE 7 
               
               
                   
               
               
                 hhid 
                 exp_date 
               
               
                   
               
             
            
               
                 123 
                 Apr. 1, 2013 
               
               
                 456 
                 May 5, 2013 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
             
               
                 TABLE 8 
               
               
                   
               
               
                 hhid 
                 segment_name 
               
               
                   
               
             
            
               
                 123 
                 Soccer_Mom 
               
               
                 123 
                 SUV_Owner 
               
               
                 456 
                 Video Gamer 
               
               
                   
               
            
           
         
       
     
     Aspects of the disclosure can determine that HHID column is a primary key (similar to the customer identifier), and generate a key map with a distinct, random value for each HHID, as shown in table 9: 
     
       
         
           
               
               
               
             
               
                 TABLE 9 
               
               
                   
               
               
                   
                 hhid 
                 anon_hhid 
               
               
                   
               
             
            
               
                   
                 123 
                 20 
               
               
                   
                 456 
                 40 
               
               
                   
               
            
           
         
       
     
     If table 7 is identifiable as EXPOSURE, table 8 is identifiable as DEMO_SEGMENT, and table 9 is identifiable as HHID_KEYMAP, then it follows that tables 7, 8 can be rendered unassociable with other data sets employing the HHID identifiers ‘123’ and ‘456’, and rendered associable with other data sets employing the anonymized HHID identifiers ‘20’ and ‘40’, using the following exemplary code:
         CREATE TABLE EXPOSURE_ANON   AS SELECT X.ANON_HHID AS HHID, E.EXP_DATE   FROM EXPOSURE E   JOIN HHID_KEYMAP X ON E.HHID=X.HHID;   CREATE TABLE DEMO_SEGMENT_ANON   AS SELECT X.ANON_HHID AS HHID, E.SEGMENT_NAME   FROM DEMO_SEGMENT E   JOIN HHID_KEYMAP X ON E.HHID=X.HHID;       

     The resulting anonymized tables, EXPOSURE_ANON (corresponding to Table 7) and DEMO_SEGMENT_ANON (corresponding to Table 8) are shown below as tables 10, 11 respectively. 
     
       
         
           
               
               
             
               
                 TABLE 10 
               
               
                   
               
               
                 hhid 
                 exp_date 
               
               
                   
               
             
            
               
                 20 
                 Apr. 1, 2013 
               
               
                 40 
                 May 5, 2013 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
             
               
                 TABLE 11 
               
               
                   
               
               
                 hhid 
                 segment_name 
               
               
                   
               
             
            
               
                 20 
                 Soccer_Mom 
               
               
                 20 
                 SUV_Owner 
               
               
                 40 
                 Video Gamer 
               
               
                   
               
            
           
         
       
     
     In this manner, in some instances, the key map can be dynamically generated based on the received data sets without the need for a reference data set. 
     As described earlier, in some embodiments, the data sets may be associated with test and/or control groups for purposes of determining advertising effectiveness as described in the &#39;627 application, in a manner that maintains user privacy (e.g., demographic information) while permitting sharing and/or dissemination of pertinent transactional, and other commercial information related to the campaign. For example, the data sets can include advertising data and transaction data for one or more promoted entities at the household level and/or for a specific retailer class such as, but not limited to, a mass channel population, an advertising campaign population, a drug channel population, a grocery channel population, a supercenter channel population, and/or the like. 
     In some embodiments, the advertising data set and transaction data set can include personally identifiable information (PII), which can be separated into a different data set of consumer data (e.g., such as table 1) followed by rendering the modified advertising data set and transaction data set unassociable with the consumer data set as described above. The consumer data set can include (but is not limited to) income, net worth, children indicators, a segment membership (e.g. lifestyle, buyer, etc.), spending information, one or more digital activity metrics, a household identifier (HHID) and/or the like. The modified advertising data set can include, among other things, information about date and time of exposure to the campaign (e.g. when exactly did the customer view the campaign advertisement), an identifier for the campaign, a HHID, an impression type and an impression identifier. The transaction data set can be product specific, and can also include a specification of a time associated with the transaction, a product identifier, the amount spent on the product, the retailer profile or source of the particular transaction for the product (e.g. a grocery channel), and a HHID. Embodiments described herein can generate a key map based on the HHID, and render the advertising data set, transaction data set unassociable with the consumer data set by replacing, in the advertising data set and transaction data set, the HHID with an anonymous HHID. 
     The processor  122  can be any suitable processor configured to run and/or execute the module(s) included in the processor  122 . Each module in the processor  122  can be any combination of hardware-based module (e.g., a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP)) and/or software-based module (e.g., a module of computer code stored in memory and/or executed at the processor  122 ) capable of performing one or more specific functions associated with that module. In some embodiments, the processor  122  can include other module(s) (not shown in  FIG. 1 ) configured to perform other function(s) for the apparatus  100 . 
     In some embodiments, the memory  124  can be, for example, a random-access memory (RAM) (e.g., a dynamic RAM, a static RAM), a flash memory, a removable memory, and/or so forth. In some embodiments, the memory  124  encompasses the database  140 . 
     The methods described herein are examples of how to implement data matching and anonymization. It is intended that the systems and methods described herein can be performed by software (stored in memory and/or executed on hardware), hardware, or a combination thereof. Hardware modules may include, for example, a general-purpose processor, a field programmable gate array (FPGA), and/or an application specific integrated circuit (ASIC). Software modules (executed on hardware) can be expressed in a variety of software languages (e.g., computer code), including Unix utilities, C, C++, Java™, Ruby, SQL, SAS®, the R programming language/software environment, Visual Basic™, and other object-oriented, procedural, or other programming language and development tools. Examples of computer code include, but are not limited to, micro-code or micro-instructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higher-level instructions that are executed by a computer using an interpreter. Additional examples of computer code include, but are not limited to, control signals, encrypted code, and compressed code. 
     Some embodiments described herein relate to devices with a non-transitory computer-readable medium (also can be referred to as a non-transitory processor-readable medium or memory) having instructions or computer code thereon for performing various computer-implemented operations. The computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not include transitory propagating signals per se (e.g., a propagating electromagnetic wave carrying information on a transmission medium such as space or a cable). The media and computer code (also can be referred to as code) may be those designed and constructed for the specific purpose or purposes. Examples of non-transitory computer-readable media include, but are not limited to: magnetic storage media such as hard disks, floppy disks, and magnetic tape; optical storage media such as Compact Disc/Digital Video Discs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), and holographic devices; magneto-optical storage media such as optical disks; carrier wave signal processing modules; and hardware devices that are specially configured to store and execute program code, such as Application-Specific Integrated Circuits (ASICs), Programmable Logic Devices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM) devices. Other embodiments described herein relate to a computer program product, which can include, for example, the instructions and/or computer code discussed herein. 
     While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. Where methods and steps described above indicate certain events occurring in certain order, the ordering of certain steps may be modified. Additionally, certain of the steps may be performed concurrently in a parallel process when possible, as well as performed sequentially as described above. Although various embodiments have been described as having particular features and/or combinations of components, other embodiments are possible having any combination or sub-combination of any features and/or components from any of the embodiments described herein.