Patent Publication Number: US-7584223-B1

Title: Verifying information in a database

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
   This application is related to the co-pending patent application with Ser. No. 10/639,140, entitled “Targeted Advertisement with Local Consumer Profile,” filed Aug. 12, 2003, and assigned to the assignee of the present invention. 
   This application is related to the co-pending patent application with Ser. No. 11/123,935, entitled “Method and System for Targeted Data Delivery,” filed May 5, 2005, assigned to the assignee of the present invention, and hereby incorporated herein by reference. 
   BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   Embodiments of the present invention relate to the field of data delivery. More particularly, embodiments of the present invention relate generally to targeted data delivery to users. 
   2. Related Art 
   The delivery of data over a communication network, such as the Internet, to users can be an inexpensive and powerful tool for marketing goods and services. As one example, data can be delivered to a display of a user through various means while the user is connected to the communication network. For example, data such as an advertisement over the Internet to a user coupled to the Internet can be delivered through the forms of banner ads, pop-up ads, e-mails, etc. Typically, delivery of such advertisement is without the consent of the user. That is, the advertisement is unsolicited by the user. 
   The initial cost for the delivery of the advertisement through the communication network is much less than that of conventional forms of advertising communicating through more traditional media, such as mailers, magazines, television, radio, etc. As a result, the advertiser can easily deliver many more advertisements to users over the communication network. Also, since, typically, few if any restrictions are placed on the delivery of advertisements, the advertisers take advantage of the cheaper form of advertising by delivering advertisements to wider segment of the population. 
   However, the inexpensive delivery of advertising can be problematic to users. That is, users may be deluged with unwanted and unsolicited advertising. For example, advertising can be delivered through e-mails. Because the cost of sending e-mail is relatively small, unsolicited junk e-mail or “spam” can be delivered to numerous e-mail addresses. As such, a user may be bombarded with multiple different spam e-mail advertisements. 
   These unsolicited spam e-mail advertisements can adversely affect the user by wasting the user&#39;s time, resources, and opportunities. Specifically, the unsolicited spam e-mail advertisement may overwhelm a user since the user must waste valuable time in addressing and deleting unwanted spam e-mail advertisement. In addition, unsolicited spam e-mail advertising may overload a user&#39;s e-mail account. That is, the in-box for the user can be quickly filled with unsolicited spam e-mails. This denies the user full use of their e-mail account, since legitimate or wanted e-mails are not delivered to the spam-filled e-mail account. As such, resources are wasted on unsolicited spam e-mails, and opportunities may be lost since the user cannot access legitimate or wanted e-mails. 
   In addition, while the inexpensive delivery of advertising over the communication network is an advantage to the advertiser, the various forms for delivering advertisements in the conventional art are inefficient. For example, banner advertising, which is more expensive to implement than spam e-mails, can be delivered to inappropriate recipients due to incomplete information about the users. That is, the targeting of users is insufficient due to a lack of information about the users. As a result, advertising funds are wasted on delivery of the advertisement to uninterested or inappropriate users. For example, a business may offer services that are valuable only to owners of an automobile. Using conventional spam delivery techniques, the advertisement may be delivered to numerous users who do not own an automobile and are uninterested in automobile-related services. As such, the business is wasting its advertising by delivering advertisements to users who are not likely to be interested in the services offered by the sources. In short, traditional data delivery methods may lack efficiency by delivering data to inappropriate users. 
   Accordingly, a form of advertising delivery that discourages spam and is more efficient in targeting would be advantageous. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a flow diagram illustrating a computer implemented method for targeted data delivery, in accordance with one embodiment of the present invention. 
       FIG. 2A  is a flow diagram illustrating a computer implemented method for targeted data delivery to determine if a user profile satisfies the selection criteria, in accordance with one embodiment of the present invention. 
       FIG. 2B  is a flow diagram illustrating a computer implemented method for targeted data delivery that determines if a user-selected relevancy criterion has been satisfied, in accordance with one embodiment of the present invention. 
       FIG. 3  is a block diagram illustrating a system architecture of a data broadcast network that is capable of targeted data delivery, in accordance with one embodiment of the present invention. 
       FIG. 4  is a flow diagram illustrating the flow of data in the data broadcast network of  FIG. 3 , in accordance with one embodiment of the present invention. 
       FIG. 5  is a block diagram of a network that is capable of targeted data delivery between a plurality of data sources and a network of users through a data distributor, in accordance with one embodiment of the present invention. 
       FIG. 6  is a block diagram of a system that is capable of targeted data delivery between a data source and a client, in accordance with one embodiment of the present invention. 
       FIG. 7  is a flow diagram illustrating operations in a computer implemented method for targeted data delivery taken from the standpoint of a user layer, in accordance with one embodiment of the present invention. 
       FIG. 8  is a block diagram of a system that is capable of targeted data delivery from the standpoint of a user layer, in accordance with one embodiment of the present invention. 
       FIG. 9  is a flow diagram illustrating operations in a computer implemented method for determining if an offer for the delivery of data has been accepted, in accordance with one embodiment of the present invention. 
       FIG. 10  is a diagram of a display illustrating an interface for an application capable of targeted data delivery, in accordance with one embodiment of the present invention. 
       FIG. 11  is a diagram of a display illustrating an icon for a minimized application capable of targeted data delivery, in accordance with one embodiment of the present invention. 
       FIG. 12  is a block diagram of a system that is capable of targeted data delivery between a plurality of advertisers and a network of consumers through a data distributor, in accordance with one embodiment of the present invention. 
       FIG. 13A  is a flow diagram illustrating operations in a computer implemented method for targeted data delivery in which a query is generated, in accordance with one embodiment of the present invention. 
       FIG. 13B  is a flow diagram illustrating operations in a computer implemented method for targeted data delivery in which a query is accessed, in accordance with one embodiment of the present invention. 
       FIG. 14  is diagram illustrating a system capable of implementing the method of  FIG. 13A , in accordance with one embodiment of the present invention. 
       FIG. 15  is a flow diagram illustrating operations in a computer implemented method for targeted delivery of data in which an incentive is distributed, in accordance with one embodiment of the present invention. 
       FIG. 16  is a data flow diagram illustrating the flow of information in the method of  FIG. 15 , in accordance with one embodiment of the present invention. 
       FIG. 17  is a flow diagram illustrating operations in a computer implemented method for targeted data delivery in which data and a query are generated for targeted data delivery, in accordance with one embodiment of the present invention. 
       FIG. 18  is a diagram illustrating a system capable of implementing the method of  FIG. 17 , in accordance with one embodiment of the present invention. 
       FIGS. 19A-19F  are diagrams illustrating exemplary user interfaces used to generate an offer for the targeted delivery of data, in accordance with one embodiment. 
       FIG. 20  is a flow diagram illustrating operations in a business method for revenue generation by enabling targeted data delivery, in accordance with one embodiment of the present invention. 
       FIG. 21  is a flow diagram illustrating operations in a business method for generating increased user interest in a particular item of data by enabling delivery of the data to selected targets, in accordance with one embodiment of the present invention. 
       FIG. 22  is a flow diagram illustrating operations in a business method for efficiently spending an advertising budget by enabling delivery of the promotion data (e.g., advertisements) to selected targets, in accordance with one embodiment of the present invention. 
       FIG. 23  is an example of a data structure in accordance with an embodiment of the present invention. 
       FIG. 24  is a flow chart of a method for verifying information is in a database according to one embodiment of the present invention. 
       FIG. 25  is a block diagram of a system for verifying information is in a database according to one embodiment of the present invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with these embodiments, it will be understood that they are not intended to limit the invention to these embodiments. On the contrary, the invention is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following description of the present invention, numerous specific details are set forth in order to provide a thorough understanding of the present invention. In other instances, well-known methods, procedures, components, and circuits have not been described in detail as not to unnecessarily obscure aspects of the present invention. 
   Notation and Nomenclature 
   Embodiments of the present invention can be implemented on software running on a computer system. The computer system can be a personal computer, notebook computer, server computer, mainframe, networked computer, handheld computer, personal digital assistant, workstation, and the like. This software program is operable for providing targeted data delivery. In one embodiment, the computer system includes a processor coupled to a bus and memory storage coupled to the bus. The memory storage can be volatile or non-volatile and can include removable storage media. The computer can also include a display, provision for data input and output, etc. 
   Some portions of the detailed descriptions that follow are presented in terms of procedures, steps, logic blocks, processing, and other symbolic representations of operations on data bits that can be performed on computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. A procedure, computer executed step, logic block, process, etc., is here, and generally, conceived to be a self-consistent sequence of operations or instructions leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. 
   It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present invention, discussions utilizing terms such as “sending,” identifying,” “comparing,” “delivering,” “accessing,” “applying,” “ranking,” “receiving,” “combining,” “evaluating,” “presenting,” “collecting,” “generating,” “storing,” “weighting” and “determining,” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, including an embedded system, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
   Overview 
   Electronic devices such as personal computers (PCs), televisions, personal digital assistants (PDAs), cellular telephones, and the like have become an essential and commonly-used part of life for millions, if not billions, of people throughout the world. These and other similar electronic devices are used frequently and for a variety of purposes. As will be described below in detail, various embodiments of the present invention utilize user information that is collected from at least one electronic device that is associated with a user. Embodiments of the present invention generate a user profile based on the collected user information. Embodiments in accordance with the present invention utilize the user profile to target delivery of data to at least one specific user. Furthermore, as will be described in detail below, embodiments in accordance with the present invention achieve such targeted delivery of data to users without requiring a release of any user information in the user profile. 
   Various specific embodiments in accordance with the present invention will be described in detail below in Sections 1 through 7. 
   Section 1: General Method and System for Targeted Data Delivery 
   Embodiments of the present invention are discussed within the context of targeted delivery of data. For purposes of brevity and clarity, the targeted delivery of advertisements is used for illustrative purposes throughout the application. However, although advertisements are used for purposes of brevity and clarity in certain examples, the targeted delivery of data is not intended to be limited to the targeted delivery of advertisements. 
   Other embodiments of the present invention are discussed within the context of electronic devices that are associated with a user. One electronic device, for example, is the PC and is used for illustrative purposes throughout the application. Although the PC is used for purposes of brevity and clarity in many of the following examples, it is not intended that the term “electronic devices” be limited to PCs. 
   In addition, other embodiments of the present invention are discussed within the context of targeting delivery of data to users. In one example, the user is an individual that is associated with at least one electronic device. Although the term “user” is used for purposes of brevity and clarity in many of the following examples, it is not intended that the term “user” be limited to individuals. For example, a user is representative of a company having many individuals. 
     FIG. 1  illustrates a method for targeted data delivery, in accordance with one embodiment of the present invention. That is,  FIG. 1  illustrates a process for targeting data delivery to a user. The method of flow diagram  100  is capable of ensuring the privacy of the user information used for targeting the delivery of data to the user. 
   At  110 , the present embodiment collects user information about a user from at least one electronic device that is associated with the user. In one embodiment, the information is automatically collected. The user information includes behavioral characteristics exhibited by the user, personal information associated with the user, and machine configuration for electronic devices associated with the user. In addition, any other information useful for targeting the user is collected. In many embodiments of the present invention, the collection of user information is performed locally at a site associated with a user. However, other embodiments are well suited to the collection of user information at locations remote from the user. For purposes of organization, specific examples and detailed descriptions of the collection process will be described in greater detail in Section 2 in which targeted data delivery is described from the standpoint of a user layer. 
   For example, behavioral characteristics collected for the user information include URL histories, usage of devices, usage of applications, etc. In particular, user activity on an electronic device associated with the user is monitored. In this way, user information of a user includes search behavior over the Internet which includes logs (which include timing information) of web browsing, logs of e-mails sent and received, the use of various electronic devices (e.g., printers, cameras, etc.), the use of those devices in a location specific manner, the use of those devices in a time specific manner, geographic locations of an electronic device associated with the user, use of software applications on the electronic devices, hardware configuration, software configuration, etc. In addition, this user information includes personal information, such as address, income, last name, etc. As such, these behavioral characteristics and personal information are used to target users exhibiting these characteristics. A full discussion of the various types of electronic devices from which user information is collected will be described in greater detail in Section 2. 
   At  120 , the present embodiment generates a user profile based on the user information. This user profile is formatted to enable the selection of particular characteristics that are used to target users exhibiting those characteristics. For example, the user profile includes attributes that represent the characteristics (e.g., search behavior, machine configuration, personal information, etc.) of the user that are collected in the background while the electronic devices associated with the user are operating. In many embodiments of the present invention, the generation of the user profile is performed locally at a site associated with a user. However, other embodiments are well suited to the generation of user profiles at locations remote from the user. Specific details regarding the generation of the user profile are discussed below in great detail in Section 2. 
   It should be noted that in accordance with embodiments of the present invention, even though the software is operating in the background, the software is operating with the authorization of the user for purposes of targeted data delivery. That is, unlike conventional spyware that is operating under fraudulently obtained or absent any authorization, embodiments of the present invention operate with the full knowledge and consent of the user, and often for the pecuniary benefit of the user. 
   At  130  the present embodiment stores the user profile. In this manner, the user profile is used to achieve delivery of data that is targeted to the user based on the user profile. In addition, this targeting of the user for the delivery of data is accomplished without requiring a release of any of the user information in the user profile. That is, the user profile is stored and used in a manner that protects the privacy of the user  250 . In many embodiments of the present invention, the storing of user information is performed locally at a site associated with a user. However, other embodiments are well suited to the collection of user information at locations remote from the user. Specific examples and detailed descriptions of the storing process are described in greater detail in Section 2 below. 
   For purposes of the present application, the term “without requiring a release of any of said user information in said user profile,” or any substantially similar term or phrase, is intended to refer to the restriction that the user information is not released outside of the system that is implementing the delivery of data. More specifically, for purposes of the present application the term “without requiring a release of any of said user information in said user profile,” or any substantially similar term or phrase, is intended to refer to the restriction that the user information is not required to be released beyond a data distribution layer as denoted by line A-A of  FIG. 5 . Furthermore, as will be described below with relation to  FIG. 5 , embodiments of the present invention do not require the release of user information beyond particular layers that define the system. In one embodiment of the present invention, the release of user information does not occur beyond a data distribution layer as denoted by line A-A. In another embodiment of the present invention, the release of user information does not occur beyond a device that is located between a user layer and the data distribution layer, as denoted between lines C-C and D-D. In still another embodiment of the present invention, the release of user information does not occur beyond a user layer denoted by line D-D. 
     FIG. 2A  is a flow diagram illustrating operations for targeted data delivery using a query that compares a user profile to selection criteria that define characteristics exhibited by targeted user profiles. The flow diagram  200  provides further illustration of the method for targeted data delivery of  FIG. 1 . The method of  FIG. 2A  is capable of protecting user information used for targeting the delivery of data to the user since release of any of the user information is not required. 
   For purposes of the present application, it is understood that in embodiments in accordance with the present invention, the query may be included within an offer for the solicitation of data. In one embodiment, the offer includes the query. In another embodiment, the offer includes the query and the location of the data to be offered for delivery. Alternatively, the offer and the query are sequential events, or separate events that transpire in parallel. In still another embodiment, the offer includes the query and the data. Additionally, in another embodiment, an optional offer of compensation for taking delivery of the data is included within the offer. In still another embodiment, the information (e.g., query, forms of the data, compensation, etc.) included within the offer is jumbled or scrambled together. Also, additional information is optionally included (e.g., expiration period, expiration data, etc.). 
   The present embodiment continues from  130  of  FIG. 1 . At  210 , the present embodiment forwards a query soliciting delivery of data to a controller that has access to the user profile. The query comprises selection criteria for selecting targeted user profiles. The selection criteria include characteristics that are exhibited or that are similar to those found in the user profiles generated in  FIG. 1 . As such, the selection criteria include information such as behavioral characteristics exhibited by the user, personal information associated with the user, and machine configuration for electronic devices associated with the user, etc. These selection criteria are used to identify targeted users whose user profiles satisfy the selection criteria. In other embodiments, the selection criteria are used to identity targeted users whose user profiles match the selection criteria. In this manner, the query is used for selecting user profiles that satisfy the selection criteria without requiring a release of any user information in the selected user profiles. Thus, the user profile of a targeted user is protected. In many embodiments of the present invention, the creation of the selection criteria is performed at the data source. However, other embodiments are well suited to the creation of selection criteria at locations other than the data source (e.g., the data distributor). For purposes of organization, Section 4 below describes the selection criteria creation process in detail. 
   In one embodiment, selection criteria define a target profile. That is, the target profile is representative of a user who is targeted for the delivery of data. As such, the selection criteria define characteristics exhibited by the user profiles that are targeted for the delivery of data. 
   In one embodiment, the query is forwarded to a controller that is co-located with an electronic device associated with the user. That is, the query is forwarded to an electronic device that is collecting user information for use in targeted data delivery. In another embodiment, the query is forwarded to a controller that is located at a remote device that is separate from the electronic devices associated with the user. That is, in such an embodiment, user information is not collected on the remote device. In many embodiments of the present invention, the operations of the controller are performed locally at a site associated with the user, although other embodiments are well suited to performing controller operations at locations remote from the user. Section 2 below provides specific details regarding the operation of the controller. 
   At  220 , the present embodiment determines if the user profile satisfies the selection criteria. The selection criteria define characteristics exhibited by targeted user profiles. If the user profile satisfies the selection criteria, then the data is presumably targeted to the user associated with the user profile. That is, the data is generated specifically for users who exhibit characteristics in their user profiles that satisfy the selection criteria. 
   On the other hand, if the user profile does not satisfy the selection criteria, then the data is not targeted to the user. That is, the data is not generated for the user whose user profile does not satisfy the selection criteria. 
   In many embodiments of the present invention, the operations performed to determine if the user profile satisfies the selection criteria are performed locally at a site associated with a user. However, other embodiments are well suited to determining whether the user profile satisfies the selection criteria at locations remote from the user. Again, for purposes of organization, specific examples and detailed descriptions of the determination of whether the user profile satisfies the selection criteria is described in greater detail in Section 2. 
   At  230 , the present embodiment initiates delivery of the data to the user when the user profile satisfies the selection criteria. Targeting the user and the delivery of the data is achieved without requiring a release of the user information in the user profile. That is, user information associated with the user that is used to determine whether the user profile satisfies the selection criteria is not required to be released. Additionally, release of the user information does not occur without the knowledge and authorization of the user to protect the user&#39;s privacy. In many embodiments of the present invention, the release of user information is performed locally at a site associated with a user. However, other embodiments are well suited to the release of user information at locations remote from the user. Further details regarding the release of user information are provided in Section 2. 
   In one embodiment, the data is delivered to an electronic device that is associated with the user. That is, the data is delivered to an electronic device from which user information is collected. In other embodiments, the data is delivered to electronic devices associated with the user from which no user information is collected. For example, the data is delivered to a television that just displays what it receives. In other embodiments, other forms of delivery media are supported, such as mailings, telephone calls, e-mails, etc. A full discussion of the various forms of delivering data to a user is provided in Section 2. 
   In one embodiment, after the data is delivered a subset of the user information in the user profile is released with authorization by the user. The subset of information is used to determine if the user profile satisfies the selection criteria. In many embodiments of the present invention, the release of the subset of user information is performed locally at a site associated with a user. However, other embodiments are well suited to the collection of user information at locations remote from the user. The release of the subset of information is described in greater detail in Section 2 below. 
     FIG. 2B  is a flow diagram  200 B illustrating operations performed in a method for initiating delivery of data that is targeted to a user, in accordance with one embodiment of the present invention. The flow diagram provides additional operations to the flow diagram  200 A and is implemented between  220  and  230  of  FIG. 2A . 
   At  240 , the present embodiment determines if the user-selected relevancy criterion has been satisfied. The relevancy criterion provides a measure for valuing the user&#39;s attention. That is, in one embodiment, the relevancy criterion is a price paid to the user for the user&#39;s time in accepting delivery of the data and viewing the data. If the relevancy criterion has not been satisfied, then the present embodiment ends. However, if the relevancy criterion has been satisfied, then the present embodiment proceeds to  250 . A full discussion of the user-selected relevancy criterion, as well as operations performed to determine if the user-selected relevancy criterion has been satisfied, is provided below in Section 2. 
   At  250 , the present embodiment presents an offer for the delivery of data to the user. This occurs when the user profile satisfies the selection criteria and the offer satisfies the user-selected relevancy criterion. Section 2 provides details of the presentation of the offer to the user. 
   At  260 , the present embodiment determines if the offer for the delivery of data has been accepted. If the offer has not been accepted, then the present embodiment ends. That is, the data is not delivered to the user. On the other hand, if the offer has been accepted, then the present embodiment proceeds to  230  to initiate the delivery of the data. As such, the user is able to determine if the data is worthwhile for viewing even though the user profile satisfies the selection criteria and the offer satisfies the user-selected relevancy criterion. 
   In this way, the user makes another determination on whether to authorize the release of user information used to satisfy the selection criteria in return for the delivery of the data. In one embodiment, acceptance of the offer authorizes a release of the subset of information used to determine if the user profile satisfies the selection criteria. Section 2 describes the operation for the acceptance of the offer in greater detail. 
   In still another embodiment, the data that is delivered to the user is customized. That is, the data is customized based on the user information. That is, the content of the data (e.g., advertisement) is dynamically adapted based on the user&#39;s profile. Not only will the user be presented with data of interest to him or her, but presentation of the data is tailored to the user&#39;s experience. 
   Turning now to  FIG. 3 , a system is shown illustrating an exemplary data broadcast network  300  that is capable of targeted data delivery between a data source  310  and a user  350 , in accordance with one embodiment of the present invention.  FIG. 3  provides further illustration to the methods of  FIGS. 1 and 2 , in one embodiment. The system of  FIG. 3  is capable of protecting user information used for targeting the delivery of data to the user. 
   The data broadcast network  300  includes a server  320  and a client  340  which are connected through the Internet  330 , or any other communication network. The server  320  is operated by a data distributor, while the client  340  (e.g., a PC) is operated by the user  350 . While the present embodiment of  FIG. 3  is described within the context of one server and one client, other embodiments are well suited to supporting data broadcast networks consisting of a plurality of servers and a plurality of clients supporting one or more sources of data (e.g., advertisers) and one or more users. 
   As shown in  FIG. 3 , the client  340  represents an electronic device associated with the user  350 . As described previously, user information is collected from the client  340 . While in the present embodiment only one client is shown, other embodiments are well suited to supporting the collection of user information from multiple clients associated with the user  350 . A detailed description of the various types of clients and the collection of user information from those clients is provided in Section 2. 
   In  FIG. 3 , a user profile is based on the user information collected from the client  340 . This user profile is formatted to enable the selection of particular characteristics that are used to target users exhibiting those characteristics. For example, in  FIG. 3 , the user profile of the user  350  includes attributes  343 . The attributes  343  represent the characteristics (e.g., behavior, machine configuration, personal information, etc.) of the user  350 . The attributes are collected in the background of the client  340 . 
   It should be noted that in accordance with embodiments of the present invention, even though the collection of attributes is operating in the background, this collection is operating with the authorization of the user for purposes of targeted data delivery. That is, unlike conventional spyware that is operating under fraudulently obtained or absent any authorization, embodiments of the present invention operate with the full knowledge and consent of the user, and often for the pecuniary benefit of the user. 
   In one embodiment, the user  350  controls what types of data are collected for the user profile, and how long such information is held in the user profile. Furthermore, the user  350  enters additional attributes  343  into an associated user profile to specify further identifiable interests. In many embodiments of the present invention, the collection of attributes is performed locally at a site associated with a user, although other embodiments are well suited to the collection of attributes at locations remote from the user. For purposes of organization, specific examples and detailed descriptions of attributes and their collection will be described in greater detail in Section 2. 
   In addition, the user profile includes an optional threshold  345 . The threshold  345  defines the minimum payment required to make it worthwhile for the user  350  to accept delivery of data. For example, the threshold defines the payment to the user required for the user  350  to view an advertisement. In accordance with embodiments of the present invention, there are multiple thresholds associated with a user  350  depending on the currency of the offer (e.g., cash, frequent flyer miles, discounts) and the content of the data. Section 2 below provides details of the selection and implementation of the threshold  345 . 
   In one embodiment, the collecting of  110 , the generating of  120 , and the storing of  130  of  FIG. 1  are performed at one of the electronic devices associated with the user  350 . For example, the collecting of  110 , the generating of  120 , and the storing of  130  are performed at locally at the client  340 . The local performance of these operations protects the privacy of the user information, and is described in detail in Section 2. 
   In another embodiment, at least one of the collecting of  110 , the generating of  120 , and the storing of  130  of  FIG. 1  is performed on a remote electronic device that is separate from the electronic devices that are associated with the user  350 . 
   Also shown in  FIG. 3  is a data source  310 . In the present embodiment, the data source  310 , or some representative of the data source  310 , creates a query  323 , a link  325  to data (e.g., an advertisement), and an optional payment  327  that is paid upon delivery of the data. The query  323 , link  325 , and optional payment  327  combined constitute an offer. The query  323 , link  325 , and payment  327  are used for the solicitation of the delivery of the data. In one embodiment, the query  323 , link  325 , and payment  327  are created by the data source  310  and stored on the server  320 . In other embodiments, the query  323 , link  325 , and payment  327  are created in combination with the server  320 , or through another representative (e.g., a broker). Also, in one embodiment, the storing of the offers is implemented through a web application interface hosted by the data distributor. A full discussion of the creation of the query  323 , link  325 , and payment  327  is provided in Section 3, in which the targeted delivery of data is described from the standpoint of the data source  310 . 
   In the present embodiment, the data distributor through the server  320  broadcasts the query  323 , link  325 , and payment  327 , to all of its associated clients in the data broadcast network  300 , including the client  340 . The client determines if an associated user profile satisfies the query. Section 2 describes, in detail, the determination of whether the user profile satisfies the query. 
   In other embodiments, the query  323 , link  325 , and payment  327  are sent to a location other than the client  340  for accessing the user profile information and determining if the user profile satisfies the query. That is, the user profile is stored in a location other than the client  340 , or the determination of whether the user profile satisfies the selection criteria of the target profile occurs at a location other than the client  340 . Details of various locations for performing the determination of whether the user profile satisfies the selection criteria of the target profile is provided in Section 3, in which the targeted delivery of data is described from the standpoint of the data distributor layer. 
   In one embodiment, if the user profile of the user  350  satisfies the query  323  and potential payment  327  satisfies the threshold  345 , the user  350  decides whether the delivery of the data associated with the query  323  is desired in return for the payment  327 . On the other hand, if the user profile or the potential payment  327  do not satisfy the query, delivery of the data is not provided as an option to the user. 
   The data broadcast network  300  of the present embodiment is able to preserve the privacy of the user  350 . In particular, in one embodiment all queries (e.g., query  323 ) are broadcast to the user  350  without revealing any information about the user  350 . In such an embodiment, only when the user  350  decides to accept delivery of the data, and in turn collects the payment  327  does the user  350  intentionally disclose identity information (e.g., information in the user&#39;s profile used to satisfy the selection criteria). In one such embodiment, the identity of the user  350  is only revealed to the data distributor associated with the server  320 . In addition, in another embodiment, to further protect the privacy of the users in the data broadcast network  300 , only aggregate data is returned to the advertiser  310 . 
     FIG. 4  is a data flow diagram  400  illustrating the flow of information in the data broadcast system  300  of  FIG. 3 , in accordance with one embodiment of the present invention. In the present embodiment, the client  340  communicates with the server  320  over the Internet  330  to receive an offer QP  410 , which includes a query and potential payment. At the comparator function block  420 , the query in QP  410  is tested against the user attributes  343 . In addition, the potential payment in QP  410  is tested against the threshold  345 . 
   Also, the offer QP  410  is checked for validity. For example, an offer is invalid after exhausting an advertiser budget. In many embodiments of the present invention, the checking for the validity of the offer is performed at the data distributor layer. For purposes of organization, details for checking the validity of the offer are provided in Section 3. 
   If the user attributes  343  satisfy the query and the potential payment satisfies the threshold  345 , then at the output of the comparator function block  420 , the offer QP  410  is presented to the user in a non-intrusive manner. Details of the offer for the delivery of data are provided in Section 2. 
   At the acceptance block  430 , the user decides whether it is desired to have the data to be delivered (e.g., for the purposes of watching an advertisement). If the user chooses to have the data delivered, the data (e.g., an advertisement)  440  is retrieved from the server  320  and delivered to the client  340  for presentation to the user. The operations for the acceptance of the offer for the delivery of data are provided in detail in Section 2. 
   At the monitoring block  450 , the present embodiment determines if the data has been presented to the user (e.g., viewed by the user). If the data has been presented, then the payment  460 , as an incentive, is registered on the server to the user  350  that is associated with the client  340 . Section 3 below provides a detailed description of the payment of the incentive. 
   While the present embodiment of  FIG. 4  describes various functions at the server  320  and the client  340 , other embodiments of the present invention are well suited to performing these functions at either the server  320 , or the client  340 , or any location other than the server  320  or client  340 , or any combination thereto. 
     FIG. 5  is a diagram of an exemplary network  500  that is capable of targeted data delivery between a plurality of data sources and a network of users, in accordance with one embodiment of the present invention. Network  500  provides more detail to the data broadcast network  300  of  FIG. 3 , in one embodiment. In  FIG. 5 , the network  500  links a plurality of data sources  510  to a network of users  550  (e.g., user A, user B, on up to user N) for the targeted delivery of data. More specifically, the present embodiment links a data source with a particular user for the targeted delivery of data. Additionally, the network  500  is capable of achieving delivery of data that is targeted to a user based on information in a user profile without requiring a release of the user information to protect privacy. 
   In the network  500 , a plurality of data sources  510  exist in a data source layer above line A-A. For purposes of introduction, a brief description of the data source layer is provided here in Section 1 below. Also, a detailed description of the operations performed at the data source layer is provided in Section 4, in which the targeted delivery of data is described from the standpoint of the data source layer. 
   The plurality of data sources  510  includes data sources  511 ,  513 , and  515 . Each of a plurality of data sources  510  provides data. The data provided by a data source includes various forms of information, such as advertisements, publications, public and private notification alerts, etc. For example, in one embodiment, the data source  510  is an advertiser. The advertiser uses network  500  to deliver advertisements, as data, to targeted users. 
   Additionally, the data sources provide selection criteria that are crucial in identifying users targeted for the delivery of data. The selection criteria identify a set of users in the network of users  550 . That is, the selection criteria are used to identify and target users in the network of users  550 . For example, the selection criteria include the use of accounting software on a user&#39;s computing device to manage budgets, and other behavioral characteristics that indicate the user has visited tax help web sites. In this way, a data source that provides income tax services is targeted to those users that satisfy the criteria. 
   In one embodiment, the data source is a broker between the originator of the data and the data distributor  520 . For example, the broker interfaces with the data distributor  520  for the targeted delivery of data. That is, the broker provides the data and the criteria on behalf of the originator of the data. 
   In the network  500 , a data distributor  520  exists in a data distributor layer between line A-A and line B-B. For purposes of introduction, a brief description of the data distribution layer is provided in Section 1 below. However, a full description of the operations performed at the data distribution layer is provided in Section 3, in which the targeted delivery of data is described from the standpoint of the data distribution layer. 
   The data distributor  520  is communicatively coupled to the network of users  550 . The data distributor  520  provides the sole distribution point for the delivery of the data from the plurality of data sources  510  to the users in the network  550 . That is, the data distributor  520  provides the avenue for accessing each of the users in the network of users  550  specifically for the delivery of data from the plurality of data sources  510 . 
   In one embodiment, the data distributor is a network owner. That is, the network owner owns and provides access to its network of users  550 . For example, a network owner has an association with a manufacturer of PCs. As such, a buyer of a PC from the manufacturer agrees to become part of the network of users  550  for the targeted delivery of data. 
   For example, the data distributor is the creator and controller of the proprietary network  500 . The data distributor  520  receives a cut of every transaction equal to a function V(A,B) that is a function of the user ask price, A, and the bid price, B. The incentive delivered to the user is limited by A&lt;=V(A,B)&lt;=B. The incentive provided by a data source for the delivery of data is partly paid to the data distributor  520 . 
   In another embodiment, the data distributor  520  is a central server broadcasting to all peers over the Internet. In another embodiment, the data distributor  520  is a federation of such central servers broadcasting to peers over the Internet. For example, the data distributor  520  is associated with the server  320  of  FIG. 3 . 
   The data distributor  520  in combination with each of the data sources in the plurality of data sources generates offers for the targeted delivery of data. For example, an offer  530  is generated that is associated with one of the data sources in the plurality of data sources  510 . The offer  530  is used to target the delivery of data. The offer  530  includes a query  531 . The offer  530  includes a link  533  to the data, or any other form of accessing the data. That is, the offer  530  includes the location of the data to be offered for delivery, in some embodiments. In other embodiments, the offer  530  includes the data itself. The offer  530  also includes a bid price that indicates the price paid by the data source for the delivery of data to a user in the network of users  550 . That is, the offer  530  also optionally includes an offer of compensation for taking delivery. 
   In one embodiment, the offer  530  is generated at the data source, or broker that represents the data source. In another embodiment, the offer  530  is generated at the data distributor. In still another embodiment, the offer  530  is generated at the data source, or broker, and the data distributor  520  in combination. For purposes of organization, specific examples and detailed description of the generation of the offer  530  will be described in Section 4, in which the targeted delivery of data is described from the standpoint of the data source layer. 
   As shown in  FIG. 5 , a broadcast layer  540  broadcasts offers from the data distributor  520  to the network of users  550 . The broadcast layer  540  is located between line B-B and line C-C. For purposes of introduction, a brief description of the broadcast layer  540  is provided in Section 1 below. A detailed description of the operations performed at the broadcast layer is provided in Section 3. 
   The method of broadcast includes one or a combination of several possibilities including, but not limited to: a direct server connection over the Internet, an indirect connection through a peer-to-peer scheme, a data casting method that broadcasts digital messages over an existing television infrastructure. 
   In one embodiment, the broadcast layer  540  sends offers in various distribution patterns. For example, the broadcast layer  540  sends offers to each of the network of users  550 , a randomly selected number of users in the network of users  550 , or a demographically selected group of users from the network of users  550  (e.g., by geographic location). 
   In network  500 , a user layer is located below line D-D. For purposes of introduction, a brief description of the user layer is provided in Section 1. Furthermore, a detailed description of the operations performed at the user layer is provided in Section 2. 
   In the network  500 , each of the network of users  550  is associated with a targeting mechanism (e.g., client software) that operates to facilitate the targeted delivery of data. The targeting mechanism operates in the background to track a wide variety of user behaviors. These behaviors include behavioral and personal characteristics, for example. It should be noted that in accordance with embodiments of the present invention, even though the targeting mechanism is operating in the background, the targeting mechanism is operating with the authorization of the user for purposes of targeted data delivery. That is, unlike conventional spyware that is operating under fraudulently obtained or absent any authorization, embodiments of the present invention operate with the full knowledge and consent of the user, and often for the pecuniary benefit of the user. 
   The user information is organized according to a schema that allows queries to be generated that are designed such that user information that satisfy the criteria set forth in the query enable the targeting of data to users. 
   This user information is collected and represented as a user profile. As such, each of the network of users  550  is associated with a user profile containing personal and configuration information related to an associated user. For example, user A is associated with user profile A, user B is associated with user profile B, and user N is associated with user profile N. 
   Also, each of the plurality or network of users  550  is associated with a minimum ask price. For example, user A is associated with ask price  563 , user B is associated with ask price  573 , and user N is associated with ask price  583 . 
   The ask price is a form of consideration that is paid in return for the delivery of the data. The ask price may be available in many forms of consideration, such as cash, frequent flier miles, donations, printers, coupons, etc. The ask price is an economic mechanism designed to monetize the scarcity of consumer attention, in one embodiment. Users will set their ask price higher if they are not interested in receiving an increase in the number of items of data (e.g., advertisements). On the other hand, user will set their ask price lower if they are interested in receiving more items of data. In this way, the ask price is used to efficiently deliver the data to users who are interested in the data. 
   In another embodiment, the minimum ask price is used to discourage spam. That is, data considered as spam most likely is associated with very low bid prices in order to distribute the spam data to a large number of users. However, users with ask prices higher than the average price associated with spam data will not receive the spam data. As such, the minimum ask price is used to discourage the distribution of spam. 
   As shown in  FIG. 5 , the location of the targeting mechanism, user profile, and ask price is variable, in accordance with embodiments of the present invention. For example, the targeting mechanism  571 , user profile B, and ask price  573  is located at the electronic device  575  (e.g., PC) associated with user B. That is, the targeting mechanisms  571 , user profile B, and ask price  573  is locally located at the user layer. As such, the collection of user information for the user profile B and the determination of whether the user profile B satisfies any query coming from the data distributor occurs locally. Likewise, the determination of whether a bid price coming from the data distributor satisfies the user&#39;s asking price occurs locally. 
   In another embodiment, the location of the targeting mechanism  561 , user profile A, and ask price  563  is located remotely from the electronic device  565  that is associated with the user A. That is, the targeting mechanisms  561 , user profile A, and ask price  563  is located between the broadcast layer  540  and the user layer. As such, the collection of user information for the user profile A and the determination of whether the user profile A satisfies any query coming from the data distributor  520  occurs remotely. Likewise, the determination of whether a bid price coming from the data distributor satisfies the user&#39;s asking price occurs remotely. 
   In still another embodiment, the location of the targeting mechanism  581 , user profile N, and ask price  583  is located at a server associated with the data distributor  520 . That is, the targeting mechanisms  581 , user profile N, and ask price  583  are located at the data distributor layer. As such, the offer  530  need not necessarily go through the broadcast layer  540 , in one embodiment. As such, the collection of user information for the user profile N, and the determination of whether the user profile N satisfies any query coming from the data distributor  520 , and if a bid price coming from the data distributor satisfies the user&#39;s ask price  583 , occur at the data distributor  520  or at a server closely associated with the data distributor  520 . 
   In addition, the locations of each of the targeting mechanisms, user profiles, and ask prices are split amongst various locations shown above, in accordance with another embodiment of the present invention. 
   The targeting mechanisms are also used to determine if the user profiles satisfy the selection criteria as defined by the queries. For example, for user B, the targeting mechanism  571  determines if the query  531  in offer  530  is satisfied by the information in user profile B. Only if the selection criteria in query  531  are satisfied by the user profile B, and the bid price  535  exceeds the ask price  573  is an offer to deliver the data (e.g., through data link  533 ) presented to the user B. The bid price  535  is used as an incentive to entice a user to accept delivery of data. 
   For example, a choice selection is presented to user B, in one embodiment. The choice selection is presented through one of a number of mechanisms, such as a popup, a less invasive flashing systray icon, etc. The choice selection includes: 1) a choice to view the data (e.g., an advertisement), or 2) a choice to ignore the data. With this choice, the purpose of the data and a description of the data source are summarized for user B. 
   In addition, in return for accepting delivery of the advertisement for viewing, user B will receive a payment as consideration. That is, if user B chooses to view the data, the data is presented to the user, and only then is the user&#39;s account credited. For example, the credit to the user&#39;s account is made at the data distributor  520 . 
     FIG. 6  is a block diagram of a system  600  for targeted data delivery, in accordance with one embodiment of the present invention. The system  600  is implemented within the data broadcasting network of  FIG. 3 , for example in one embodiment. As such, system  600  includes a data source  310 , a server  320  that communicates with a client  340  through a communication network  610  (e.g., Internet  330 ), and a user  350 . 
   The remaining components of the system  600  are shown below the dotted line A-A. Representation of these components under dotted line A-A indicates that one or more of the components are located at the client  340 , the server  320 , or some other remote device communicating through the communication network with the server  320  and the client  340 . 
   The system  600  includes a collector  615 , a generator  620  and a storing module  625 . The collector  615  collects user information about a user  350  from at least one electronic device that is associated with the user  350 . In addition, the collector further includes a monitor for monitoring user activity on an electronic device associated with the user. The generator  620  generates a user profile based on the user information. In addition, the storing module  625  stores the user profile. As such, the user profile is used to achieve delivery of data (e.g., an advertisement) which is targeted to the user  340  based on the user profile without requiring a release of any of the user information in the user profile, in one embodiment. 
   In one embodiment, the collector  615 , generator  620 , and storing module  625  are disposed on an electronic device that is associated with the user. In another embodiment, the collector  615 , said generator  620 , and said storing module  625  are disposed on a remote electronic device that is separate from the electronic devices associated with the user. 
   In addition, the system  600  includes a storage medium  630 . The storage medium stores the user profile associated with the user  350 . In this way, information in the user profile is used to target the delivery of data to the user  350 . 
   In one embodiment of the present invention, the system  600  also includes an offer transmitter (not shown). For example, the offer transmitter is located at the server  320 , in one embodiment. The offer transmitter sends an offer soliciting delivery of the data to a controller  640  that has access to the user profile. In one embodiment, the broadcasting of the offer is implemented through client polling. Also, in one embodiment, the controller  640  is co-located with an electronic device that is associated with the user  350 . In another embodiment, the controller  640  is located on a remote device that is separate from the electronic devices associated with the user  350  from which user information is collected. 
   The offer&#39;s query defines selection criteria for selecting targeted user profiles. In addition, the system  600  also includes a profile comparator  645 . The profile comparator  645  determines if the user profile satisfies the selection criteria. 
   When the user profile satisfies the selection criteria, a delivery module  650  initiates the delivery of the data to the user  350 . In one embodiment, the delivery module is configured to initiate delivery of the data to an electronic device associated with the user, where user information is collected from the electronic device. In another embodiment, the delivery module is configured to initiate delivery of the data to an electronic device that does not collect user information. 
   As shown in  FIG. 6 , the system  600  also includes optional components, as follows: the relevancy criteria comparator  660 , the offer presenter  665 , and the acceptance determining module  670 . The relevancy criteria comparator  660  determines if the offer satisfies a user-selected relevancy criterion. That is, in one embodiment, the relevancy criteria comparator  660  determines if a bid price set by the data source  310  included in the offer meets or exceeds an ask price defined by the user as the user-selected relevancy criterion. 
   In addition, the system includes an offer presentor  665  for presenting an offer to an electronic device associated with the user for delivery of the data to the user. The offer is presented when the user profile satisfies the selection criteria. In another embodiment, the offer presentor is configured to release a subset of the user information. 
   In addition, the system includes an acceptance determining module  670 . The module  670  is able to determine if the offer has been accepted. In one embodiment, acceptance of the offer for delivery of data authorizes the release of a subset of the user information used to determine if the user profile satisfies the selection criteria. A releaser  676  releases the subset of information upon receipt of authorization by the user. 
   In one embodiment, on delivery of the data, an incentive delivery module  672  delivers an incentive to the user, or to a data distributor that forwards the query, or some combination of the two. 
   Also, in another embodiment, the system  600  includes a customizer  674 . The customizer  674  customizes the data delivered to the user. More specifically, the data is customized using the user information. 
   Section 2: Method and System for Targeted Data Delivery from the Standpoint of the User Layer 
   Embodiments of the present invention in Section 2 are described from the standpoint of the user layer. For example, the user layer is located below line D-D in  FIG. 5 . As such, description of the targeted delivery of data is described below from the standpoint of the user layer. 
     FIGS. 7 and 8  in combination illustrate a method and system for the targeted delivery of data, in embodiments of the present invention. More specifically,  FIG. 7  is a flow diagram  700  illustrating operations performed in a method for targeted data delivery, in accordance with one embodiment of the present invention. In addition, the components of the system of  FIG. 8  are capable of implementing the method of  FIG. 7 , in accordance with one embodiment of the present invention. 
   In  FIG. 7 , the operations performed in flow diagram  700  are performed locally at the user layer of  FIG. 5 , in the present embodiment. Similarly, in  FIG. 8 , components of system  800  perform the operations of  FIG. 7  and are located at the user layer. That is, system  800  is located entirely at the user layer, and is controlled by the user without any outside intervention. For example, system  800  is implemented entirely within the targeting mechanism  571  of the electronic device  575  located at the user layer to ensure privacy protection of the user&#39;s information. 
   In addition, continuing with  FIG. 5 , the user profile B is located at the electronic device  575 , in one embodiment. As such, privacy protection of the user&#39;s information stored in user profile B is controlled by the user. In this way, the targeted delivery of data is implemented without requiring the release of any user information beyond or outside of the user layer. That is, the present embodiment is configured such that use of the user information for targeting purposes is limited to the user layer. As such, the user exercises tight control over the use and release of user information during the targeted delivery of data. 
   Operations associated with the components of system  800  are implemented in hardware or software implemented within an electronic device from which the user information is collected. In another embodiment, the components of system  800  are implemented within an electronic device other than the electronic device from which the user information is collected. 
   In still other embodiments, one or more of the components of system  800  are located remotely from the devices associated with the user. That is, one or more of the components of system  800  are located remotely from the user layer of  FIG. 5 . However, in one such embodiment, to ensure protection of user information, secure communication with the remotely located system  800  is employed. 
   Returning to  FIG. 7 , at  710 , the present embodiment collects user information about a user from at least one electronic device that is associated with the user. That is, user information describing characteristics of the user is collected from one or more electronic devices comprising a set of electronic devices that is associated with the user. More specifically, each of the set of electronic devices is located below line D-D within the user layer of  FIG. 5 . As shown in  FIG. 8 , the collector  810  collects the user information. In the present embodiment, the components of the system  800  including the collector  810  are located at the user layer of  FIG. 5 , although in other embodiments, the collector  810  is located at other layers of  FIG. 5 . 
   The user information is collected from one or more electronic devices associated with the user. That is, any electronic device from which useful information can be collected about the user for purposes of targeting the user for the delivery of data is used. For example, electronic devices from which user information is collected includes, but is not limited to, the following: PC, printer, cell phone, pagers, household appliances, global positioning satellite (GPS) devices, television, personal digital assistant (PDAs), digital cameras, video recorders, cable set-top boxes, etc. 
   The user information includes characteristics that help to identify the user for the targeted delivery of data. These characteristics include personal information associated with the user, behavioral characteristics exhibited by the user, machine configuration for electronic devices of the user, etc. 
   In one embodiment, the user information is collected in the background of the electronic devices. That is, the user information is automatically collected while the electronic devices are operating. The user need not start, nor be acutely aware of, the collection of user information, in one embodiment. It should be noted that in accordance with embodiments of the present invention, even though the collection of user information is operating in the background, this collection occurs with the authorization of the user for purposes of targeted data delivery. That is, unlike conventional spyware that is operating under fraudulently obtained or absent any authorization, embodiments of the present invention operate with the full knowledge and consent of the user, and often for the pecuniary benefit of the user. 
   In one embodiment, personal information is collected for purposes of targeting the user for the delivery of data. For example, the user&#39;s name, sex, income, address, zip code, education level, etc. are collected. Many of these personal items of information are collected directly, or indirectly through inference. For example, the direct collection of personal information is collected from registration forms the user filled out and registered using the various devices associated with the user. In other words, in embodiments in accordance with the present invention, these personal items of information are located within definable spaces of the various electronic devices associated with the user. 
   In other embodiments, the collection of personal information is achieved through inference. For example, the stated income level may not be found in any of the various electronic devices associated with the user. However, based on a zip code that has been previously collected, a stated income level is inferred. That is, it is inferred that a user with a zip code associated with a middle class neighborhood earns approximately a middle class income. 
   As another example, the sex of the user is inferred through various means. In one case, the types of URLs visited provide a clue as to the sex of the user. For example, visits to URLs tailored to cosmetics may indicate that the user is a female. In another case, the name of the user may provide an indication as to the sex of the user. 
   In still other embodiments, the collection of user information includes behavioral characteristics of the user when interacting with the various electronic devices associated with the user. For example, behavioral characteristics collected as user information include URL histories, usage of devices, usage of applications, etc. 
   In one embodiment, the user&#39;s search behavior over the Internet as a behavioral characteristic is monitored for purposes of targeted data delivery. For example, raw data is collected to include the various URLs visited by the user. That is, the user information will identify which URL the user has visited, such as news groups, chatrooms, blogs, car URLs, printer URLs, etc. 
   For purposes of illustration only, an example is provided for reference throughout the application in which a user is interested in purchasing an inkjet printer. The user performs some research over the Internet and visits a URL associated with company A for purposes of researching an inkjet printer provided by company A. For purposes of collecting behavioral characteristics, the present embodiment is capable of collecting the fact that the user has visited the URL associated with company A. 
   In addition, collector  810  of  FIG. 8  collects various other raw pieces of information related to web browsing as behavioral characteristics. For example, collector  810  collects timing information in one embodiment in accordance with the present invention. That is, collector  810  collects a timestamp that identifies when a particular URL was visited (e.g., time and date). This is useful in determining how many times a particular URL was visited and with what frequency. Recent activity for a particular URL may indicate great interest in the subject associated with that URL. 
   In addition, various search terms used by the user are collected, by collector  810 , as a behavioral characteristic. That is, in the above example of a user researching printers, the search term “inkjet” is collected for targeting purposes. This is valuable to an inkjet manufacturer who is targeting users who are in the market of purchasing an inkjet printer. 
   In one embodiment, post processing of the raw data collected by collector  810  is necessary to determine behavioral characteristics. It may not be enough to collect that the user has visited a URL. Hence, embodiments in accordance with the present invention implement various post processing techniques to obtain further identifying characteristics of the user. The techniques discussed below show examples of post processing for illustrative purposes only and are not intended to be exhaustive. 
   In one embodiment in accordance with the present invention, post processing of the raw data determines if the user really visited the URL. For example, it is valuable to know whether the user just visited the URL briefly or interacted with the URL. An examination of whether the user scrolled through a page on the URL indicates how long and how interested the user was in that URL. This behavioral tracking indicates that the user not only visited the URL, but was also interested in the URL. As such, this user information is more accurate and relevant for use in targeted data delivery. 
   In addition, post processing of URL data indicates that the URLs visited recently include specific types of URLs. For example, the URL data indicates that the user was visiting URLs associated with car manufacturers and printer manufacturers. This post processing indicates that the user is interested in purchasing both a new car and a new printer. As such, car manufacturers and also printer manufacturers utilize this information for targeted delivery of their URLs. 
   Other post processing techniques are also useful in collecting user information. For example, in one embodiment, a spelling corrector is implemented to obtain a true representation of data. Text in e-mails and text messages from handheld devices are riddled with incorrect spellings in the interest of time. A spelling corrector in the post processing stage corrects the spelling so that accurate and relevant user information is collected. 
   In another embodiment, logs of e-mails are collected by collector  810  when performing the operation described at  710  of  FIG. 7 . That is, in one embodiment in accordance with the present invention, collector  810  collects a log of sent and received e-mails for purposes of targeted data delivery. Post processing of the e-mails is required to obtain useful user information. For example, a filtering of the text used in the e-mails identifies that the user is in the market for inkjet printers. Other text identifies other behavioral characteristics of the user, such as being a fan of a particular baseball team, etc. In still another embodiment, post processing of downloaded web pages is preformed by scanning text content of downloaded web pages using word frequency statistics as a possible input source. 
   Collector  810  of the present embodiment is also well suited to collecting behavioral activity that is not related to browsing the Internet. For example, in one embodiment, information specifying the use of various devices is collected by collector  810 . That is, a user utilizes a number of electronic devices, such as printers, cameras, cell phones, etc. Use of these devices is valuable in targeted data delivery to manufacturers of those devices since the user may be interested in purchasing another similar electronic device or accessory related thereto. 
   In addition, in various embodiments in accordance with the present invention, collection of location specific information related to the use of electronic devices is important. For example, the geographic locations of an electronic device indicate the location of the user of that electronic device. User information collected by collector  810  from a user identifies that that user travels a particular route when commuting to work. Using such information, it is determined that the user&#39;s route passes by a particular supermarket that is also located close to the user&#39;s home. The user information also includes how many times the user has passed the particular supermarket. In such an embodiment, this information is used to target that user for the delivery of advertising (e.g., coupons) enticing the user to use the nearby and frequently passed supermarket. 
   Other information that is collected by collector  810  when performing the operation described at  710  of  FIG. 7  might indicate that a user stores digital photographs on an electronic device (e.g., a PC). In addition, user information indicates that the user prints photographs from those digital photograph files. Using the previous example of a user in the market for an inkjet printer, a particular printer manufacturer targets the delivery of advertising for a color inkjet printer to the user who stores and prints digital photograph files using a competitor printer. 
   Still other information that is collected by the collector  810  is the type of music that the user listens to, in one embodiment. Music tracks typically include additional information that identifies the band, artist and type of music that the electronic device is playing. This information also is collected and is associated with the user. 
   In addition, user information that is collected by collector  810  includes hardware configuration of the electronic devices associated with the user. For example, the particular machine configurations of the various electronic devices associated with the user are collected. That is, the particular models of the various devices, peripheral components, drivers installed, interconnect connections (e.g., DSL, cable, telephone), and other associated hardware is collected. As such, the user information includes the model and date of manufacture of the printer associated with the user. The various hardware configuration information indicates when the user may need a new printer cartridge. 
   In still other embodiments, the software configuration is collected by collector  810  for purposes of targeted data delivery. That is, the types and versions of applications that are loaded onto a particular electronic device (e.g., PC) are collected. In addition, the usage of those types of applications is collected by collector  810 . For example, information that indicates a software configuration including a high level publishing application and additional information indicating high use of that application are useful in targeting that particular user for printers that are suitable for desktop publishing. 
   Additionally, user information indicates how often a particular application crashes in one embodiment. If a particular application crashes frequently, a manufacturer of a competitor application uses this information to target the delivery of advertising data to the user whose application frequently crashes. 
   Still other forms of collection of user information are possible. For example, in one embodiment, the collection of user information is achieved through voice over Internet protocol (VOIP) technology. That is, voice recognition software or VOIP technology is implemented to collect user information related to a user. As an example, the VOIP technology indicates that a user is interested in taking a vacation, in one embodiment. A travel agency uses this information to target the delivery of trip advertising to this particular user. 
   Other embodiments of the present invention are able to perform post-processing of the raw data collected from the electronic devices associated with the user to identify which user is associated with the data. For example, multiple users may have access to an electronic device (e.g., a PC). The collection of information associated with the electronic device is most useful if the data is associated with a particular user. 
   Various post processing mechanisms are employed to identify the user. For example, keystroke rhythm and timing are analyzed to determine which user is currently using the electronic device. One user is identified as having a quick rate of keystroke typing in one embodiment. Another user is identified as having a slower rate of keystroke typing. In addition, users are identified based on the rhythms of the keystroke typing. A smooth rhythm indicates one user, while a choppy rhythm indicates another user. 
   In still other embodiments, behavioral characteristics of users are used to identify users using other types of electronic device. This is helpful in an environment where multiple users are interacting with the same set of electronic devices. As such, behavior exhibited to a particular user is correctly associated with that user. 
   Now returning to  FIG. 7 , at  720 , embodiments in accordance with the present invention generate a user profile based on the user information collected in  710 . Correspondingly, as shown in  FIG. 8 , user profile generator  820  implements the operation described at  720  of  FIG. 7 . As will be described below the user profile generator  820  formats the user profile to enable the use or selection of particular characteristics for purposes of targeting users exhibiting those characteristics. In the present embodiment, the components of the system  800  including the user profile generator  820  are located at the user layer of  FIG. 5 . More specifically, in one embodiment in accordance with the present invention, the user profile is generated by the user profile generator  820  that is contained within the targeting mechanism  571 . 
   In one embodiment in accordance with the present invention, user profile generation by the user profile generator  820  is only performed with the explicit authorization of the user. That is, the user profile generator  820  functions under the sole control of the user. As such, in one embodiment, the user is capable of requesting that user information not be collected. In another embodiment, the user is capable of deleting the user profile, or transferring the user profile to another location, or taking other actions as controlled by the user, etc. 
   In another embodiment in accordance with the present invention the user profile is generated by an entity that functions as a proxy to the user. That is, the user specifically authorizes the proxy to generate the user profile and to perform operations associated with the targeted delivery of data. For example, an Internet Service Provider (ISP) functions as a proxy, in one embodiment. 
   As such, wherever the user profile is generated and maintained by a user profile generator  820 , the user has confidence that the user information in the user profile is not compromised and that the user has control over that user information. For instance, whether the user profile generator  820  is located on the hard drive of the electronic device associated with the user, or the user profile is maintained at the ISP, or on a stored database remotely located from the user, the user profile generator  820  is still thought of as an extension of the user&#39;s electronic device, since user information is not required to be released. 
   Furthermore, for purposes of the present application, the term “without requiring a release of any of said user information in said user profile,” or any substantially similar term or phrase, is intended to refer to the restriction that the user information is not released outside of the system that is implementing the delivery of data. More specifically, for purposes of the present application the term “without requiring a release of any of said user information in said user profile,” or any substantially similar term or phrase, is intended to refer to the restriction that the user information is not required to be released beyond a data distribution layer as denoted by line A-A of  FIG. 5 . As described previously with relation to  FIG. 5 , embodiments of the present invention do not require the release of user information beyond particular layers that define the system. In one embodiment of the present invention, the release of user information does not occur beyond a data distribution layer as denoted by line A-A. In another embodiment of the present invention, the release of user information does not occur beyond a device that is located between a user layer and the data distribution layer, as denoted between lines C-C and D-D. In still another embodiment of the present invention, the release of user information does not occur beyond a user layer denoted by line D-D. 
   In one embodiment, the user profile includes attributes that are associated with the user information. As such, the attributes represent the characteristics (e.g., behavior, personal information, machine configuration, etc.) associated with the user. That is, user information collected by collector  810  is transformed into an attribute by the user profile generator  820 . For example, an attribute indicates a URL that has been visited. Another attribute indicates search terms. Still other attributes indicate characteristics particular to a user, as previously described. 
   In another embodiment, the user profile generator  820  formats these attributes by grouping the attributes into one or more databases. That is, the attributes are stored in a database by the user profile generator  820 . For example, the database includes attributes indicating the URLs visited. The same or another database includes all search terms used. In addition, attributes in the database expire after a period of time. As such, one or more databases include all the attributes associated with user information identifying characteristics of the user. These attributes are formatted to be included in a particular database for searching. Thus, the attributes are used for the targeted delivery of data. 
   In the present embodiment, the database storing the user information is located at the user layer as described in  FIG. 5 . For example, the user profile B is shown stored in the electronic device  575  at the user layer. In such an embodiment, control over the user information associated with the database is tightly monitored by the user if necessary. 
   In other embodiments of the present invention, the user profile generator  820  stores the user profile at devices remote from the user layer. In one such embodiment, the user profile generator  820  stores the user profile at the data distributor layer (e.g., user profile N), or some location between the data distributor layer and the user layer (e.g., user profile A). The protection of privacy of the user information is paramount even with the remote storage of the database of user information. Embodiments in accordance with the present invention ensure a user&#39;s privacy by implementing various secure communication means, such as communication over a secure socket layer, communication using encryption, and other secure communication approaches well known in the art. 
   In still other embodiments, the database of user information corresponding to the user profile is split between various locations. In such embodiments, the user profile generator  820  stores sensitive user information in secure locations at the user layer, or data distribution layer, or some layer in between. Less sensitive user information (e.g., public information regarding the user) is stored by the user profile generator  820  in less secure areas in various locations (e.g., a general database at the data distributor  520  of  FIG. 5 ). 
   Also, the user information in the user profile is used to automatically fill out electronic forms, in one embodiment. That is, the user information is parsed to select various terms that are applied to an electronic form. 
   In one embodiment, the user has editing capabilities with regards to an associated user profile. That is, the user adds additional information to the user profile and subtracts information from the user profile. However, the editing capability is limited to prevent user fraud. For example, user editing of profiles is limited to prevent a user from lying about information in the user profile in order to increase chances for satisfying selection criteria. In such an embodiment, the user is trying to accumulate incentives tied to the delivery of data without truly having an interest in that data. That is, the user is trying to make money by automatically trying to satisfy as many queries as possible. Prohibiting the editing of critical features of the user profile prevents misdirected and fraudulent targeting of users. 
   As a further example, in another embodiment of the present invention, the user is allowed to add or edit information in certain marked fields, such as “name” or “address.” However, the user is restricted from editing information in most fields (e.g., URLs visited). 
   In still another embodiment, fields are editable by users in a limited fashion. That is, the present embodiment allows changing most fields to “not telling” rather than simply deleting their contents. A “not telling” value matches neither positive criteria nor negative criteria. As such, this prevents a user from hiding the fact that the user did not visit company C. Thus, the user is prevented from falsely claiming an incentive for an inkjet advertisement from company C, for example. 
   At  730 , the present embodiment receives an offer soliciting delivery of data. More specifically, in one embodiment, the offer includes a query that defines selection criteria of targeted user profiles. In other embodiments in accordance with the present invention, the offer includes the location of the data to be offered for delivery or the data itself, and optionally an offer of compensation for taking delivery. Additional information is optionally included within the offer, such as an expiration period, etc. As shown in  FIG. 8 , the offer receiver  830  is used to receive the offer. In the present embodiment, the components of the system  800  including the offer receiver  830  are located at the user layer of  FIG. 5 . To put it into the context of  FIG. 5 , the offer is received at the electronic device  575  that includes the offer receiver  830  contained within the targeting mechanism  571 . In one embodiment in accordance with the present invention, the offer is received by the offer receiver  830  from the data distributor  520  through the broadcast layer. 
   The offer is used for the solicitation of the delivery of data, in one embodiment. That is, the query within the offer is used to determine if the user receiving the offer is a user who should be targeted. The offer is not specifically targeted to particular users. Instead, the offer is forwarded to a broad group of users. Thus, the information contained within the offer is used to select and target the delivery of data to particular users, as will be described further below. 
   In the present embodiment, the query included within the offer includes selection criteria used for targeting one or more user profiles. That is, the selection criteria are used to characterize particular users to whom the data is targeted. As such, the selection criteria contain certain characteristics that are important in distinguishing the targeted user profiles from other user profiles. In this way, delivery of data is targeted to particular users. 
   In one embodiment, the selection criteria contain positive characteristics of the targeted user. For example, these positive characteristics might be actual search terms used, actual URLs visited, actual income, etc. 
   In other embodiments, the selection criteria contain negative characteristics of the targeted user. For example, these negative characteristics might be search terms that are not used in conjunction with search terms that are used, URLs that have not been visited in a period of time, etc. 
   In one embodiment, the query included within the offer is represented as a Boolean expression. The Boolean expression includes a number of characteristics. The characteristics in the expression are connected with “and,” “or,” and “not” Boolean operands. That is, the query includes a number of selection criteria connected with the above mentioned operands. In another embodiment, the query is represented as an arithmetic expression. 
   Using the previous example of a user in the market for an inkjet printer, the selection criteria may help target or identify a user who is interested in purchasing an inkjet printer. For example, the selection criteria contain characteristics that define a user who has, at least twice, spent time on the URLs of company A and company B, both of whom manufacture inkjet printers, with pages from each domain containing the term “inkjet” and “printer,” but who has never visited the URL of company C. 
   In addition, in another embodiment, to prevent fraud where users try to satisfy as many selection criteria as possible in order to receive incentives, the query is conditioned in favor of users who actually have a favorable behavior. For example, in the case of targeted advertising, the query is directed to users who actually make purchases of an advertised product (e.g., a cosmetic line). In other words, the selection criteria are so specific that it is unlikely that a fraudulent user can meet all of the specific criteria automatically. 
   Returning to  FIG. 7 , at  740 , the present embodiment determines if the user profile satisfies the selection criteria. That is, the user profile is compared against the selection criteria. As shown in  FIG. 8 , the comparator  840  is used to access the user profile and to make the determination of whether the user profile satisfies the selection criteria in the query. In the present embodiment, the components of the system  800  including the comparator  840  are located at the user layer of  FIG. 5 . To put it into the context of  FIG. 5 , the determination of whether the user profile satisfies the query is made at the electronic device  575  that includes the comparator  840  contained within the targeting mechanism  571 . 
   In embodiments in accordance with the present invention, the determination by the comparator  840  that a user&#39;s profile satisfies the selection criteria is made without requiring a release of the user information. Specifically, the user profile satisfies the selection criteria defined in the query. In other words, the determination that the user should be targeted for the delivery of data is made in such a way that the user has control over the user information utilized, and the operations performed, by comparator  840  in making the determination that the user is a targeted user. As such, this targeting of the user is performed without requiring a release of the user information. 
   In accordance with one embodiment of the present invention, the determination that the user is targeted for the delivery of data is made by the comparator  840  at the electronic device  575  associated with the user. That is, the components of system  800  that operate to perform the targeted delivery of data are located on the electronic device  575  that the user controls. In addition, the user profile and the information contained therein are also stored at the electronic device  575  that is in the control of the user. As such, the targeting of the user is made without requiring any release of the user information outside of the user layer of  FIG. 5 . More specifically, in the present embodiment, the targeting of the user is made entirely within the electronic device  575 , and the user information used to target the user never leaves the electronic device  575 . As such, the user is anonymously targeted for the delivery of data. 
   In one such embodiment as mentioned above, components in system  800  are stored at an electronic device in the control of the user. However, other embodiments of the present invention are well suited to storing the user profile and components of system  800  at a location that is remote from the user and in a secure manner such that the user information is still not required to be released. That is, the determination that the user profile satisfies the selection criteria using information in the user profile is made in a secure location that does not require a release of the user information. 
   For example, referring back to  FIG. 5 , the targeting mechanism  581  and the user profile N is located at the data distribution layer that is outside of the user layer. However, the transfer of the user information to the data distribution layer for storage in user profile N is made over a secure data link  587  (e.g., a secure socket layer) that ensures that the user information in user profile N is not released. 
   Moreover, the use of the information by the targeting mechanism  581  to make the determination of whether the user profile satisfies the selection criteria is made without requiring the release of the user information outside of the targeting mechanism. More specifically, the user N is targeted without requiring a release of information outside the data distribution layer. Since the data distribution layer is a secure layer, the user profile N, the targeting mechanism  581 , and the information used by the targeting mechanism  581  to determine that user N is targeted for the delivery of data will not leave the secure data distribution layer. As such, user N is targeted for the delivery of data without requiring a release of information. 
   As such, embodiments in accordance with the present invention are capable of the targeted delivery of data without requiring a release of user information. For purposes of the present application, the term “without requiring a release of any of said user information in said user profile,” or any substantially similar term or phrase, is intended to refer to the restriction that the user information is not released outside of the system that is implementing the delivery of data. More specifically, for purposes of the present application the term “without requiring a release of any of said user information in said user profile,” or any substantially similar term or phrase, is intended to refer to the restriction that the user information is not required to be released beyond a data distribution layer as denoted by line A-A of  FIG. 5 . As described previously with relation to  FIG. 5 , embodiments of the present invention do not require the release of user information beyond particular layers that define the system. In one embodiment of the present invention, the release of user information does not occur beyond a data distribution layer as denoted by line A-A. In another embodiment of the present invention, the release of user information does not occur beyond a device that is located between a user layer and the data distribution layer, as denoted between lines C-C and D-D. In still another embodiment of the present invention, the release of user information does not occur beyond a user layer denoted by line D-D. 
   For example, in one embodiment, the targeting mechanism, a subsystem of the system that is implementing the delivery of data is implemented within the user layer below line D-D of  FIG. 5 . That is, the determination that a user is a targeted user for the delivery of data is implemented within this layer. As such, user information is not released to entities outside of the user layer. 
   In another embodiment, the targeting mechanism is implemented within the data distribution layer between lines A-A and B-B. That is, the targeting of users is implemented within this layer. As such, user information is not released to entities outside of the data distribution layer. It should be noted however, that even when at least one of the components of  FIG. 8  resides within the data distribution layer (e.g., at data distributor  520 ), embodiments in accordance with the present invention do not require a releasing of the user information. That is, although user information may be utilized by at least one component of  FIG. 8  at the data distribution layer, user information is still securely protected and is not divulged to entities outside of the system implementing the targeted delivery of data. 
   In still another embodiment, the targeting mechanism is implemented within a layer between the data distribution layer and the user layer of  FIG. 5 . For example, the system is implemented at the system  560  that is located within a layer defined between lines C-C and D-D. That is, the targeting of users is implemented within this layer. Again, in the present embodiment user information is not released to entities outside of system  560  that is located at the layer defined between lines C-C and D-D. 
   In still another embodiment, the targeting mechanism is implemented within a combination of the layers of  FIG. 5  below line A-A. That is, the targeting of users is implemented within these layers. As such, user information is not released to entities outside of these layers. 
   For example, a manufacturer (e.g., data source  511 ) who makes a product, widget A, can market widget A to a particular group of customers. This group of customers is a targeted group. The manufacturer defines the characteristics exhibited by the targeted group of customers generally, and these characteristics are used as selection criteria. That is, the manufacturer is not specifically targeting an identifiable customer, but a group of customers that happen to exhibit common characteristics. 
   Thereafter, in accordance with an embodiment of the present invention, user information is used by a targeting mechanism (e.g.,  561 ,  571 , or  581 ) to determine if an associated user (e.g., user A, B or N) meets those characteristics. More specifically, the present embodiment determines if the user profile satisfies the selection criteria without requiring a release of user information. As such, in accordance with the present embodiment, the targeting mechanism (e.g.,  561 ,  571 , or  581 ) waits to receive the selection criteria, and then at that point determines if the user is one of the targeted groups by determining if the user profile satisfies the selection criteria. At no point is the user information required to be released, especially to the manufacturer of widget A, the data source  511 . 
   As such, in such an embodiment of the present invention user information that a particular user has exhibited particular characteristics (e.g., performed specific operations on a PC) that indicates potential interest in widget A is not divulged to the manufacturer of widget A. Instead the manufacturer perceives that there may be users who are interested in widget A, and perceives that these targeted users exhibit perceived characteristics. These perceived characteristics are used to select targeted users using the targeting mechanism. 
   In contrast to embodiments in accordance with the present invention, conventional spyware routinely divulges confidential data to third party sources, thereby compromising the privacy of unsuspecting individuals. For example, a common technique in conventional spyware is to place an application on a user&#39;s PC to spy on an individual&#39;s behavior and activity. Typically, the user does not want these spying applications loaded onto the user&#39;s PC. However, the spyware is commonly and surreptitiously loaded onto the user&#39;s PC with authorization that is fraudulently or deceptively obtained. For example, the user unknowingly accepts the spyware onto the system when downloading desired files or applications that unfortunately come packaged with the spyware. That is, spyware is used to spy on information, and spill that information to third party advertisers so that they can target individuals with unwanted advertisements (e.g., unwanted e-mails). As such, spyware is incapable of providing the targeted delivery of data without requiring a release of user information, as provided in accordance with embodiments of the present invention. 
   For example, a spyware company would gather information for a plurality of individuals, ship this information to a remote server, create logical representations of each of these individuals, and then market this information to advertisers. The advertisers can then see which individuals can be targeted for specific advertisements. 
   In a more refined approach, a spyware company would gather information for an individual, ship this information to a remote server, create a logical representation of the individual, come to a conclusion that the individual is in the market for an diamond ring, and markets that individual to diamond merchants. That is, the spyware company divulges information about the individual so that interested diamond merchants can directly contact that individual. 
   In addition, in direct contrast to embodiments of the present invention, conventional spyware commonly gathers data about the individual and then transfers the data to a remote site for further processing. In the case of spyware, personal data is often transferred without the individual even knowing that the transfer is happening. The transfer frequently occurs in the background when the individual is connected to the Internet. During the connection, the individual&#39;s PC is communicatively coupled to a remote server for the transfer of the personal data deleteriously gathered by the spyware. This personal data is then used at a remote location without knowing authorization by the individual. 
   On the other hand, in embodiments of the present invention, the user is targeted for the delivery of data in a manner that ensures the protection of the user&#39;s privacy. That is, the user information in the user profile is protected even though it is used to determine if the user profile satisfies the selection criteria. 
   The protection of the user information is particularly important if the user has sensitive user information stored in a user profile that the user never wants released. For example, the user profile of a user includes sensitive medical information that might be particularly private or embarrassing to the user. The user may have been doing research for a medical condition inflicting the user. As such, the user may not want this information released to an outside source for fear of being identified as someone having such a condition or affliction. 
   Embodiments of the present invention are able to protect the user&#39;s privacy, especially the fact that the user has recently searched web sites that are directed to the user&#39;s condition, and also anonymously target the user for the delivery of data. That is, the user is targeted for the delivery of data without requiring a release of user information. In such an embodiment, the user continues to freely conduct research for the user&#39;s medical condition, and also be targeted for data related to the conditions (e.g., advertisements for medication treating the user&#39;s condition). Furthermore, the user can be targeted for such data without any concern that user information will be traced back to the user indicating that the user has recently visited web sites related to the user&#39;s condition. 
   As another example of the benefits conferred by embodiments in accordance with the present invention, using the previous example of a user in the market for an inkjet printer, the user may not want information released indicating that the user is interested in an inkjet printer manufactured by company C. For example, the user may work for company A that also manufactures inkjet printers. For whatever reason, however the user favors inkjet printers from a competitor, company C. If the user information were to be leaked that could identify that the user is interested in an inkjet printer from company C and that information were obtained by company A, then the user&#39;s standing within company A may be severely compromised. Fortunately, embodiments of the present invention allow the user to continue to freely search for inkjet printers from company C. In addition, these embodiments allow that fact to be collected as user information. As such, this user can be targeted for delivery of data related to inkjet printers from company C under a system that protects the user&#39;s privacy, since there is no requirement that user information be released for the targeted delivery of data. 
   Moreover, embodiments of the present invention are well suited to attracting users to a system or network that performs the targeted delivery of data. Normally, a user may be reluctant to participate in a system that may be construed as secretly collecting information related to that user for fear that the information may be released to others. However, embodiments of the present invention are able to collect user information in way that ensures that the user information is not required to be released for the targeted delivery of data. That is, the user knows that the user information is used only for the targeted delivery of data. As such, embodiments of the present invention are able to attract users to the system of targeted data delivery, especially if enticed with incentives for the delivery of data. 
   Moreover, in embodiments in accordance with the present invention, user information is only released upon explicit authorization by the user. That is, in embodiments in accordance with the present invention user information is required to be released in order for the user to receive data and any corresponding incentives. However, the user information is only released to the data distributor (e.g., distributor  520  of  FIG. 5 ) in a secure fashion and the user information is not further released beyond the data distributor on an individual basis, in one such embodiment. The data distributor releases user information in an aggregate fashion that does not identify any users. As such, the user&#39;s privacy is ensured as the user information is never identified with the user, and is never released in a manner that connects the user to the user information. 
   Returning to  740  of  FIG. 7 , in one embodiment, satisfaction of the selection criteria is performed through a vector matching process. More specifically, the vector matching process is represented by a function F(x), where x is the query. For example, x is the vector defining the selection criteria of the target profile described previously. Although various embodiments are described using a vector matching process for clarity and brevity, other embodiments are well suited to other techniques that are capable of determining whether the user profile satisfies the selection criteria. 
   In one particular implementation, the query, x, is a vector that takes all of the words of web pages that are theoretically visited by a targeted user. The vector is over a high dimensional space. 
   In one embodiment, the function F(x) is a dot product of the query vector and the user profile in vector form. Various techniques are used to determine if the user profile satisfies the selection criteria as defined in the query. For instance, in one case, if the dot product exceeds a threshold value, then the user profile satisfies the selection criteria. 
   Also, if the dot product is within a predetermined numerical range, this indicates a highly valued match. That is, the user profile closely matches or satisfies the selection criteria. This rating of matches is used to determine the appropriate incentive to be paid to the user for the delivery of data, as will be described more fully below in relation to  910  of  FIG. 9 . That is, in accordance with embodiments of the present invention a highly valued match will be paid a greater incentive than a lower valued match. 
   In addition, a selection of dot products with random vectors is taken. That is, the function F(x) is a randomly generated function. It should be pointed out, that in other embodiments of the present invention, the function F(x) is not a randomly generated function. In one embodiment, the randomly generated function can be used to select a targeted user. For example, the result of F(x) provides numbers that compactly represent a user. This compact representation can be used to filter and determine if a user satisfies the selection criteria. In one embodiment, an inference module is used to determine if the compact representation satisfies the selection criteria. 
   In one embodiment, if the user profile does not satisfy the selection criteria in the query, the offer containing that query is kept for a period of time. That is, the offer is stored. For example, in one embodiment in accordance with the present invention the query is stored at the targeting mechanism  571  used for targeting the delivery of data. At a subsequent time, when the user profile has changed, the user profile is again compared to the selection criteria to determine if the user profile satisfies the selection criteria. 
   Returning to  FIG. 7 , at  750 , the present embodiment determines if the data should be delivered to the user when the user profile satisfies the selection criteria, in accordance with one embodiment of the present invention. A determining module  646  makes the determination of whether the data should be delivered to the user. More specifically, in one embodiment, the determining module  646  presents the offer to the user through an electronic device of the set of electronic devices associated with the user. As such, the user can actively accept the offer for the delivery of the data. In one embodiment, the offer includes information that indicates which user information in the user profile will be released should the user accept delivery of the data. As a result, the data is delivered when the offer is accepted by the user, and conversely, the data is not delivered when the offer is not accepted by the user. 
   At  760  the delivery of data is initiated if it has been determined that the data should be delivered to the user. That is, the data is delivered after it has been determined that the user has accepted the offer for the delivery of data, in one embodiment. More specifically, the initiation of the delivery of data is achieved without releasing any user information in the user profile, except for an indication that the user profile satisfies the selection criteria. In addition, the performance of the collecting operation at  710 , the generating operation at  720 , the receiving operation at  730 , the determining operation at  740 , the determining operation at  750  and the initiating operation at  750  are performed without requiring a release of the user information in the user profile except for the indication that the user profile satisfies the selection criteria. 
   More specifically, the indication that the user profile satisfies the selection criteria is not required to be released beyond line A-A of the data distribution layer in  FIG. 5 , in accordance with one embodiment of the present invention. In one embodiment, the indication that the user profile satisfies the selection criteria does not provide any user information, and only informs that the user profile satisfies the selection criteria. In another embodiment, the indication that the user profile satisfies the selection criteria is limited to user information that is used to satisfy the selection criteria. As such, because the user profile is closely guarded to protect the privacy of the user and since the determination of whether the user profile satisfies the selection criteria is made in a secure location, user information is not required to be released beyond line D-D of the user layer of  FIG. 5  for purposes of targeted delivery of data except for the indication that the user profile satisfies the selection criteria. Hence, as stated above, the user&#39;s privacy is protected in a system that is capable of targeted data delivery. 
   In another embodiment, the initiation of the delivery of data is achieved without releasing any user information in the user profile beyond the user layer. That is, in one embodiment, in determining if the user profile satisfies the selection criteria, user information is not required to be released beyond line D—D of  FIG. 5 . For example, an offer can be multicasted to a plurality of users. More specifically, the offer forwarded to an electronic device associated with a user includes the query and either the data, a link to the data, or some form of accessing the data. If no compensation is required, as soon as the user profile associated with the user satisfies the selection criteria, initiation of the delivery of data is achieved without releasing any user information in the user profile. That is, the determination that the user profile satisfies the selection criteria and the initiation of the delivery of the data to the user occurs entirely below line D—D of  FIG. 5 , and does not require any release of user information beyond line D-D of  FIG. 5 . 
   As shown in  FIG. 8 , the delivery module  850  initiates delivery of the data. In the present embodiment, the components of the system  800  including the user profile generator  820  are located at the user layer of  FIG. 5 . To put it into the context of  FIG. 5 , the initiation of the delivery of data is performed by the delivery module  850 , which is contained within the targeting mechanism  571 . 
   Various methods for initiating delivery of the data are implemented. In one embodiment, the message containing the query also contains a link to the data. As such, once the present embodiment determines that the user profile satisfies the selection criteria, the link is invoked to initiate delivery of the data to a display that is viewed by the user. For example, the data is stored at the data distributor  520  of  FIG. 5 . In one such embodiment, invoking the link activates the delivery of the data by the data distributor  520  to the user who invokes the link. In this way, the user readily receives the data targeted to that user. 
   In one embodiment, the data is delivered to the user via a display that is present on the electronic device associated with the user. The display is any display viewable by the user. In one embodiment, the display is on a PC from which user information is collected. In another embodiment, the display is on a device that performs the various functions for targeting delivery of data, such as system  800  of  FIG. 8 . 
   In still other embodiments, the display is on any device accessible by the user. That is, the device is a dumb display, which presents the data. For example, the display is a television. 
   In another embodiment, the data is delivered through a standard telephone communication network. For example, the data is faxed to a user. In another example, the user is directly called in a telemarketing system. That is, once the user is targeted, the user is contacted via phone. The data is automatically played back in a recording to the user, or a telemarketer converses with the user to discuss the data. 
   In still another embodiment, the data is delivered in the form of e-mails. That is, once the user is targeted, one or more e-mails containing the data are delivered to the user. 
   Also, in another embodiment the data is customized to the user. That is, before the data is delivered to the user, user information is used to tailor presentation of the data to the user. In the example of the inkjet purchase by a user, the data is customized with a comparison of an inkjet printer of Manufacturer C to inkjet printers of Manufacturers A and B. For instance customizing the data includes text promoting the benefits of an inkjet printer by Manufacturer C, as follows: “Reasons that an inkjet printer from Manufacturer C is superior to inkjet printers from Manufacturers A or C.”. 
   Turning now To  FIG. 9 , a flow diagram illustrating operations for determining if an offer for the delivery of data has been accepted is described, in accordance with one embodiment. Additionally, flow diagram  900  provides further details related to flow diagram  200 B of  FIG. 2B . 
   At  910 , the present embodiment determines if a user-selected relevancy criterion has been satisfied before the initiation of the delivery of data to the user. That is, both the user-selected relevancy criterion and the selection criteria must be satisfied before the data is delivered to the user. 
   In one embodiment, the relevancy criterion measures the value to the user of viewing the data. That is, the relevancy criterion is an economic mechanism designed to set a value, as consideration, for the user&#39;s attention. For example, the relevancy criterion is some function that translates the user&#39;s attention to cash, goods, services, etc. As such, the relevancy criterion is a minimum value that is paid as consideration to the user upon delivery of the data and viewing the data. In other words, in such an embodiment, an incentive is paid to the user for the delivery of the data. 
   In one embodiment, the user-selected relevancy criterion is an “ask price” that is selected by the user. That is, the user-selected relevancy criterion is a minimum ask price that must be met before the data is delivered to that user. The ask price is included within the user profile. For example, a bid price is included within an offer soliciting the delivery of data. The bid price indicates the incentive paid to a user for the delivery of the data to the user, and the viewing of the data if required. As such, before the data is delivered, the bid price must equal or exceed the ask price in order to satisfy the user-selected relevancy criterion. 
   Other forms of consideration for the relevancy criterion are coupons, frequent flier miles, points that are accumulated for purchasing awards, etc. In one particular embodiment, the user-selected relevancy criterion corresponds to points for the purchase of a PC or printer cartridge, or some other manufactured goods or services. That is, once the user accumulates enough points, a free or reduced price PC or printer cartridge is made available to the user. In accordance with some embodiments of the present invention, the user is resupplied with printer cartridges through the receipt of the incentives paid for viewing data targeted to that user. 
   In addition, there are different relevancy criterion for various types of data. For example, a user might have a greater interest in sports than in treatment medications. As such, the user may want more data related to sports and less data related to treatment medications. Thus, one relevancy criterion is set to a lower threshold to attract sports data, and another relevancy criterion is set to a higher threshold to detract data regarding treatment medications. 
   In one embodiment, the relevancy criterion is set using a sliding bar, as shown in  FIG. 10 .  FIG. 10  is a diagram of a display  100  displaying an interface to an application that provides targeted delivery of data to a user. In the display  100 , a sliding bar  1010  under “preferences” sets the minimum user-selected relevancy criterion as a threshold value (e.g., ask price). As shown in display  100 , the threshold is set to 64 cents. Movement of the pointer in the sliding bar  1010  increases or decreases the threshold. The sliding bar  1010  is shown for illustrative purposes only as other methods for setting the threshold are used in other embodiments. 
   In still other embodiments, the relevancy criterion is a constraint set by the user. For example, a threshold limits the number of offers that are accepted in a month. In one embodiment, if the threshold has been exceeded, then data will not be delivered to the user. On the other hand, if the threshold has not been exceeded, then the data will be delivered to the user. In another embodiment of the present invention, the relevancy criterion is manipulated internally to limit the number of offers presented to the user to the requested threshold. 
   In other embodiments, the relevancy criterion is automatically set. That is, based on general criteria set by the user, the relevancy criterion is automatically selected. For example, the threshold in sliding bar  1010  is moved as a function of the number of items of data that have been delivered to spread out a monthly quota of delivered data. 
   The interface in display  1000  is reached by invoking the icon  1120  of  FIG. 11 . The display  1100  of  FIG. 11  illustrates the user interface that implements features of the targeted delivery of data when minimized. At the bottom tool bar  1140 , the ML icon  1120  represents the application used for targeting the delivery of data. Block  1130  provides various user options available from the minimized application. 
   In addition, a summary  1110  of the available amount of consideration that is available to the user is shown. For example, the summary  1110  shows that $2.45 in cash is available for the user to earn if they view all the relevant data. This available amount is repeated in display  1000  at block  1020 . 
   Returning to  FIG. 9 , at  920 , an offer is presented for the delivery of data when the user profile satisfies the selection criteria. That is, before the data is delivered, the user has a choice of whether or not to accept delivery of data for viewing, in accordance with one embodiment. For example, the user bases a decision on whether the incentive or payment associated with the delivery of data is worthwhile to the user. 
   In accordance with embodiments of the present invention, the offer is presented to the user in an unobtrusive manner. That is, the offer need not be blatantly presented to the user, as in a pop-up advertisement in the conventional art, since the user&#39;s attention has already been filtered and the user most likely is interested in the data. For example, in the display  1000  of  FIG. 10 , the user interface presents a list of offers available to the user. As shown in  FIG. 10 , there is one offer (“Spam Killer”) that is available to the user. The number of offers shown in  FIG. 10  will vary depending on how many offers have selection criteria that are satisfied by the user profile. 
   Once the option  1030  in  FIG. 10  is selected, information is provided to the user that is related to the offer. For example, a brief summary of the data source (e.g., printer company C) is provided. In addition, a brief summary of the data is also provided to the user. Other additional information is provided to the user so that the user may make an informed decision as to whether the delivery of data is desired. In another embodiment, this information is shown directly as part of display  1000 . 
   Returning to  FIG. 9 , the present embodiment determines if the user is interested in the offer at  930 . If the user is interested, the present embodiment proceeds to  940 . On the other hand, if the user is not interested, then the present embodiment ends. 
   At  940 , the present embodiment optionally presents to the user a summary of the user information that will be released if the offer is accepted. That is, user information that is used to satisfy the selection criteria will be released to the data distributor if the offer is accepted and is summarized for the user before being released. In this manner, the user can determine if he wants to release the user information. The release of user information is additional consideration for payment of the incentive to the user for accepting delivery of data and viewing the data. 
   The user&#39;s privacy is protected since only user information that is used to satisfy the selection criteria is subject to release and not the entire user profile. As such, sensitive user information that the user would like to keep private would not be released if it is not used to satisfy the selection criteria. For example, returning to the inkjet purchasing example, if the user is an employee of printer company A and is using a company computer, the user may not want information released indicating that URLs visited included a URL for a competitor of company A. 
   At  950 , the present embodiment optionally determines if the user authorizes release of the user information. At  950 , the present embodiment allows the user to stop the release of user information. That is, when the user decides that the user information is too private, the present embodiment allows the user to deny authorization for the release of the user information. When authorization is not given, then the present embodiment ends. As such, the delivery of data does not occur. 
   On the other hand, if authorization is given, then the present embodiment proceeds to  960  to initiate the delivery of data, as previously described in relation to  750  of  FIG. 7 . That is, the data is delivered to the user for viewing. 
   At  970  the user information is released and payment of the incentive is made. In one embodiment, the information is released to the data distributor (e.g., data distributor  520  of  FIG. 5 ). In such an embodiment, the privacy of the user information has not been significantly compromised since it is in the data distributor&#39;s best interest to protect the privacy of its users in its network of users. As such, the data distributor provides aggregate data without violating any privacy interests to the data sources. 
   Automated viewing of the data facilitates fraudulent manipulation of the targeted data delivery system in order to automatically receive incentives paid for the delivery and user viewing of data without the user themselves viewing the data. That is, fraudulent users who satisfy the selection criteria and the relevancy criterion may try to have software view the data for them. However, in one embodiment, to prevent fraud computer generated Turing tests are implemented to defeat the automated viewing of the data. Other embodiments are well suited to implementing other means for determining when the data is automatically viewed fraudulently. 
   In another embodiment in accordance with the present invention, data is delivered before the release of information is authorized. As such, even though the data has been delivered and the user has viewed the data, the user has a last chance to stop the release of user information. That is, when the user decides that the user information is too private, the user chooses to deny authorization for the release of user information. When authorization is not given, the present embodiment ends. However, the incentive associated with viewing the data is not presented to the user as the user did not release the related user information. 
   In still another embodiment, after the user accepts the delivery of data and authorizes the release of user information, an additional tag-on offer is presented to the user for the delivery of additional data. The additional data is unrelated or related to the previously delivered data. For example, an offer is presented to the user for the delivery of three future e-mails from the advertiser in return for a cash incentive of five dollars. 
     FIG. 12  is a diagram of an exemplary system  1200  that is capable of targeted data delivery between advertisers and consumers, in accordance with one embodiment of the present invention.  FIG. 12  is presented here to apply the methods and systems of  FIGS. 7-11  in one particular implementation of targeting the delivery of advertisements. 
   In  FIG. 12 , the system  1200  links a plurality of advertisers  1210  to a network of consumers  1250  for the targeted delivery of advertisements. More specifically, the present embodiment links an advertiser with a particular consumer for the targeted delivery of an advertisement. For purposes of illustration, the system  1200  is described in the context of delivering advertisements, but is also used for the delivery of other types of data, such as announcements, messages, etc. Additionally, the system  1200  is capable of achieving delivery of an advertisement that is targeted to a user based on information in a user profile associated with the consumer without requiring a release of user information in the user profile to protect privacy. 
   The architecture of system  1200  functions similarly to the architecture in network  500  of  FIG. 5 . For example, an advertiser uses a network owner  1220  to broadcast an offer  1230  through a broadcast layer  1240  to reach a network of consumers  1250 . That is, the offer  1230  is propagated to each of the network of consumers  1250 . The offer  1230  includes a query  1231 , an advertisement link  1233 , and a bid price  1235 . If the selection criteria in query  1231  are satisfied by any of the user profiles associated with a consumer, then the advertisement is delivered to that consumer. 
   In system  1200 , client software is installed on PCs sold to users who participate in the network of consumers  1250 . In other embodiments, the client software is installed onto computing devices of the user (e.g., PC) when the user decides to join the network of consumers  1250 . That is, the client software is installed after the purchase of the computing device. The software operates in the background of the PC and tracks a wide variety of user information and behaviors, as previously described in relation to  710  of  FIG. 7 . This logged information is put into a database that is stored locally on the PC. In other embodiments, the logged information is stored in a database that is remotely located from the user and not on the PC. 
   It should be noted that in accordance with embodiments of the present invention, even though the software is operating in the background, the software is operating with the authorization of the user for purposes of targeted data delivery. That is, unlike conventional spyware that is operating under fraudulently obtained or absent any authorization, embodiments of the present invention operate with the full knowledge and consent of the user, and often for the pecuniary benefit of the user. 
   For example, PC  1251  is associated with a particular consumer. Client software  1253  is installed on the PC  1251  to track and log user information associated with the consumer. The user information is provided in a user profile  1255 , which is stored locally on the PC  1251 . Because the user information is stored locally, the consumer has maximum control over the associated data. That is, the consumer has control over whether information is released from the PC  1251 , and when that information is released. In addition, an ask price  1257  associated with the consumer is stored locally on the PC  1251 . 
   Also, the client software  1253  is used to determine if the user profile  1255  satisfies the query  1231 . That is, the computation done to determine if the user profile  1255  satisfies the query  1231  occurs locally on the PC  1251 . Further, the computation is performed transparently to the consumer. 
   Only when the criteria in the query  1231  is satisfied by the user profile  1255 , and when the bid price  1235  exceeds the ask price  1257  will the offer to view the advertisement be presented to the consumer. If the consumer chooses to the view the advertisement, then the network owner  1220  delivers the advertisement to the consumer for viewing (e.g., the link  1233  is invoked). In addition, in return for the consumer&#39;s attention, a payment is made to the consumer who is viewing the advertisement, as depicted at  1260 . The payment is a function of the user ask price, A, and the bid price, B. The incentive delivered to the user is limited by A&lt;=V(A,B)&lt;=B. In addition, in another embodiment, credit to the consumer is managed by the network owner  1220 . 
   In the present embodiment, the system  1200  operates without anyone other than the consumer knowing that the consumer&#39;s user profile  1255  satisfies the criteria set forth in the query  1231 , until the offer is accepted, at which point the network owner is made aware of information in the consumer&#39;s user profile. Even though the targeting is extremely specific (e.g., exploiting a wide range of consumer data) which allows the advertiser to reach exactly who they want to reach, this is accomplished without violating the consumer&#39;s privacy because of the privileged position of the PC as the final stage in the delivery of all content. 
   As a result, the system of  FIG. 12  is capable of targeting advertisements to particular users who exhibit characteristics that the advertiser thinks are indicative of a potential buyer. Further, the advertiser is willing to pay, for example, three dollars for the attention of a consumer, if the selection criteria are satisfied. 
   Section 3: Method and System for Targeted Data Delivery from the Standpoint of the Data Distribution Layer 
   In Section 3, embodiments of the present invention are described from the standpoint of the data distribution layer. For example, the data distribution layer is located between lines A-A and B-B in  FIG. 5 . As such, description of the targeted delivery of data is described below from the standpoint of the data distribution layer. 
   Embodiments described in conjunction with  FIGS. 13A ,  13 B, and  14  pertain to methods for targeted data delivery and a system for implementing the same.  FIG. 13A  is a flow diagram illustrating operations performed in a method for targeted data delivery in which a query is generated, in accordance with one embodiment.  FIG. 13B  is a flow diagram illustrating operations performed in a method for targeted data delivery in which a query is accessed, in accordance with one embodiment.  FIG. 14  is diagram illustrating a system capable of implementing the methods of  FIG. 13A , in accordance with one embodiment. 
   Although embodiments of  FIGS. 13A ,  13 B, and  14  are described from the standpoint of the data distribution layer of  FIG. 5  for purposes of clarity and brevity, other embodiments of the same Figures are implemented from the standpoint of other layers in  FIG. 5 , as will be described below. 
   The operations of the methods described in  FIGS. 13A and 13B  are performed at the data distribution layer of  FIG. 5 , in one embodiment of the present invention. In addition, in  FIG. 14 , the components of system  1400  which perform the operations of the method of  FIG. 13A  are located at the data distribution layer, in one embodiment. However, other embodiments of the present invention are well suited to locating the components of system  1400  at the data source layer of  FIG. 5 , or some other layer between the data source and the data distribution layer. In one embodiment in accordance with the present invention, the components of system  1400  are located on the system of a broker, not shown, who represents the data source and generates a query. Still other embodiments locate the components of system  1400  in a combination of the layers described above. 
   Turning now to  FIG. 13A , a flow diagram  1300 A is described illustrating operations for targeted data delivery for the generation of a query, in accordance with one embodiment of the present invention. In the present embodiment, the flow diagram  1300 A is taken from the standpoint of the data distribution layer in  FIG. 5 . However, other embodiments of the present invention are well suited to performing the method of  FIG. 13A  at other layers, or a combination of layers in  FIG. 5 . That is, generation of the query occurs at the data distribution layer, the data source layer, or some layer in between, in accordance with embodiments of the present invention. The method of flow diagram  1300 A is used to ensure the privacy of user information used for targeting delivery of data. 
   At  1310 , the present embodiment accesses selection criteria. As described previously with respect to  FIG. 7 , the selection criteria define characteristics exhibited by a targeted user for the delivery of data. These characteristics are met by the user profile of a targeted user. That is, the selection criteria are used to determine if the user profile of a potentially targeted user satisfies the target profile described by the selection criteria. 
   The user profile comprises user information corresponding to the potentially targeted user. If the user profile satisfies the selection criteria, then the potentially targeted user is a targeted user to whom data may be delivered. A full discussion regarding user profiles was previously presented in conjunction with operation  720  of  FIG. 7 . 
   As shown in  FIG. 14 , a selection criteria accessor  1410  is used to access the selection criteria. In the present embodiment, the components of system  1400  including the selection criteria accessor  1410  are located at the data distribution layer, however, in other embodiments the selection criteria accessor  1410  is located at other layers of  FIG. 5 . 
   At  1320 , the present embodiment generates a query for determining whether the user profile of the potentially targeted user satisfies the selection criteria. That is, the query is used to determine if the potentially targeted user is targeted for the delivery of data by initiating operations that compare the user profile to the selection criteria, as previously described above in conjunction with the discussion of operation  740  of  FIG. 7 . As shown in  FIG. 14 , a query generator  1420  is used to generate the query. While the present embodiment describes the query generation as being located at the data distribution layer of  FIG. 5 , other embodiments are well suited to locating the query generator  1420  at other layers of  FIG. 5 . 
   Embodiments in accordance with the present invention determine whether the user profile satisfies the selection criteria without requiring a release of any user information in the user profile associated with the potentially targeted user. In one embodiment, user information is not required to be released beyond the data distributor. That is, in determining if the user profile satisfies the selection criteria, user information is not required to be released beyond the line A-A in  FIG. 5 . As such, embodiments in accordance with the present invention are capable of generating a query that is implemented within a system that targets users who satisfy selection criteria without requiring a release of user information. Thus, the present embodiment maintains the privacy of the user information comprising the user profiles. 
   In one embodiment, query generator  1420  generates the query in response to input through a web enabled interface. For example, input entered from a data source is delivered to query generator  1420 . The query generator  1420  is located at the data distribution layer of  FIG. 5 , in one embodiment. At that point, query generator  1420  generates the query for use in targeting data delivery. 
   In one embodiment, the query is vetted to see if it is objectionable. For example, before the query is broadcasted in  1330 , the data distributor who forwards the query examines the contents of the query to determine if the query is searching for objectionable (e.g., very private information such as social security numbers), characteristics of users. If the data distributor finds the query is objectionable, then the query is rejected before it is broadcasted. In one embodiment, any data associated with the query is vetted to see if it is objectionable. 
   For purposes of organization, specific examples and detailed descriptions of the query generation process will be described in greater detail in Section 4 in which targeted data delivery is described from the standpoint of the data source layer. 
   At  1330 , the present embodiment optionally forwards the query to a plurality of users, including the potentially targeted user. That is, the query is broadly distributed to determine which of the plurality of users has a user profile that satisfies the selection criteria and is a targeted user for the delivery of data. In some embodiments, the query may be accompanied by data, a link to data, or a bid price. 
   As shown in  FIG. 14 , in one embodiment in accordance with the present invention, query forwarding module  1430  forwards the query to the plurality of users. In the present embodiment, query forwarding module  1430  is located at the data distribution layer. In other embodiments, query forwarding module  1430  is located at the broadcast layer of  FIG. 5 . 
   In embodiments of the present invention, the query forwarding module  1430  broadcasts the query to the plurality of users. The query forwarding module  1430  is a server of the data distributor  520  at the data distribution layer of  FIG. 5 , in one embodiment, or one or more servers of the data distributor  520  in other embodiments. 
   In one embodiment, the query forwarding module  1430  broadcasts the query through the Internet. That is, the query is broadcast to the plurality of users via the Internet, such as through a direct server to an electronic device (e.g., PC). However, other embodiments are well suited to other forms of broadcasting. For example, the query forwarding module  1430  is capable of broadcasting the query through an indirect connection, such as a peer-to-peer scheme. 
   Also, query forwarding module  1430  is capable of broadcasting through a datacasting method. The query is sent via a datacast network, such as an existing television (TV) network, a high definition television (HDTV) network, a HD radio network, satellite radio network, a radio network, or any suitable broadcasting medium. That is, in one embodiment in accordance with the present invention, the query is delivered to a broadcast antenna and then broadcasted to any user within the coverage area. The number of potential users is quite large, as datacasting will send the query to mobile as well as stationary users. For example, various handheld devices (e.g., PDAs, cell phones, etc.) as well as watches are configured to receive datacast messages. In this way, the query is anonymously transmitted to a plurality of users at one time without any targeting, instead of transmitting the query separately to each individual user. In another embodiment, the broadcasting of the query is implemented through client polling 
   In one embodiment, query forwarding module  1430  is capable of selecting users to whom the query is broadcasted. For example, query forwarding module  1430  sends the query to all of its supported users, or to randomly selected users, or demographically selected users. This reduces the cost of broadcasting. For example, query forwarding module  1430  filters out the users by zip code. As such, the query is broadcast only to one or more predetermined zip codes, although query forwarding module  1430  is capable of supporting many more zip codes. 
   In one embodiment, query forwarding module  1430  broadcasts the query in stages. For example, the query is broadcast in stages to help determine the interest in the data. This aids in determining the interest in the associated data, how quickly a budget could be exceeded or how much a budget would have to be adjusted. For example, an incentive budget is placed for delivery of data associated with a query. The query is first sent to one-thousand potentially targeted users, for example, to determine interest in the data. The sample size is small enough that the budget will not be exceeded. If the hit rate of targeted users is high, then there is high interest in the data and the budget may be quickly exceeded. As such, the budget cap may need to be increased before another broadcast is made. On the other hand, if the hit rate is low, then there is lesser interest in the data. As such, the query is directly sent to another set of potentially targeted users. 
   In another embodiment, query forwarding module  1430  charges a fee for broadcasting the query. For example, a fee of one cent per broadcasted message might be charged. Other rate schemes are implemented depending on the number of queries that are broadcasted by a particular data source. 
   Turning now to  FIG. 13B , a flow diagram  1300 B illustrates operations performed in a method for targeting data delivery, in accordance with one embodiment of the present invention. In the present embodiment, the flow diagram  1300 B is taken from the standpoint of the data distribution layer in  FIG. 5 . That is, the operations in flow diagram  1300 B are performed between lines A-A and C-C. 
   At  1340 , the present embodiment accesses a query for determining whether a user profile of a potentially targeted user satisfies the selection criteria. As described previously in  1310 , the query includes the selection criteria and is used to select users who satisfy the selection criteria for the delivery of data. In the present embodiment, the query is forwarded to a plurality of users including the potentially targeted user, at  1330 , as previously described in  FIG. 13A . 
   In the present embodiment, the query is generated at a location other than the data distributor. That is, the query is generated at a layer other than the data distribution layer of  FIG. 5 . More specifically, the query is generated at the data source layer or some other layer between the data source and the data distribution layer. For example, the query is generated by a data source through a web enabled interface run by the data distributor, as will be described more fully below in Section 4. 
     FIG. 15  in combination with  FIG. 16  illustrate an exemplary system for the targeted delivery of data in which an incentive is distributed.  FIG. 15  illustrates a method for targeted delivery of data, and  FIG. 16  is a system for implementing the method of  FIG. 15  in accordance with embodiments of the present invention. 
   Turning now to  FIG. 15 , a flow diagram  1500  illustrates operations performed in a method for targeted delivery of data in which an incentive is distributed, in accordance with one embodiment. Flow diagram  1500  describes additional operations performed subsequent to the performance of the operations described in the flow diagrams of  FIGS. 13A and 13B . 
   At  1510 , the present embodiment presents an offer to a targeted user for the delivery of data. This offer is presented to determine if the targeted user wishes the data delivered. The presentation of the offer by the offer presentor  1624  is shown in operation  1  of the data flow diagram of  FIG. 16 . 
   In  FIG. 16 , a data flow diagram illustrates the flow of information in the method of  FIG. 15 , in accordance with one embodiment. As shown in FIG.  16 , an offer  1610  containing some combination of the query, data, and ask price is delivered to a receiver in the data distributor  1620 . More specifically, the offer  1610  contains the query, in one embodiment. In another embodiment, the offer  1610  contains the query and some form of accessing the data, such as the data itself, or a link to the data. In still another embodiment, the offer  1610  contains the query, some form of accessing the data, and the ask price. As such, in embodiments in accordance with the present invention, the query is contained in the offer  1610 , or is generated by the data distributor  1620 . 
   In addition, an optional user profile database  1630  is configured to be accessible by the data distributor  1620 . That is, the targeting mechanism is centralized at the data distributor  1620 . As such, the data distributor  1620  is capable of determining which of the plurality of potentially targeted users it supports has a user profile that satisfies the query. In such an embodiment, the data distributor  1620  stores the user profiles of its network of users at the optional user profile database  1630 . In another embodiment, to ease the considerable burden of storing and searching millions of user profiles, the storing and computation performed on user profiles are off-loaded to the clients associated with the plurality of potentially targeted users, as previously discussed in Section 2. 
   In one embodiment, operation  1  may be performed to determine whether the user profile of the potentially targeted user satisfies the selection criteria as defined by the query that is included within an offer. That is, the offer is delivered to the user by the offer presentor  1624  in operation  1 . In accordance with embodiments of the present invention, the offer includes the query, and the location of the data to be offered for delivery, or alternatively the data itself. Additionally, an optional offer of compensation (e.g., incentive) for taking delivery is included within the offer. Also, additional information is included within the offer, such as expiration period, expiration date, etc. 
   More specifically, in accordance with one embodiment of the present invention, the data distributor  1620  is capable of determining which of the plurality of potentially targeted users it supports has a user profile that satisfies the query. As such, a determining module  1622  located within the data distributor  1620  uses the query within the offer to determine if the user profile satisfies the selection criteria. That is, in the present embodiment, a user profile accessor  1621  located within the data distributor  1620  accesses the user profile and a comparator  1623  located within the data distributor  1620  compares the user profile to the selection criteria. 
   In the embodiment where the data distributor  1620  performs the computation of determining whether the user profile satisfies the selection criteria, then data distributor  1620  is able to filter out targeted users. As such, delivery of data is targeted to those users (e.g., through user  1640 ) whose user profile satisfies the selection criteria in the query included within the offer. More specifically, in the present embodiment the offer is forwarded to users whose user profiles satisfy the selection criteria so that those users can determine whether to authorize delivery of the data. 
   On the other hand, in operation  1 , if the computation for determining whether a user profile satisfies the selection criteria is not performed at the data distributor  1620 , then the offer is delivered in a broadcast message to a plurality of users, including a user  1640  that is a potentially targeted user, in one embodiment. In such an embodiment, a determining module (e.g., located within the targeting mechanisms  561 ,  571 , or  581  of  FIG. 5 ) that is associated with a particular user uses the offer to determine if the user profile satisfies the selection criteria. That is, in the present embodiment a user profile accessor (e.g., located within the targeting mechanisms  561 ,  571 , or  581  of  FIG. 5 ) accesses the user profile and a comparator (e.g., located within the targeting mechanisms  561 ,  571 , or  581  of  FIG. 5 ) compares the user profile to the selection criteria. If the comparator determines that the user profile satisfies the query, then the offer is then presented to the user for acceptance of delivery of the data. 
   In accordance with one embodiment of the present invention, an optional bid price associated with the data is also delivered. That is, a bid price is presented to the user as part of the offer for the delivery of data, in one embodiment. As such, the bid price is used to determine if the user is interested in the delivery of data. That is, the bid price is accessed and compared against a user-selected relevancy criterion. The incentive associated with the data is a function of the bid price. Data is delivered only when the bid price satisfies the user-selected relevancy criterion. Previously, a full discussion on the delivery of the bid price was presented in relation to  FIG. 10 . The bid price is shown in  FIGS. 3 ,  4 ,  5 , and  12 . 
   For example, if data distributor  1620  determines if the bid price satisfies the user-selected relevancy criterion, then an incentive accessor  1625  in the data distributor  1620  accesses the incentive (e.g., bid price) associated with the data. Comparator  1623  in the data distributor  1620  compares the incentive against the user-selected relevancy criterion (e.g., ask price). An offer presentor  1624  in the data distributor  1620  offers to deliver the data when the incentive satisfies the user-selected relevancy criterion. 
   Returning to  FIG. 15 , at  1520 , the present embodiment delivers data upon acceptance of the offer. Acceptance of the offer is conditioned upon a determination that the user profile satisfies the selection criteria. For example, delivery module  1626  in the data distributor  1620  delivers the data. Previous descriptions of the presentation and acceptance of the offer were presented in  FIG. 9 . As shown in  FIG. 16 , acceptance of the offer is illustrated in operation  2 , where the user  1640  delivers acceptance of the offer. In such an embodiment, the user authorizes the delivery of data. 
   At  1520  of  FIG. 15 , the present embodiment delivers data to the user  1640  upon acceptance of the offer. As shown in  FIG. 16 , operation  3  illustrates the delivery of data from data distributor  1620  to the user  1640  whose user profile satisfies the selection criteria. Delivery module  1626  contained within the data distributor  1620  delivers the data. For example, in one embodiment the delivery is implemented when the user invokes a link to the data. In such an embodiment, the data is stored at the data distributor  1620  and is delivered to the user  1640  upon invoking the link. 
   In addition, at  1530 , optionally, the present embodiment receives a subset of user information upon delivery of the data. For example, a receiver at the data distributor  1620  receives a subset of the user information upon delivery of the data. Release of the subset of user information from an information receiver (e.g., located at a targeting mechanism  561 ,  571 , or  581  of  FIG. 5 ) is conditioned upon authorization of the user, in one embodiment. Release of the user information was presented in conjunction with the discussion of  FIG. 9 . For example, the released information is limited to only the user information used to satisfy the selection criteria. As shown in  FIG. 16 , operation  4  illustrates the receipt of the user information by the data distributor  1620 . 
   In still another embodiment, data distributor  1620  forwards the user information to the data source of the data. In still other embodiments, the data distributor  1620  only forwards an aggregate of the user information to the data source in the interest of protecting the privacy of the network of users. 
   At  1540 , the present embodiment distributes an incentive associated with the delivery of data. As shown in  FIG. 16 , operation  5  illustrates the delivery of the incentive from an incentive delivery module  1627  of the data distributor  1620  to the database of accounts  1650 . In  FIG. 16 , the data distributor  1620  controls accounts for both the user  1640  and the data distributor  1620 . As such, in one embodiment of the present invention the distribution of an incentive is made directly to an account associated with the user  1640 . In another embodiment, the distribution of an incentive is made directly to an account associated with the data distributor  1620 . In still another embodiment, the incentives are distributed to accounts associated with the user  1640  and the data distributor  1620 . The various types of incentives (e.g., cash, frequent flier miles, points for goods and services, etc.) have been previously discussed in conjunction with the discussion of  FIG. 10 . 
   In one embodiment, data distributor  1620  controls the distribution of incentives. That is, data distributor determines the allocation of the incentive for the delivery of data. For example, data distributor  1620  distributes funds according to a function V(A,B), where A&lt;=V(A,B)&lt;=B. The variable A is the ask price of the user and B is the bid price. In one embodiment, the delivery of data occurs when the bid price exceeds the ask price set by the user. As such, the incentive V is set between the ask price and the bid price. 
   In embodiments in accordance with the present invention, the exact value of V is determined according to a number of formulas that split the valuation surplus between the user  1640  and the data distributor  1620 . The choice of the formula is important because it creates different incentives. 
   In one embodiment, data distributor  1620  distributes the incentive to a combination of the user and the data distributor  1620 . For example, the data distributor allocates a flat fee for every data delivered to itself, and assigns the rest to the user  1640 , or allocates incentives according to a percentage of the amount paid for the delivery of data by the data source (e.g., the bid price), etc. 
   In addition, other rate schemes are implemented by data distributor  1620  in various other embodiments of the present invention. For example, newer users receive a higher portion of the incentives distributed. Older users receive a lower portion of the incentives distributed. In addition, data distributor  1620  receives no incentive, or the user  1640  receives no incentive. 
   Section 4: Method and System for Targeted Data Delivery from the Standpoint of the Data Source Layer 
   Embodiments of the present invention in Section 4 are described from the standpoint of the data source layer. For example, the data source layer is located above line A-A in  FIG. 5 . As such, description of the targeted delivery of data is described below from the standpoint of the data source layer. 
   Embodiments described in conjunction with  FIGS. 17 and 18  pertain to methods for targeted data delivery and a system for implementing the same.  FIG. 17  is a flow diagram illustrating operations performed in a method for targeted data delivery in which data and a query are generated for targeted data delivery, in accordance with one embodiment.  FIG. 18  is diagram illustrating a system capable of implementing the method of  FIG. 17 , in accordance with one embodiment. 
   Although embodiments of  FIGS. 17 and 18  are described from the standpoint of the data source layer of  FIG. 5  for purposes of clarity and brevity, other embodiments of the same figures are implemented from the standpoint of other layers in  FIG. 5 , as will be described below. 
   As such, the operations of  FIG. 17  are performed at the data source layer of  FIG. 5  in accordance with one embodiment of the present invention. In addition, in  FIG. 18 , the components of system  1800  which perform the operations of  FIG. 17  are located at the data source layer, in one embodiment. However, other embodiments of the present invention are well suited to locating some or all of the components of system  1800  at the data distribution layer of  FIG. 5 , or some other layer in between the data source and the data distribution layer, for example at a server of a broker who represents the data source and generates the data and the query. 
   Embodiments of the present invention are discussed within the context of a data source that provides data. For purposes of brevity and clarity, the targeted delivery of advertisements, as data, that are generated from advertisers, as a data source, is used for illustrative purposes throughout the application. However, although advertisements that are generated from advertisers are used for purposes of brevity and clarity in certain examples, the targeted delivery of data generated from data sources is not intended to be limited to the targeted delivery of advertisements from advertisers. 
   Turning now to  FIG. 17 , a flow diagram  1700  is described illustrating operations for the generation of data and a query for the targeted delivery of the data. In accordance with one embodiment, the flow diagram  1700  is taken from the standpoint of the data source layer in  FIG. 5 . However, other embodiments of the present invention are well suited to performing the method of  FIG. 17  at other layers, or a combination of layers in  FIG. 5 . That is, generation of one or both of data and query occurs at the data source layer, the data distribution layer, or some layer in between, in accordance with embodiments of the present invention. The method of flow diagram  1700  is used to ensure the privacy of user information used for targeting the delivery of data. 
   At  1710 , the present embodiment generates data. The data that is generated is any suitable form of data that is targeted to particular users. It is assumed that the targeted users have a perceived interest in the data. 
   As shown in  FIG. 18 , data generator  1810  generates the data. In the present embodiment, the components of system  1800  are located at the data source layer, however, in other embodiments the data generator  1810  is located at other layers of  FIG. 5 . 
   In one embodiment, for illustrative purposes only, the data that is generated by data generator  1810  is an advertisement. The advertisement is generated specifically to appeal to a particular targeted group of users. For example, the advertisement promotes a series of reading books that are tailored to the education of science to elementary school-aged girls. More specifically, the books are not textbooks. The targeted group of users is the elementary school-aged girls, or the parents of those elementary school-aged girls. 
   As mentioned above, embodiments in accordance with the present invention are not limited to use with data comprising advertisements. Listed below are examples of data other than advertisements, which are well suited to being targeted to users in accordance with embodiments of the present invention. For illustrative purposes only, the data that is generated by data generator  1810  is an announcement, such as public service announcement, business announcement, internal company announcement, professional group announcement, etc. The announcement is targeted to a particular targeted group of users. For example, in the case of the public service announcement, the data generated by data generator  1810  is a warning for a flash flood and the data is targeted to a targeted group of news media. The warning is targeted to the group of news media for further dissemination to the general public. In the case of the business announcement, the data generated by data generator  1810  is breaking news affecting a price of a stock that is targeted to a group of users who subscribe to an investment publication. Those users use the data for their personal investment strategies. In the case of the internal company announcement, the data is general news particular to employees of a company. As such, this news is targeted only to those employees. In the case of the professional group announcement, the data generated by data generator  1810  is breaking news affecting the activities of that professional group. For example, the targeted group of users is medical doctors. As such, the data is breaking news regarding the efficacy of a particular drug that is widely disseminated for public use. In addition, other data are generated by data generator  1810  for the targeted delivery of that data. 
   At  1720 , the present embodiment generates selection criteria that define characteristics exhibited by a targeted user. That is, the selection criteria define characteristics met by a user profile of a potentially targeted user. As such, the selection criteria as generated by a selection criteria generator  1820  are used to determine if a potentially targeted user is a user to whom the data should be targeted for delivery. A full discussion on the selection criteria and its comparison to a user profile is provided in conjunction with a discussion of the operations of  FIG. 7  of Section 2. For instance, the selection criteria might contain characteristics (e.g., activity on a user&#39;s electronic device, search behavior, income, zip code, etc.) that help target or identify a user. In one embodiment, the selection criteria as generated by the selection criteria generator  1820  define a location of an electronic device associated with the potentially targeted user. In another embodiment, the selection criteria generated by the selection criteria generator  1820  include a history of web sites or URLs visited. In another embodiment, the selection criteria generated by the selection criteria generator include a history of web sites or URLs that are not visited. In still another embodiment, the selection criteria generated by the selection criteria generator  1820  include positive (e.g., did search URL X) as well as negative characteristics (e.g., did not search URL X). 
   As shown in  FIG. 18 , selection criteria generator  1820  generates the selection criteria. In the present embodiment, the components of system  1800  are located at the data source layer, however, in other embodiments the selection criteria generator  1820  is located at other layers of  FIG. 5 . 
   In addition, the user profile that is compared against the selection criteria includes user information collected from at least one electronic device associated with the potentially targeted user. A full discussion on the user information of the user profile is provided in conjunction with a discussion of the operations of  FIG. 7  of Section 2. For example, user information might contain the user&#39;s search behavior, name, zip code, etc. 
   Further, the selection criteria that are generated by selection criteria generator  1820  are used to determine whether the user profile satisfies the selection criteria with requiring a release of any user information in the user profile, in accordance with one embodiment of the present invention. In another embodiment, user information is not required to be released to a data source. That is, in determining whether the user profile satisfies the selection criteria, user information is not required to be released beyond the line A-A in  FIG. 5 . More specifically, in determining whether the user profile satisfies the selection criteria, user information is not required to be released to the data source (e.g.,  511 ,  513 , or  515 ) of  FIG. 5 . As such, user information associated with the potentially targeted user that is used to determine whether the user profile satisfies the selection criteria is not required to be released. In other words, the determination that the potentially targeted user is selected as one to whom the data is targeted is made without releasing any of the user information. 
   In one embodiment, the data includes open faced questions. That is, a question is posed to the targeted user. Based on the answer to the question, a follow on offer for the delivery of additional data is sent to the targeted user. For example, an open ended question to a targeted user might ask if the user owns a green car. If the user answers positively, another additional offer is posed to the user wherein the additional offer asks if the user would like to see additional advertisements. 
   A close relationship exists between the data as generated by data generator  1810  and selection criteria as generated by selection criteria generator  1820 . In particular, the selection criteria are generated very narrowly and with great granularity to target data to a specific group of users. As such, various relationships exist in generating both the data and the selection criteria. 
   In one embodiment the data is generated by data generator  1810  before the selection criteria are generated by the selection criteria generator  1820 . That is, the data is generated by the data generator  1810  with a general user in mind. Thereafter, selection criteria are created which pertain to a particular group of users that are targeted for the delivery of data generated by the data generator  1810 . As such, in one case the selection criteria are more general, and targeted to a wide group of users. In another case, the selection criteria are more specific, and are targeted narrowly to appeal to a specific group of users. In both cases, the advertisement is identical. 
   As an example, the data is an advertisement promoting laundry detergent. In one case, the selection criteria are used to describe characteristics of a homemaker. Targeting of the homemaker through the method of flow diagram  1700  is a supplement to more traditional forms of advertising targeted to the homemaker, such as television advertisements during soap operas. In another case, the selection criteria describe characteristics of a college student. In such an embodiment, the targeting of the college student through the method of flow diagram  1700  is more effective than traditional forms of advertising, since college students may not be exposed to television or print advertising. 
   In another embodiment, the selection criteria are generated by selection criteria generator  1820  before the data is generated by data generator  1810 . That is, the data is narrowly generated with a particular user group in mind. In the present embodiment, a specific group of users and their characteristics are first determined. Thereafter, the data is generated to be targeted to that particular group of users. In this scenario, data is narrowly tailored to appeal to the specific group of users that are targeted. 
   In the above scenario two different items of data are generated by data generator  1810  even though they both promote the same product, for example a high performance outdoor jacket. In one case, the selection criteria generated by the selection criteria generator  1820  narrowly describes a group of users who rock climb on a regular basis. Thereafter the data promoting the outdoor jacket is created to appeal to the rock climbers as a group of users and includes images of rock climbers wearing the jacket. This same selection criteria are used for other data (e.g., rock climbing equipment) that is targeted to that group of rock climbers. 
   In another case, the selection criteria generated by the selection criteria generator  1820  more generally describes a group of users who like to wear quality outdoor gear whether walking around town, hiking, etc. The data promoting the outdoor jacket is generated by the data generator to appeal to this more general group of users and includes images of hikers wearing the jacket. In both cases, the advertisement is different. 
   In still another embodiment, the data generated by data generator  1810  and the selection criteria generated by the selection criteria generator  1820  are created jointly. That is, the data and the selection criteria are created simultaneously. 
   Also, in another embodiment, the data is configured by data generator  1810  such that a design module is capable of customizing the data to a potentially targeted user based on user information in an associated user profile. That is, once the user profile of the potentially targeted user is determined to satisfy the section criteria, before delivery, the data is customized to the potentially targeted user using a customizer, not shown. For example, using the example of a user in the market for an inkjet printer, if the query included search terms that included a search URL of company A, and a non-search of company C, the data is customized to include the following terms: “Reasons that company C&#39;s inkjet printers are superior to company A&#39;s inkjet printers.” 
   At  1730 , the present embodiment optionally generates a query for determining whether the user profile satisfies the selection criteria. A full discussion describing the operations used for determining whether a user profile satisfies the selection criteria is provided in conjunction with a discussion of the operations of  FIG. 7  of Section 2. As shown in  FIG. 18 , a query generator  1830  generates the query. 
   Further, the selection criteria are included within the query that is used for the targeted delivery of data, as previously described in conjunction with  FIG. 13  of Section 3 in accordance with one embodiment of the present invention. The query is broadcast to a plurality of users by a broadcaster (e.g., one located at the broadcast layer of  FIG. 5 ). More specifically, the query is broadcast to a controller associated with a potentially targeted user. The controller is able to access the user profile associated with the potentially targeted user and compare the user profile against the target profile defined by the selection criteria to determine if the user profile satisfies the selection criteria. 
   In one embodiment, the selection criteria are modified by selection criteria generator  1820  before the query is broadcasted to a plurality of users. That is, the selection criteria are adjusted by selection criteria generator  1820  to pinpoint delivery of the data to a particular group of users. For instance, data is generated that is targeted to a group of users. If it is found that the query used to target delivery of the data to the group of users had a low hit rate, then the selection criteria used in the query are adjusted before making another broadcast. 
   In another embodiment, the offer is only broadcast once by a broadcaster (not shown). That is, to discourage fraud, the availability of the data is not repeated so that fraudulent users cannot try afterwards to generate a user profile that satisfies the selection criteria included within the offer. As a result, if the offer is only broadcast once, then there is no incentive to try to simulate the characteristics identified by the selection criteria in hopes of generating consideration when that offer is repeated in the future. 
   In still another embodiment, to prevent fraud, the query generated by query generator  1830  is partially encrypted so that it is difficult to determine exactly what characteristics are included within the selection criteria of a query. In such an embodiment, the risk of broadcasting a query multiple times and having users guess and simulate the characteristics comprising the selection criteria is lower. 
   At  1740 , the present embodiment generates an incentive by incentive generator  1840  as compensation or consideration for delivering the data to the potentially targeted user. In the present embodiment, the offer for the delivery of data includes the incentive. The incentive is compared against a user-selected relevancy criterion to determine if the user is interested in being presented with the offer. A full discussion of the user-selected relevancy criterion is provided in relation to  FIG. 9  of Section 2. 
   For instance, the incentive is an offer of a certain amount of money, called the bid price, submitted by a data source. The user-selected relevancy criterion is the minimum amount of money the user requires to view an advertisement, called an ask price. In such an embodiment, the data is delivered when the incentive bid price meets or exceeds the ask price. 
   In addition, an incentive limiter  1850  is shown in system  1800 . The incentive limiter  1850  sets a cap on the total amount of incentives that are distributed for a particular item of data. That is, the incentive limiter  1850  limits the incentives that are paid to a set of targeted users whose user profiles satisfy the selection criteria. As such, even if a user profile of a particular user satisfies the selection criteria, any incentive will not be paid to the user since the cap has been reached. In still another embodiment, a cap is placed on payments to individual users. This is also an important tool for limiting fraud. For instance, incentive payments are capped to all users associated with a specific US postal address. 
   In still another embodiment, the number of users whose user profiles satisfy the selection criteria of an offer and whose asking price for their attention is exceeded by the bid price associated with the offer is not known before the distribution of the offer. As such, the cost for distributing the offer is unknown. This is addressed using caps, random sampling, message primitives, and voting protocols. For example, in one embodiment, the costs for distributing incentives are capped by specifying to users that only the first n number of users whose user profiles satisfy the selection criteria and accept will receive an incentive. In another embodiment, through random sampling of the user population, the overall cost of an advertisement is estimated to (almost) arbitrary accuracy. In still another embodiment, anonymous message primitives are allowed in the system (e.g., through a broadcast layer that uses a peer-to-peer architecture), and the number of user profiles that satisfy the selection criteria is determined transparently to users, still without violating a user&#39;s privacy. In another embodiment, Internet voting protocols are used to determine the number of users whose user profiles satisfy the selection criteria. 
   In addition, the present embodiment is configured such that user information is released upon delivery of data. That is, once the present embodiment determines that a user profile of an associated user satisfies the selection criteria of a query, and the data has been delivered for viewing by the user, user information is released as compensation or consideration of receipt of the data by a receiver (e.g., a receiver at the data distributor  520  of  FIG. 5 ). 
   In another embodiment, information related to a set of targeted users is received by a receiver (e.g., a receiver at a data source of  FIG. 5 ). The information is an aggregate of information compiled from the user information received from individual users upon receipt of data. In such an embodiment, the privacy of each individual user is still ensured since information is not identifiable to a particular user. Additionally, in embodiments in accordance with the present invention, user information is only released with explicit authorization by the user. 
     FIGS. 19A-19F  are diagrams illustrating exemplary user interfaces used to generate an offer for the targeted delivery of data, in accordance with one embodiment. The user interfaces of  FIGS. 19A-19F  are implemented within the data source layer of  FIG. 5 , or by a third party between the data source layer and the data distributor layer of  FIG. 5 . In addition, the user interfaces of  FIGS. 19A-19F  are implemented in conjunction with the data distributor layer of  FIG. 5 . That is, a server associated with a data distributor of the data distributor layer of  FIG. 5  manages the user interfaces of  FIGS. 19A-19F  that are used to generate an offer for the targeted delivery of data. For purposes of illustration only, the offer that is generated in  FIGS. 19A-19F  is related to a spam killer application advertisement that is being targeted to a particular group of users. In one embodiment, the broadcasting of the offer is implemented through client polling. In addition, values and terms used as inputs are for illustration only and other embodiments of the present invention support various other values and terms. 
   Turning now to  FIG. 19A , a window  1900 A of a user interface for generating an offer is shown, in accordance with one embodiment. The window  1900 A is a content manager for a system that is capable of targeted delivery of data. The window  1900 A is relevant to a data source that is simultaneously presenting multiple offers for the delivery of different items of data. For example, as shown in  FIG. 19A , two active offers are shown. A first active offer presented by the data source is related to the heading of “Table PC.” A second active offer presented by the data source is a TEST offer in row  1909 . 
   Information in row  1907  is provided for the heading of “Tablet PC.” For example, the bid price is set to $1.11 in column  1901  and represents the amount of cash as an incentive the data source is willing to pay for the targeted delivery of data. In addition, a budget of $100.00 total is included in column  1903  and represents the maximum incentives the supplier is willing to pay for the distribution of the data. That is, no more than $100.00 is to be distributed as incentive for the delivery of data. In the next two columns, a creation date is included in column  1904  and an expiration date is included in column  1905 . Also, in column  1906 , the status of the offer with the heading of “Tablet PC” is included. As shown in  FIG. 19A , the offer for the data with the heading of “Tablet PC” is in an active state. In column  1908 , further options are available. For example, editing of the offer is possible, changing the expiration date is possible, or deleting the offer is possible. 
   A button  1902 , when invoked, presents exemplary interfaces used to generate another offer. That is, button  1902  enables entry to the interfaces of  FIGS. 19B-E  in order to generate a new offer. 
     FIG. 19B  is a window of an exemplary user interface  1900 B that is used to create an offer for the promotion of the spam killer application, in accordance with one embodiment of the present invention. In general,  FIGS. 19B-E  are used to generate a new offer. 
   At input block  1910 , the title of the advertisement is created. That is, the text “Spam Killer” referring to the application being promoted is included within block  1910 . As such, the offer is being generated to promote the “Spam Killer” application. 
   At input block  1912 , additional information is included that is related to the “Spam Killer” application. This additional information is presented to a potentially targeted user for helping that user decide if the “Spam Killer” application is worthwhile to view. 
   At input block  1915 , a method of delivering data is presented. For example in window  1900 B, a URL is inputted that contains the data, in this case the advertisement that is promoting the “Spam Killer” application. As such, the offer being generated includes the link to the URL contained in block  1915 . If the user profile satisfies the selection criteria, as later defined, then the URL at block  1915  is invoked to deliver the data contained within the URL. 
   Once the blocks in window  1900 B have been completed, selection of the continue option brings up window  1900 C. 
     FIG. 19C  is a window of an exemplary user interface  1900 C that is used to set selection criteria, in accordance with one embodiment. More specifically, the user interface  1900 C is used to generate selection criteria used to identify targeted users. As such, those user profiles that satisfy the selection criteria are the targeted users to whom the delivery of data is targeted. 
   Block  1937  provides a summary of important information associated with the offer being generated by the user interfaces of  FIGS. 19B-E . For example, the title of the application is included, along with a brief description of the application. The location of the URL where the data is located (e.g., the advertisement) is also included. 
   The first matching criteria include the exact terms used in recent searches. Block  1930 , a drop-down field, describes the type of the first matching criteria as “Exact Search String.” Input block  1931  includes the terms or values of the “Exact Search String.” In block  1931 , the term “spam” is used to define characteristics of targeted users. That is, the selection criteria are isolating users who have searched the Internet using the “spam” search term. 
   The second matching criteria include a visited URL. Block  1933  describes the type of the second matching criteria as “URL (visited).” In block  1934 , the term “http://howtokillallspam” is used to define the visited URL for the second matching criteria. 
   Block  1935  is presented to illustrate that multiple criteria are used to define characteristics of the targeted users. For instance, another type of “URL (visited)” is used as a third matching criteria. In such an embodiment, the value is blank until the URL is entered. 
   Once the input blocks in window  1900 C have been completed, selection of the continue option brings up window  1900 D. 
     FIG. 19D  is a window of an exemplary user interface  1900 D that is used to confirm the matching criteria that is set in window  1900 C, in accordance with one embodiment of the present invention. More specifically, the user interface  1900 D is used to confirm the selection criteria used to identify targeted users. 
   Block  1947  provides a summary of important information associated with the offer being generated by the user interfaces of  FIGS. 19B-E . For example, the title of the application is included, along with a brief description of the application. The location of the URL where the data (e.g., the advertisement) is accessed is also included, as well as the data source that generates the data associated with the offer. In this case, the “Test Advertiser” is the provider. 
   In window  1900 D, two selection criteria have been generated. At row  1941 , the selection criteria for the “spam” search term is presented in summary. At row  1942 , the selection criteria for the URL “http://howtokillallspam” are presented in summary. In column  1945 , an action is available to delete either of these selection criteria. In addition, selection of option  1949  allows the user to return to window  1900 C and add another selection criteria, for example. 
   Once the input blocks in window  1900 D have been completed, selection of the continue option brings up window  1900 E. 
     FIG. 19E  is a window of an exemplary user interface  1900 E that is used to set the budget for the offer created in  FIGS. 19B-E , in accordance with one embodiment of the present invention. 
   Block  1957  provides a summary of important information associated with the offer being generated by the user interfaces of  FIGS. 19B-E . The summary in block  1957  is identical to the summary in block  1947 . 
   Input block  1951  sets the total budget for distributing the offer. In block  1951 , five thousand dollars is set as the total budget. As such, up to five thousand dollars will be distributed as incentives for the delivery of the advertisement promoting the “Spam Killer” application, for example in one embodiment. 
   Input block  1953  sets the individual cap for incentives that are paid for each delivery of data to a targeted user whose user profile satisfies the selection criteria of the offer. In this case, a sum of $2.45 is provided as incentive to be distributed. The incentive is distributed to the user who receives the advertisement, the data distributor broadcasting the offer, or a combination of the two, in embodiments of the present invention. 
   Input block  1955  sets the block of time that the offer is valid. In this case, a total of  30  days is provided as a window for an active offer. 
   Once the blocks in window  1900 E have been completed, selection of the continue option brings up window  1900 F. In  FIG. 19F , a window  1900 F of a user interface for generating an offer is shown, in accordance with one embodiment of the present invention. The window  1900 F is a content manager to a system that is capable of targeted delivery of data and is similar to the window  1900 A of  FIG. 19A , except for the additional row of information  1960  indicating the offer generated through the use of windows  19 B- 19 E. 
   The window  1900 F is relevant to a data source that is simultaneously presenting multiple offers for the delivery of different items of data. For example, as shown in  FIG. 19F , three active offers are shown. A first active offer presented by the data source is related to the heading of “Table PC.” A second active offer presented by the data source is a TEST query in row  1909 . In addition, a third active offer presented by the data source is related to the newly generated offer with the heading of “Spam Killer.” 
   In accordance with another embodiment of the present invention, a conversion tracking capability is provided for a particular offer. For purposes of the present Application, the term “conversion” is intended to refer to a specific action taken by a user in response to an offer. Typically, the action has some benefit to the originator of the offer. As such, conversion includes hard actions, such as purchasing a product, or soft actions, such as clicking through a web site, or signing up for an electronic newsletter delivered over the Internet, etc. In addition, in another embodiment, an incentive (e.g., cash, frequent flier miles, coupons, etc) can be paid to the user and/or the network owner delivering the offer when the user performs a conversion. For example, a user is said to convert when they purchase the good being offered by that advertisement. That is, for a particular offer, the present embodiment is able to track a future event, such as the conversion of a product being promoted in the offer. For example, an offer associated with promotional data advertising a tennis racket is tracked. That is, the present embodiment is able to track the total number of tennis rackets being promoted that were sold to the targeted users whose user profiles satisfy the selection criteria of the offer. 
   In still another embodiment of the present invention, the cookies related to delivery of the data are automatically blocked. Cookies are used to identify users who have visited a URL, for example. In this manner, further protection of the identity of the targeted user is preserved. 
   In accordance with another embodiment of the present invention, multiple items of data are generated for a single query. For example, one query is associated with two or more advertisements for the same product. The present embodiment is able to track the version of the data that is presented to a particular user and ensure that only one item of data is presented to that user. For instance, one version of an advertisement of a single product is only presented to the targeted user. 
   In still another embodiment, an auction is provided for a particular type of data that is targeted to one targeted user. For example, one item of data (e.g., an advertisement) is promoting vehicle type V and is generated by dealer A. At the same time, a second item of data promoting the same vehicle type V is generated by dealer B. The first and second items are not identical. That is, the items of data are tailored to each individual dealer, A and B. One item of data is tailored to dealer A for the promotion of vehicle type V, and the other item of data is tailored to dealer B for the promotion of vehicle type V. In addition, the selection criteria are configured such that the first and second items of data are targeting a single user. 
   In such an embodiment, the present embodiment employs an auction between dealer A and B in order to determine which dealer can deliver their item of data. In this way, additional revenue is generated, and the user is not bombarded with two advertisements promoting the same product. 
   Section 5: Business Method for Generating Revenue by Enabling Targeted Data Delivery 
   Embodiments of the present invention are discussed within the context of electronic devices that are associated with a user. One electronic device, for example is the PC and is used for illustrative purposes. Although the PC is used for purposes of brevity and clarity in many of the following examples, other embodiments of the present invention are well suited to creating networks of users for the targeted delivery of data using electronic devices other than PCs. 
   In one embodiment, manufacturers of PCs can leverage their position to create a network of users that are controlled by the PC manufacturer as the network owner. The PC as a commodity is ubiquitous throughout the world. As such, the PC is used to efficiently connect data sources (e.g., advertisers) and users through the targeted delivery of data (e.g., advertisements). For example user information collected from PCs is used to efficiently target delivery of data. In one implementation, a data source targets data to users over a communication network based on the user information. As an added benefit, the targeted delivery of data is accomplished without requiring a release of any of the user information absent authorization and consent by the user. As a result, data sources utilize networks, which couple vast numbers of PCs, to electronically distribute their data to a large audience of PC users. 
   As a result, the use of the PC for the targeted delivery of data provides economic advantages to manufacturers of the PC. In this way, the use of the PC brings an economic benefit both to the user and manufacturers of the PC. This economic shift to the manufacturer of the PC, for example, would be in direct contrast to another highly commoditized technology, the television (TV) which provides virtually no marginal economic benefit to manufactures of the TV with the use of their TV product. For example, TVs are ubiquitous and inexpensive. As such, manufacturers of TVs marketed to the general public have low profit margins. However, even though the television occupies a crucial position in the chain of delivering content to users, heretofore the use of the TV provides little or no economic benefit to the TV manufacturers. That is, the conventional use of TVs and PCs directs profits to the suppliers of content and the suppliers of software that shape the delivery of content, and not the manufactures of the TVs or PCs. 
   On the other hand, embodiments of the present invention provide for a network architecture that allows PC manufacturers as data distributors to leverage their position as a supplier and center point of tens of millions of PCs in order to obtain an economic benefit from the use of PCs. This is possible by turning the base of computer users into a proprietary network of users to which data (e.g., advertising) is delivered. The network architecture of embodiments of the present invention is beneficial in that it discourages spam. In addition, the network architecture provides for highly relevant targeted advertising by exploiting the PC&#39;s position as the last link in the delivery of content. Also, the network architecture protects the privacy of all user data. 
     FIG. 20  is a flow diagram  2000  illustrating operations in a business method for revenue generation by enabling data delivery to selected targets, in accordance with one embodiment of the present invention. The flow diagram  2000  is capable of providing an economic benefit to network owners that control access to a network of users. 
   At  2010 , the present embodiment provides access to a plurality of potentially targeted users. Access to the plurality of potentially targeted users is achieved through a proprietary network, such as a virtual network. For example, in one embodiment, access is provided through the data distribution layer of  FIG. 5  as the network owner. 
   As such, the targeted delivery of data provides benefits for network owners (e.g., data distributors of  FIG. 5 ) who control the proprietary network. In such an embodiment, the network of potentially targeted users is owned by the network owner, and as such, access to those users is through the network owner. As such, data is targeted to those users through the network owner. Since the network owner is a control point, it realizes an economic benefit from providing the access to those users in its network. 
   At  2020 , the present embodiment accesses selection criteria used for selecting user profiles of the plurality of potentially targeted users. The selection criteria are used to identify users to whom delivery of data is desired. As such, the selection criteria include characteristics of users to whom the data is targeted. For example, the selection criteria define in one embodiment behavioral activity of the targeted users on associated electronic devices. In addition, in another embodiment, the selection criteria define a history of web sites visited, or not visited, or a combination of both. 
   In the present embodiment, the user profiles are based on user information collected from electronic devices associated with the plurality of potentially targeted users. For example, user information in one embodiment includes behavioral characteristics of the plurality of potentially targeted users. A full description of the collection of user information from associated electronic devices is provided in  FIG. 7  of Section 2. 
   At  2030 , the present embodiment determines which of the user profiles associated with the plurality of potentially targeted users satisfy the selection criteria. As such, the present embodiment is able to provide access to the user profiles of the plurality of potentially targeted users in order to determine which of the user profiles satisfy the selection criteria. 
   More specifically, the selection criteria are included within a query that is performed to determine which user profiles of a plurality of potentially targeted users satisfy the selection criteria. That is, the query is forwarded to the plurality of potentially targeted users. The query is performed against the user profiles of the plurality of potentially targeted users to identify which of the potentially targeted users is a targeted user. A full discussion of the selection criteria and their use in determining whether a user profile satisfies the selection criteria is provided in  FIG. 13A  of Section 3. 
   Moreover, the determination of which user profiles in the plurality of potentially targeted users satisfy the selection criteria is performed without requiring a release of user information in the user profiles. As such, the privacy of the plurality of potentially targeted users is maintained. That is, the network including the plurality of potentially targeted users targets the delivery of data by determining if a user profile satisfies the selection criteria without releasing any of the user information used to make that determination. 
   At  2040 , the present embodiment enables distribution of data to the users of the plurality of potentially targeted users whose user profiles satisfy the selection criteria. The data is generated by a data source with the knowledge that the data is delivered to targeted users who are defined by definable user characteristics and whose user profiles satisfy the selection criteria. As such, the data is tailored to appeal directly to those targeted users. 
   At  2050 , the present embodiment receives compensation or consideration for enabling the distribution of data. That is, the present embodiment is able to receive compensation or consideration for enabling the targeted delivery of data to a plurality of potentially targeted users whose user profiles satisfy the selection criteria. That is, by providing access to a plurality of potentially targeted users through a proprietary network, an economic platform is created that generates income for the targeted delivery of data to users whose user profiles satisfy the selection criteria. 
   In one embodiment, the source of the data provides the compensation to the network owner who controls access to the plurality of potentially targeted users. For example, the data source is an advertiser who is interested in the targeted delivery of advertisements, in one embodiment. 
   In another embodiment, the network owner who controls access to the plurality of potentially targeted users is a search engine. In such an embodiment, the search engine provides the network to perform the targeted delivery of data and, as such receives compensation for the targeted delivery of data to user profiles that satisfy the selection criteria. 
   In still another embodiment, the network owner who controls access to the plurality of potentially targeted users is an Internet service provider (ISP). In such an embodiment, the ISP provides the network to perform the targeted delivery of data and, as such receives compensation for the targeted delivery of data to user profiles that satisfy the selection criteria. 
   In still another embodiment, the user to whom the data is delivered receives compensation. For example, an incentive as compensation or consideration is paid to the user for the user&#39;s attention in receiving and viewing the data. In that case, the offer for the delivery of data includes the incentive that is generated as consideration for the delivery of the data to a targeted user. The incentive is used to determine if a targeted user deems it worthwhile to receive the data, and as consideration for the delivery of the data. A full discussion of the satisfaction of the user-selected relevancy criterion is presented in relation to  FIG. 9  of Section 2. 
   As a result, embodiments in accordance with the present invention enable the ubiquitous PC to be converted from a commodity with low profit margins to a revenue generating device. Other embodiments in accordance with the present invention are well suited to converting other low margin electronic devices to a high revenue generating appliance. For example, other low margin electronic devices include, but are not limited to, videocassette recorders (VCRs), personal digital assistants, cell phones, etc. 
   More specifically, embodiments in accordance with the present invention provide for after market revenue generation of these PCs and other low margin electronic devices. That is, instead of ending the economic relationship between a user who has purchased of one of these low margin electronic devices that are commodities, embodiments in accordance with the present invention establish an on-going economic relationship with the user. The relationship can extend for the lifetime of the user. 
   Embodiments in accordance with the present invention encourage participation of users by providing economic incentives and awards to the user. Specifically, by a user participating in a data delivery network, the present embodiment provides incentives to the user for viewing data that is relevant to the user&#39;s interests. Other embodiments reduce the amount of user&#39;s attention given to unwanted data. As such, embodiments of the present invention provide an economic and time benefit to the user. 
   In addition, the owner of the data delivery network (e.g., the data distributor) receives payment for delivering data to the user, in accordance with embodiments of the present invention. That is, by providing the network that allows access to the user and other users, embodiments of the present invention provide for revenue generation to the data distributor for the targeted delivery of data. The larger the network of users, the larger the revenue stream flowing to the data distributor. Also, the revenue flow per user can be timeless. That is, a user may participate within the data delivery network for the lifetime of the user if the incentives make it worthwhile to stay for the extended period. 
   Moreover, embodiments in accordance with the present invention are capable of providing the targeted delivery of data while maintaining the privacy of the users. As such, embodiments of the present invention are able to develop good will amongst the network of users that participate in the targeted delivery of data. This in turn develops a core of loyal customer users who are comfortable in receiving the targeted delivery of data, while at the same time receiving an economic incentive. 
   Hence, embodiments in accordance with the present invention provide for the extension and creation of a relationship with a user through a commoditized electronic device. As such, embodiments in accordance with the present invention are able to continue the relationship beyond the sale of the electronic device, and further are capable of generating revenue both for the user and the owner of the network (e.g., data distributor) that provides the targeted delivery of data. This revenue stream conceivably can last the lifetime of the user. 
   Section 6: Business Method for Generating Increased User Interest and Efficient Allocation of Advertising Resources by Enabling Targeted Data Delivery 
     FIGS. 21 and 22  utilize the methods and systems as presented in the  FIGS. 1-19  to provide increased user interest in the delivery of data and the efficient use of advertising money when advertising. Specifically, embodiments of the present invention of  FIGS. 21 and 22  are capable of being implemented within the network structures of  FIGS. 5 and 12  for the targeted delivery of data. That is, users are identified and targeted for the delivery of data without requiring a release of any user information. 
     FIG. 21  is a flow diagram  2100  illustrating operations in a business method for generating increased user interest in a particular item of data by enabling delivery of the data to selected targets, in accordance with one embodiment of the present invention. Embodiments of flow diagram  2100  are able to perform targeted delivery of data to generate increased interest in data while protecting the privacy of targeted users. Because embodiments of the present invention are able to deliver data to targeted users within a proprietary network, a revenue stream is generated related to the targeted delivery of data. 
   At  2110 , the present embodiment generates data targeted to users exhibiting definable user characteristics. That is, data is generated with the knowledge that the data will be delivered to targeted users who are defined by the definable user characteristics. As such, the data is tailored to appeal directly to those targeted users. Since the data will not be received by users other than the targeted users, concerns relating to the creation of the data relevant to those users other than the targeted users do not need to be addressed. Generation of data is fully described in relation to  FIG. 17  in Section 4. 
   At  2120 , the present embodiment generates selection criteria based on the definable user characteristics. The selection criteria are used to identify users to whom delivery of data is desired. The selection criteria include characteristics of users to whom the data is targeted. For example, the selection criteria define, in one embodiment, required behavioral activity of the potentially targeted users on associated electronic devices. In addition, in another embodiment, the selection criteria define a list of web sites that must have been visited, or not visited, or a combination of both. 
   More specifically, the selection criteria are included within a query that is generated. The query is used to target delivery of the data. The query is forwarded to a plurality of potentially targeted users. The selection criteria are used to determine potentially targeted users whose user profiles satisfy the selection criteria. That is, performance of the query determines which user profiles of a plurality of potentially targeted users satisfy the selection criteria. A full discussion of the query and its use in determining whether a user profile satisfies the selection criteria is provided in  FIG. 13A  of Section 3. Information is also provided in Section 7 below. 
   In one embodiment, the data is associated with an incentive that is generated as consideration for the delivery of the data to a targeted user. In one embodiment of the present invention, the incentive is included within the offer for the delivery of the data. The incentive determines if a targeted user deems it worthwhile to receive the data, and as consideration for the delivery of the data. 
   As such, the incentive is compared against a user-selected relevancy criterion of said targeted user. For example, the incentive includes an incentive bid price, such that the targeted user&#39;s relevancy criterion is satisfied by the incentive when the incentive bid price meets or exceeds the user ask price defined by the user-selected relevancy criterion. The incentive is used to determine which of those users whose user profiles satisfies the selection criteria are willing to consider the associated offer of data delivery. A full discussion of the satisfaction of the user-selected relevancy criterion is presented in relation to  FIG. 9  in Section 2. 
   In addition, in return for the delivery of the data, the network owner receives user information that was used to determine if an associated user profile satisfies the selection criteria. This information is used to further refine the selection criteria for targeted users in the targeted delivery of data, in one embodiment. 
   Moreover, the determination of which user profiles in the plurality of potentially targeted users satisfy the selection criteria is performed without requiring a release of user information in the user profiles. As such, the privacy of the plurality of potentially targeted users is maintained. That is, a network including the plurality of potentially targeted users targets the delivery of data by determining if a user profile satisfies the selection criteria without releasing any of the user information used to make that determination without authorization or consent. 
   At  2130 , the present embodiment generates increased user interest for the data by distributing the data only to targeted users. That is, the data is efficiently delivered only to potentially targeted users whose user profiles satisfy the selection criteria. In that way, if desired, the targeted delivery of data to potentially targeted users whose user profiles do not satisfy the selection criteria is avoided. As such, the data is distributed only to users who are perceived to be highly interested in the data, as opposed to distributing data to a wide audience through conventional means that includes users who are highly interested, mildly interested, and not interested in the data. As a result, because the delivery of the data is specifically targeted to users who have high interest in the data and the users know this, a higher degree of interest is generated in the data than would occur if the data were broadly distributed to a wide audience. 
   In one embodiment, for efficient distribution, the query is broadcasted to the plurality of potentially targeted users. More specifically, the query is broadcasted to a controller associated with a potentially targeted user. The controller is capable of comparing a user profile of an associated potentially targeted user against selection criteria to determine if the user profile satisfies the selection criteria. A full discussion of the broadcasting of the query is provided in relation to  FIGS. 13A and 13B  of Section 3. 
   In one embodiment, to more efficiently target the data, the selection criteria are adjusted based on changes to the definable user characteristics exhibited. That is, if the definable user characteristics of the desired users change throughout the life of an offer, the selection criteria are adjusted to reflect those changes. In that case, new and refined selection criteria are generated that are used to determine which of the potentially targeted users have user profiles that satisfy the selection criteria. 
     FIG. 22  is a flow diagram  2200  illustrating operations in a business method for efficiently allocating advertising resources by enabling delivery of the data (e.g., advertisements) to selected targets, in accordance with one embodiment of the present invention. 
   Embodiments of the present invention are described within the context of the targeted delivery of data, and more specifically to the targeted delivery of an advertisement. Because embodiments of the present invention are able to deliver the advertisement to only targeted users, the costs per conversion are minimized. Embodiments of  FIG. 22  of the present invention are discussed within the context of delivering advertisements, but other embodiments are well suited to the targeted delivery of data that are not advertisements. 
   At  2210 , the present embodiment generates data (e.g., advertisements) targeted to users exhibiting definable user characteristics. The operation executed in  2210  is similar to the operations of  2110  of  FIG. 21 . A full discussion devoted to the generation of data is provided with respect to  2110  of  FIG. 21 . 
   At  2220 , the present embodiment generates selection criteria based on the definable user characteristics, such that a query comprising the selection criteria is performed to determine which user profiles of a plurality of potentially targeted users satisfy the selection criteria without requiring a release of user information in the user profiles. The selection criteria include characteristics of users to whom the data is targeted and is included within the query. The operation executed in  2220  is similar to the operations of  2120  of  FIG. 21 . A full discussion devoted to the generation of selection criteria is provided with respect to  2120  of  FIG. 21 . 
   At  2230 , the present embodiment efficiently spends an advertising budget related to the distribution of the advertisement by delivering the advertisement to potentially targeted users whose user profiles satisfy the selection criteria. That is, advertising money related to the distribution of the advertisement, as data, is efficiently spent by using a system that is capable of highly satisfactory conversion rates. Efficient spending of the advertising budget is achieved, even though the cost per user for delivery of the data may be higher, because the advertisement is delivered only to interested users, which are more likely to perform an action that converts the advertisement. For example, the advertisement is delivered directly users whose user profiles satisfy the selection criteria. Moreover, advertising expenditures are focused on interested users by only delivering the advertisement to potentially targeted users whose user profiles satisfy said selection criteria. 
   As such, since the advertisement is only delivered to interested users, advertising expenditures need not be unnecessarily expended for the delivery of the advertisement to users who are not interested in the advertisement. That is, the present embodiment is capable, if desired, of avoiding delivery of the advertisement to potentially targeted users whose user profiles do not satisfy the selection criteria. 
   In addition, the present embodiment generates increased user interest for the data by distributing the data only to targeted users. That is, the data is delivered only to potentially targeted users whose user profiles satisfy the selection criteria. As such, the data is distributed only to users who are perceived to be highly interested in the data. As a result, because the delivery of the data is specifically targeted to users who have high interest in the data and they know this, a higher degree of interest is generated in the data than would occur if the data were broadly distributed to a wide audience. It is easier and cheaper to attract the attention of a user to an advertisement the user knows is likely to be interesting than to one the user expects to be uninteresting. 
   In another embodiment, increased interest in the data is generated by marking the data that is delivered to users whose user profiles satisfy the selection criteria. That is, the data is marked in such a way to indicate that the data has been delivered to a user because the associated user profile satisfies the selection criteria. In this manner, the user can immediately distinguish data of greater interest to the user because of the marking that indicates that the data has been delivered because an associated user profile has satisfied the selection criteria. 
   Further, the targeted delivery of data provides economic benefits for network owners (e.g., data distributors of  FIG. 5 ) who control a proprietary network, such as a virtual network in embodiments of the present invention. The network of users is owned by the network owner, and as such, access to those users is through the network owner. As such, data is targeted to those users through the network owner. Since the network owner is a control point, it realizes an economic benefit from providing the access to those users in its network. 
   As such, in one embodiment, a distributor of the data receives compensation or consideration for enabling the targeted distribution of data. That is, the present embodiment is able to provide compensation or consideration to the distributor upon delivery of the advertisement to potentially targeted users whose user profiles satisfy the selection criteria. That is, by providing access to a plurality of potentially targeted users through a proprietary network controlled by the distributor, embodiments in accordance with the present invention establish an economic platform that generates income to the distributor for the targeted delivery of data to users whose user profiles satisfy the selection criteria. 
   In still another embodiment, the data distributor who forwards the offer to targeted users whose user profile satisfy the selection criteria receives compensation or consideration. That is, the data distributor receives compensation for delivering the offer to a user even though it has not yet been determined if the user accepts the offer for the delivery of the data. 
   In another embodiment, the distributor who controls access to the plurality of potentially targeted users is a search engine. In such an embodiment, the search engine provides the network to perform the targeted delivery of data and, as such receives compensation for the targeted delivery of data to users whose user profiles satisfy the selection criteria. 
   In still another embodiment, the distributor who controls access to the plurality of potentially targeted users is an Internet service provider (ISP). In such an embodiment, the ISP provides the network to perform the targeted delivery of data and, as such receives compensation for the targeted delivery of data to users whose user profiles satisfy the selection criteria. 
   Also, in still another embodiment, the user to whom the data is delivered receives compensation or consideration. For example, an incentive as compensation or consideration is paid to the user for the user&#39;s attention in receiving and viewing the data. That is, an incentive payment associated with the query is paid to potentially targeted users whose user profiles satisfy the selection criteria and to whom the advertisement is delivered. 
   In addition, in another embodiment, the incentive determines if a targeted user deems it worthwhile to receive the data. That is, an offer is shown only to targeted users whose user-selected relevancy criterion meets or falls below the incentive. A full discussion of the satisfaction of the user-selected relevancy criterion is presented in relation to  FIG. 9  of Section 2. 
   Accordingly, various embodiments of the present invention disclose methods and systems for targeted data delivery. Embodiments of the present invention provide for protection of user privacy. In addition, other embodiments of the present invention provide the above accomplishments and provide for more efficient advertising by targeting advertising to interested recipients. Also, other embodiments of the present invention provide the above accomplishments and also discourage spam through a filtering mechanism through the preprocessing of messages to determine if they meet a relevancy criterion set by the user. In addition, embodiments of the present invention are able to provide an incoming revenue stream from commodity electronic devices after the initial sale of the electronic device (e.g., PCs). Various other embodiments achieve the above accomplishments and generate increased user interest for data by distributing data only to targeted users. Still other embodiments achieve the above accomplishments and also more efficiently advertise by delivering the advertisement to only users who are interested in it. 
   While the methods of embodiments illustrated in flow charts  100 ,  200 A,  200 B,  700 ,  900 ,  1300 A,  1300 B,  1500 ,  1700 ,  2000 ,  2100  and  2200  show specific sequences and quantity of operations, the present invention is suitable to alternative embodiments. For example, not all the operations provided for in the methods presented above are required for the present invention. Furthermore, additional operations can be added to the operations presented in the present embodiments. Likewise, the sequences of operations can be modified depending upon the application. 
   Section 7: Targeting Data Delivery while Protecting Information in a User Profile 
   Embodiments in accordance with the present invention described in Section 7 pertain to methods and systems that can be used for demonstrating whether or not information is represented in a database while protecting the contents of the database. Specifically, such methods and systems can be used to target data delivery based on the information in a user profile while protecting the privacy of the information in the user profile. 
   Presented in Sections 1-6 are methods and systems for targeted data delivery. User information is collected and a user profile is generated and stored. The user profile can then be used to deliver data, such as an advertisement, that is targeted to the user based on information in the user profile. The user may be compensated for taking delivery of the data. 
   In overview, in one embodiment, participating users commit to their respective user profiles by a particular deadline before an offer is issued or broadcast. See the discussion of block  210  of  FIG. 2A , for example. In order to receive the data associated with the offer, such as an advertisement, or any compensation associated with receiving the offer&#39;s data, a proof that the committed-to user profile satisfies that offer&#39;s query is provided by the device upon which the user profile is maintained. In addition, the proof is provided in a manner that is sufficient to demonstrate that the offer&#39;s query is satisfied, but does not reveal other information in the user profile. 
   As presented above, according to one embodiment, a “coordinator,” such as data distributor  520  of  FIG. 5 , network owner  1220  of  FIG. 12 , or data distributor  1620  of  FIG. 16 , broadcasts a message to participating users. Associated with the message is a time deadline for responding. Each participating user uses a commitment method to commit to their profile. The coordinator can then send an offer to participating users after the time deadline arrives. For each participant, the user&#39;s profile can then be evaluated to determine whether the profile satisfies the offer&#39;s query. Each user whose user profile satisfies the query and who accepts the offer can provide a proof that their user profile satisfies the query by constructing a proof that is sent to the coordinator. The coordinator verifies the proof. If the verification is successful, the data, such as an advertisement, is sent to the user, and the user may be compensated. 
   According to embodiments of the present invention, commitment methods are used to commit to some data privately and later prove what data was committed-to. Set commitment methods are a specialization of commitment methods that may be used to commit privately to a set of values compiled in a user profile, such as but not limited to a set of numbers, a set of names, or a set of web sites visited, and to later (optionally) prove information about the contents of the committed-to set or user profile. In particular, commitment methods can be used to prove that particular values are in the committed-to user profile and, conversely, that particular values are not in the committed-to user profile. 
   Although embodiments in accordance with the present invention are described in the context of the user profile, the present invention is not so limited. That is, the present invention can be extended to disguise and protect information included in virtually any set of data. 
   In general, according to embodiments of the present invention, a “committing party,” which will be referred to as the “committer,” commits to a set of at most “n” items a — 1, . . . , a_n. The commitment reveals no information about the items in the data set, except possibly for an upper bound on the number of items. In response to a query of the form “Is ‘b’ in the set?”, the committer can provide a response and prove the correctness of the response. Importantly, the proof does not reveal any information other than the answer to the query. 
   In overview, using an example in which the data set constitutes a user profile, this is accomplished as follows: 
   (1) the committer maps “identities” in a user&#39;s user profile to “pseudo-identities” using a function “f_k,” which is a collision-resistant verifiable pseudorandom function; 
   (2) the committer commits to a set of pseudo-identities using a “weak commitment scheme” that prevents changes to the set but does not necessarily hide the pseudo-identities of the committed items; 
   (3) in response to a query with regard to whether an item is in the user profile, the function f_k is used to map the identity of that item to a pseudo-identity that can be compared in some fashion to the list of committed-to pseudo-identities; and 
   (4) the committer provides verification information to prove that the pseudo-identity of the item named in the query was determined using the same function f_k used to determine the pseudo-identities in the committed-to user profile. 
   As used herein, an “identity” refers to a name, or some other type of identifier, associated with an item in the user profile, and a “pseudo-identity” refers to a changed or otherwise disguised identity that is derived by applying the function f_k to the identity in the user profile. Using the above approach, the committed-to set cannot be changed, and answers to queries about items in the committed-to set can be provided, without having to keep secret the information in the committed-to set—that is, the pseudo-identities are not kept secret but the identities of the items in the user profile remain concealed. 
   There are several approaches that can be used to commit to a user profile or a set of values contained in the user profile. In one embodiment, the committer commits to a set of values by publishing or otherwise disclosing the list of pseudo-identities. In another embodiment, the committer commits to a set of values by constructing a “Merkle Tree” over a domain of all possible values and publishing the root (a Merkle Tree may be defined as a hash-based directed acyclic graph that can be represented as a tree in which every leaf or node has no more than one parent and in which every node is reachable from a single node called the root node). In yet another embodiment, the commitment is performed by first hashing the original values using a collision-resistant hash function such as SHA-2, thereby obtaining values of a fixed length (e.g., 256 bits). Then, a “modified Merkle Tree” (see  FIG. 23 ) of depth “L” is constructed, where the leaves of the tree that correspond to hashes of actual values contain 1, the rest of the leaves contain 0, and any node all of whose descendants have a 0 (null) value is also set to have a 0 value (in essence, if all the descendants of a node have a 0 value, then those descendants are all collapsed into that node and are thus represented as a single node). Although this tree is potentially of size 2^L, the number of nodes with a non-zero value is bounded by “L” times the number of items in the set. The committer sends the root of the modified Merkle Tree as a commitment to the set of values. 
     FIG. 23  is an example of a modified Merkle Tree  2300  according to one embodiment of the present invention. The modified Merkle Tree  2300  in the example of  FIG. 23  includes root node  2301  and also nodes  2302 ,  2303 ,  2304  and  2305 . 
   Consider an example in which the committed-to data set includes a “yes” value (which may be presented as a single bit value “1”) for the items represented by the bit strings 00 and 01, and a “no” value (which may be represented as a single bit value “0”) for the bits strings 10 and 11. A conventional binary tree of depth two would include four leaf nodes representing the following two-bit strings: 00, 01, 10 and 11. In contrast, modified Merkle Tree  2300  collapses the leaf nodes representing  10  and  11  into the node  2303 . That is, because all of the descendents of node  2303  have “no” values, the descendents of node  2303  are not individually represented, but instead are all represented by the “no” value at node  2303 . The descendents of node  2302  (nodes  2304  and  2305 ) include at least one “yes” value, and hence the nodes  2304  and  2305  are individually represented in modified Merkle Tree  2300 . If one of the nodes  2304  or  2305  had a “no” value, the modified Merkle Tree  2300  would have the same structure, because all of the descendents of node  2302  are not “no.” 
   A modified Merkle Tree can be similarly constructed for binary trees of any depth “L.” In general, if all of the descendents of a “parent” node have a “no” value, where that parent node is at any level in the tree and also has a “no” value, then those descendents are all represented by a “no” value at that parent node. 
   Yet another form of a modified Merkle tree to use in a weak commitment scheme can use the following method to hide the location of nodes which have a 0 value: a new value, which is a maximal root of a sub-tree containing only 0 values (namely, a node for which all of its descendents have 0 values, while its ancestor in the tree has a non-zero value), is defined for each node. The new value of the node is computed as a commitment to a normal form for what will be understood to mean that the entire sub-tree rooted at this node is empty (that is, for example, if filled out to full depth, the sub-tree&#39;s leaves would consist entirely of non-members of the set). In practice, this commitment might be implemented by computing the value of the hash of a random bit-string of a fixed length (say, 100 bits), preceded by an appropriate-length string of 0 bits. 
   In another embodiment of a weak commitment scheme, suppose that a set contains “n” items. In one embodiment, the committer constructs a Merkle tree with O(n^2) leaves in the following way: Let “c” be a constant such that the probability of the following collision event is low. Choose h( ) as a pair-wise independent hash function whose range is the set of integers [1,c*n^2]. Choose n^2 random items a — 1, . . . , a_{n^2}. The collision event occurs if there are two different indices i, j for which h(a_i)=h(a_j). 
   Given “c,” the committer defines a binary tree with c*n^2 leaves. The committer chooses a pair-wise independent hash function h( ) whose range is [1,c*n^2]. The choice of h( ) is independent of the set that is committed to. (Choosing h( ) in a different way does not enable the committer to cheat, and might even hurt the operation of the committer). 
   Each item a_i in the set is associated with the leaf with index h(a_i). The committer stores in this leaf the value a_i. (Here, a_i is stored rather than just the value 1, since the committer can easily find two distinct values a_i, a_i′ which map to the same leaf, and the construction must ensure that it commits to a single value). All other leaves are labeled with 0. The committer computes the values of internal nodes, as in an ordinary Merkle tree, using a collision-resistant hash function (perhaps a different function from “h”) and publishes the root value. 
   To show that an item “b” is in the set, the committer reveals the values of the siblings of the nodes in the path from the leaf with index h(b) to the root. The verifier checks this proof by assigning the value “b” to the leaf with index h(b), recomputing the nodes in the path to the root, and checking that the resulting root value is equal to the value that was previously published by the committer. To prove that a value “b” is not in the tree, the committer provides the same list of values, and the verifier assigns the value 0 to the leaf h(b) and proceeds as just described. 
   The correctness of the construction results from the fact that if h( ) is chosen independently of the set, then with high probability there are no two items which are mapped to the same leaf. Therefore, the committer can construct the tree. Now, given a published root value, the committer cannot assign two different values to a leaf. Therefore, each leaf can either have a 0 value or a value which is equal to a single output of h( ), and since the tree is built with a collision-resistant hash function, this value can only correspond to a single value. Constructing the tree requires O(n^2) applications of a hash function. Providing an answer requires O(n) communication and work. 
   As mentioned above, in one embodiment, a function f_k is used to map identities to pseudo-identities, using one of a family of functions that is keyed by a key “k.” The function f_k is both collision-resistant and verifiably pseudorandom. 
   A collision-resistant function has the following property: it is impossible, even knowing “k,” to find two distinct valid input values “a” and “b” such that f_k(a)=f_k(b). The collision resistance property can be achieved by applying a collision-resistant hash function, such as SHA-2, to any input, and then applying to the result a function f_k which is a pseudo-random verifiable permutation. (Such a permutation can be constructed from a pseudo-random verifiable function using the Luby-Rackoff construction, for example.) Alternatively, the analysis of security can model the hash function (such as SHA-2) as a random oracle. In this case, a more efficient implementation is possible, and a pseudo-random verifiable function can be applied directly to the output of the hash function. 
   Without knowing “k,” the outputs of f_k are pseudorandom. This is true even given several input-output pairs for f_k. That is, given f_k(a — 1), . . . , f_k(a_m), for any items a — 1, . . . , a_m, it holds that for any value “b” not in {a — 1, . . . a_m} it is infeasible to distinguish f_k(b) from a random value in the range of f_k. 
   A party that knows “k” (and perhaps some additional information about the choice of “k”) can prove the correctness of statements of the form “f_k(b)=y.” That is, it can be demonstrated that the pseudo-identity of “b” is indeed “y” if the same function f_k is applied to “b” that was also applied to the items a — 1, a_m in the user profile. This is accomplished without leaking any information about other values of f_k, in order to preserve the pseudorandomness property. 
   There are several approaches that can be used to prove the correctness of statements of the form “f_k(b)=y.” In a first embodiment, the committer sends the statement “f_k(b)=y” together with verification information that enables the verification of this specific value of f_k. The verification can then be carried out without further communication with the committer. In this case, any party that learns f_k(b) and receives the proof can convince a third party of the correctness of f_k(b). In a second embodiment, a verification of the statement “f_k(b)=y” is accomplished using an interactive protocol between the committer and the verifier. In this case, the party that learns f_k(b) might not be able to convince third parties of the correctness of this value. 
   To summarize, a committer picks a key “k” and uses the collision resistant verifiable pseudorandom function f_k to compute the pseudo-identities f_k(a — 1), . . . , f_k(a_n). The committer commits to the pseudo-identities using a weak set commitment scheme, and publishes verification information for f_k. To answer a query “Is the value ‘b’ in the set?”, the committer answers the query, computes and publishes the pseudo-identity f_k(b), and uses the verification information to prove that this value was computed correctly—that is, that f_k(b) was determined using the same f_k used to determine f_k(a — 1), . . . , f_k(a_n). The parties then use the weak set commitment scheme to verify the answer to the query. For example, in an embodiment in which the list of pseudo-identities f_k(a — 1), . . . , f_k(a_n) are published, the pseudo-identity f_k(b) is compared to the published list to determine if f_k(b) is in the list. 
   The committer cannot change the committed-to user profile because of the following properties: 1) the infeasibility of finding collisions in the function f_k; 2) the verifiability of the function; and 3) the security of the weak set commitment scheme. In other words, in order to falsely add an item “z” to the committed-to user profile, the committer must find two values (z and z′) such that f_k(z)=f_k(z′), which violates the first property; or claim that f_k(z′)=f_k(z) for a value “z” which is different than an element z′ of the committed-to user profile, which violates the second property; or show that f_k(z′) is in the committed-to user profile, which violates the third property. Similar arguments can be used to show that it is infeasible for the committer to remove an item from the committed-to user profile. 
   Furthermore, the privacy of the committed-to user profile is preserved because the verifier cannot learn about inputs from the committer that are not specifically contained in queries. That is, even though the verifier may learn all pseudo-identities f_k(a — 1), . . . , f_k(a_n), from the perspective of the verifier all values of f_k look pseudo-random, except for the pseudo-identity of the item named in the query. 
   Two example approaches for implementing f_k are discussed below. One example is based on the Naor-Reingold pseudorandom function (PRF), and one is based on “arithmetic in the exponents” with a polynomial of degree “t.” 
   In a first example approach for implementing f_k, the Naor-Reingold (NR) PRF, when applied to “m” bit inputs, uses a key of the form:
 
 k=[r, k   — 0, . . . ,  k   —   {m− 1}],
 
and a public parameter “g” which is a generator of a group in which the Decisional-Diffie-Hellman (DDH) assumption is assumed to hold, where “r” and k — 0, . . . , k_{m−1} are random exponents (e.g., random positive integers less than the order of “g”). Let the binary representation of “x” be x_{m−1}, . . . , x — 0. Then:
 
 f   —   k ( x )= f   —   k ( x   —   {m− 1 }, . . . , x   — 0)=( g^r )^ p,  
 
where “p” is the product of those k_i values for which the corresponding x_i=1. For example,
 
 f (5)= g^{r*k   — 2 *k   — 0}.
 
The “global” verification information that the committer publishes for “k” is:
 
[h, h^r, h^{k — 0}, . . . , h^{k_{m−1}}],
 
where “h” is another generator of the group. This information is used for proving statements about the correctness of f_k for all input values. For example, to prove that f(5) is correct, the committer publishes [h^{r*k — 0}, h^{r*k — 0*k — 2}], and proves the following:
 
   1. The discrete log of h^{r*k — 0} to the base h^r is the same as the discrete log of h^{k — 0} to the base “h;” 
   2. The discrete log of h^{r*k — 0*k — 2} to the base h^{r*k — 0} is the same as the discrete log of h^{k — 2} to the base “h;” and 
   3. The discrete log of h^{r*k — 0*k — 2} to the base g^{r*k — 0*k — 2} is the same as the discrete log of “h” to the base “g.” 
   Each of these proofs can be performed either interactively or non-interactively (using the Fiat-Shamir paradigm and the random oracle methodology, for example). The overhead of each of these proofs is a small constant number of exponentiations. The above example can be readily generalized to proving the correctness of statements f_k(a)=y for all values of “a.” The total overhead depends on the number of bits set to 1 in the committed-to value. 
   In a second example approach for implementing f_k, the function is defined as a polynomial of degree “t” with coefficients k=&lt;k — 0, . . . , k_t&gt;. In addition, the committer defines another polynomial “r,” also of degree “t,” with coefficients &lt;r — 0, . . . , r_t&gt;. There are public parameters “g” and “h” that are generators of a group in which the DDH problem is hard, and it is ensured that the committer does not know the discrete logarithm of “h” to the base “g.” The global verification information that the committer publishes is a list [e — 0, . . . , e_t], which is defined as:
 
[ e   — 0 =g^{k   — 0}* h^{r   — 0}, . . . ,  e   —   t=g^{k   —   t}*h^{r   —   t}].  
 
This information is used for all proofs, and no additional information has to be provided by the party that knows “k” (and consequently, the proofs are non-interactive). When the committer publishes a value of f_k(a), the committer also publishes the value of r(a). It is now possible to verify that value without additional interaction with the committer. To verify that f_k(a)=w, first compute:
 
 v   — 1= g^w*h^{r ( a )},
 
and then compute:
 
 v   — 2= e   — 0*( e   — 1)^ a * . . . *( e   —   t )^{ a^t}.  
 
The verification succeeds if and only if v — 1=v — 2. The system is secure as long as an adversary learns at most t+1 values of the polynomial. The overhead of the proof is linear in “t.”
 
   As a variant to the approaches described generally and by example above, f_k can implement symmetric encryption with key “k” using, for example, the Advanced Encryption Standard (AES), in which case the committer sends “k” along with the modified Merkle Tree described above (see  FIG. 23 ). After receiving an answer to a query about a particular element “x,” enabling the computation of the path from the leaf f_k(x) to the root of the tree, the verifier can infer the non-existence of set elements that are mapped by f_k to certain sub-trees; the non-existent set elements are ruled out by interior nodes that were redefined to 0 during construction of the modified Merkle Tree exemplified by  FIG. 23 . To reduce the knowledge that a verifier may gain about any input x′ by testing whether f_k(x′) is in one of these excluded sub-trees, at regular intervals the committer can re-randomize by choosing a new value for “k,” re-computing the modified Merkle Tree, and sending the new tree root and the new value for “k.” 
   The approaches described above are not limited to answering yes/no types of queries. In one embodiment, arbitrary data is associated with each of the elements in the list “S” of committed elements using the following procedure: for every element “x” in the set “S,” rather than storing 1 in the leaf at location f_k(x) of the modified Merkle Tree, the data associated with “x” is stored. 
     FIG. 24  is a flow chart  2400  of a method for verifying information is in a database (e.g., a user profile) according to one embodiment of the present invention. Although specific steps are disclosed in flow chart  2400 , such steps are exemplary. That is, embodiments of the present invention are well-suited to performing various other steps or variations of the steps recited in flow chart  2400 . It is appreciated that the steps in flow chart  2400  may be performed in an order different than presented, and that not all of the steps in flow chart  2400  may be performed. In one embodiment, flow chart  2400  includes processes of the present invention that, in one embodiment, are carried out by a processor under the control of computer-readable and computer-executable instructions. Networks and systems for implementing the method of flow chart  2400  are described above (see the discussion in conjunction with  FIGS. 5 ,  12  and  16 , for example). 
   In block  2401  of  FIG. 24 , a first version of a database is accessed. In one embodiment, the database constitutes a user profile that is generated as previously described herein. The database includes the identities for items of information represented in the database. 
   In block  2402 , a function (e.g., a function f_k) is applied to the identities in the database, or user profile, to change the identities to pseudo-identities, effectively concealing or disguising the identities of the items of information. 
   In block  2403 , in one embodiment, a commitment is made to a second version of the database, or user profile. The second version contains the changed identities—the pseudo-identities—that were created as described by block  2402 . Various approaches for committing to the set of values in the second version are described above. In another embodiment, a commitment is made to the first version of the database, or user profile, and then the function is applied to create the pseudo-identities. In general, the second version represents the committed-to version of the database or user profile. 
   In block  2404 , in one embodiment as previously described herein, an offer of data (e.g., an advertisement) is issued or broadcast. Associated with the offer is a query that includes selection criteria used to determine whether a user is eligible to be presented with the data. The query seeks to determine whether a particular item is represented in the user&#39;s user profile. Accordingly, the function of block  2402  is applied to the item named in the offer&#39;s query to generate a changed identity for that item. 
   In block  2405 , in one embodiment, a determination is made as to whether the changed identity of the particular item named in the query is included in the second (committed-to) version of the database or user profile (see block  2403 ), and hence the query item is in the first version of the database or user profile. 
   In block  2406 , in one embodiment, verification information is provided in order to demonstrate that the changed identity for the particular item identified in the query was determined using the function of block  2402 . Various approaches for providing verification information are described above. 
   In block  2407 , in one embodiment, if the conditions of block  2405  and  2406  are satisfied, then the user is eligible to receive the data targeted to the user, which is presented to the user if the offer is accepted. Significantly, this is achieved without requiring a release of any information about the identities of any other items in the first version of the database or user profile. 
     FIG. 25  is a block diagram of a system  2500  for verifying information is in a database according to one embodiment of the present invention. With reference also to  FIG. 3 , system  2500  may be implemented on server  320  or client  340 , or the functionality of system  2500  may be distributed between server  320  and client  340 . 
   In the example of  FIG. 25 , system  2500  includes a compiler  2501  for compiling or generating a database or user profile, as previously described herein. The identity changer  2502  is used to create changed identities, or pseudo-identities, by applying a function to the identities of the items of information in the database or user profile, as previously described herein. Committer  2503  commits the version of the database or user profile that includes the pseudo-identities, as previously described herein. Comparator  2504  compares the identity of an item of information included in an offer&#39;s query to the pseudo-identities in the committed-to database or user profile. Specifically, a pseudo-identity is created for the item of information named in the query, using the same function applied by identity changer  2502 , as previously described herein. Function prover  2505  provides verification information to demonstrate that the function applied to the item of information named in the query is the same function used by identity changer  2502 , as previously described herein. 
   In summary, according to the present invention, a commitment is made to a set “S” of items, while hiding the actual values of the items. At a later time, it can be proven whether or not a specific item or items belong to the set. The committer is prevented from changing the set “S” that has been committed. The commitment to the set does not reveal any information about the items in the set, except perhaps for an upper bound on the number of items in the set. Also, a proof of a statement of the form “x is in S” or of a statement “x is not in S” does not reveal information about whether any item different from “x” is in the set or not. Furthermore, updates of the items in the set—by the party that made the initial commitment—are accomplished efficiently. 
   In particular, the present invention enables a committer to commit to a version of a user profile that conceals the real identities of the information in the user profile. After the commitment to the user profile is performed, advertisers or retailers or content providers, for example, can provide offers that are targeted to the user. The commitment scheme described herein enables the committer to prove that the user profile matches the requirements of the offer. Accordingly, embodiments in accordance with the present invention discourage spam and are more efficient in targeting the data. Importantly, the advertisers or retailers or content providers can target an offer for data without learning anything more about the contents of the user profile other than that which is implied by the fact that the offer applies to the user—more specifically, that the user profile satisfies the offer&#39;s query. Furthermore, the committer cannot change the user profile, and the proof is done without revealing any other information about the committer&#39;s user profile. 
   Embodiments in accordance with the present invention provide a number of advantages over conventional approaches. In general, embodiments in accordance with the present invention provide techniques that process databases more quickly and that consume fewer computer system resources, while protecting or concealing the information in the database. For instance, the present invention uses many fewer invocations of public key operations. Namely, according to embodiments of the present invention, O(n) public key operations are performed to prepare the set commitment, while conventional approaches require O(n log M) public key operations, where “M” is the size of the domain of the committed values and is typically 2 160  or larger. 
   Furthermore, according to the present invention, O(1) public key operations or, in some embodiments, no public key operations at all are used to add a new item to the set of committed values, while deleting an item does not involve public key operations. In contrast, the overhead of conventional approaches for adding or deleting items is of the same order as that of constructing a new set—that is, O(n log M) public key operations. 
   Embodiments of the present invention are described. While the invention is described in conjunction with these embodiments, it is understood that they are not intended to limit the invention to these embodiments. On the contrary, the invention is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the detailed description of the present invention, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be recognized by one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail as not to unnecessarily obscure aspects of the present invention.