DESC:METHOD AND SYSTEM FOR DISTRIBUTION OF ADVERTISEMENT FRAUD DATA TO THIRD PARTIES

TECHNICAL FIELD

[0001] The present disclosure relates to the field of fraud detection systems and, in particular, relates to a method and system for distribution of advertisement fraud data to the third parties.

BACKGROUND

[0002] With the advancements in technology over the last few years, users have predominantly shifted towards smartphones for accessing multimedia content. Nowadays, users access content through a number of mobile applications available for download through various online application stores. Businesses (Advertisers) have started focusing on generating revenue by targeting consumers through these mobile applications. In addition, businesses have started investing heavily on doing business through these mobile applications. Moreover, businesses (publisher and/or advertising networks) have started developing advertisement capable applications for serving advertisements through these mobile applications. These advertisements are published in real time or fixed placements through these mobile applications and watched by the users. The advertisers are benefited in terms of internet traffic generated on clicking, taking action like installing or on watching these advertisements. However, certain online publisher and advertising networks working with these publishers take undue advantage of this in order to generate high revenues. These online publishers and advertising networks employ fraudulent techniques in order to generate clicks, or increasing actions like increasing number of application installs for the advertisers through fraudulent means. In addition, these online publishers incentivize the users for clicking the links, downloading applications and the like. This results in a loss of advertisers marketing budget spent as many times these publishers claim a normal user-initiated action (Organic action, e.g. Organic Install) as one initiated by them or at times the clicks or application installs are not driven by humans at all and instead by bots. There is a consistent need to stop publishers from performing such types of click fraud and transaction fraud.
OBJECT OF THE DISCLOSURE

[0003] A primary object of the present disclosure is to provide a method and system for distribution of advertisement fraud data to third parties in real time.

[0004] Another object of the present disclosure is to alert third parties about the fraudulent behavior of the user or publisher and accordingly take action.

[0005] Yet another object of the present disclosure is to stop users or publisher who is doing mobile advertisement fraud.

[0006] Yet another object of the present disclosure is to provide secure method to the third parties

[0007] Yet another object of the present disclosure is to prevent loss to third parties by providing access to information related to fraudulent activities.

SUMMARY

[0008] In one aspect, the present disclosure provides a computer system. The computer system includes one or more processors and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of instructions causes the one or more processors to perform a method to detect advertisement fraud based on time between events. The method includes a first step to receive a request from one or more third parties to access fraud data. In addition, the method includes second step to correlates data from the one or more third parties and the fraud data. The correlation is done after authorizing the one or more third parties for accessing the fraud data. Further, the method includes third step to analyze publisher data, application data and the fraud data collected after correlation. The analysis is done after optimizing rules for identification of fraud. Furthermore, the method includes fourth step to block the publisher based on the analysis. The request include set of data and data associated with at least one publisher. The correlation is done in real time. The analysis is done to identify the publisher in the blacklist or whitelist. The blocking is done to stop the publisher from publishing one or more advertisement on one or more media devices by fraud and add the publisher in the blacklist.

[0009] In an embodiment of the present disclosure, the fraud data include blacklist and whitelist. The blacklist includes the publisher showing fraudulent activity. The whitelist includes the publisher using genuine means for showing the one or more advertisement on the one or more media devices. The fraud data represents the publisher in the blacklist or whitelist by way of IP address and device Id’s.

[0010] In another embodiment of the present disclosure, the publisher data includes number of click, past revenue generated by the publisher, number of transaction, location of click, number of install, interaction data and time-stamp.

[0011] In yet another embodiment of the present disclosure, the application data includes application size, time to download, time to run, redirection time, click to install and click to run. Further, the application idea includes user click time, device load time, time to run, time to install, network download time, application usage time, application idle time and application opening time.

[0012] In yet another embodiment of the present disclosure, the rules are conditions specified by the one or more third parties in order to list the publisher as fraud or genuine. The rules are specified by the one or more third parties with the connection request as the set of data.

[0013] In yet another embodiment of the present disclosure, the data sharing platform may authorize the publisher based on the analysis. The authorization is done to allow the publisher to publish the one or more advertisement on the one or more media devices and add the publisher in the whitelist.

[0014] In yet another embodiment of the present disclosure, the data sharing platform may notifies the one or more third parties about the publisher using fraud means showing the one or more advertisements. The notification is send by e-mail or message to the one or more third parties in real time

[0015] In yet another embodiment of the present disclosure, the data sharing platform may block payment to the publisher from the one or more third parties by integrating with the automatic payment network. The blocking is done to stop the payment for the publisher present in the blacklist of the fraud data.

[0016] In yet another embodiment of the present disclosure, the data sharing platform may generate report in a pre-defined interval of time. The report include the publisher who are using genuine or fraud means for the publishing of the one or more advertisement on the one or more media devices

BRIEF DESCRIPTION OF FIGURES
[0017] Having thus described the invention in general terms, references will now be made to the accompanying figures, wherein:

[0018] FIG. 1 illustrates an interactive computing environment between users and one or more components for distribution of mobile advertisement fraud data to third parties in real time, in accordance with various embodiments of the present disclosure; and

[0019] FIG. 2 illustrates a block diagram of a computing device, in accordance with various embodiments of the present disclosure.

[0020] It should be noted that the accompanying figures are intended to present illustrations of exemplary embodiments of the present invention. These figures are not intended to limit the scope of the present invention. It should also be noted that accompanying figures are not necessarily drawn to scale.

DETAILED DESCRIPTION

[0021] Reference will now be made in detail to selected embodiments of the present invention in conjunction with accompanying figures. The embodiments described herein are not intended to limit the scope of the invention, and the present invention should not be construed as limited to the embodiments described. This invention may be embodied in different forms without departing from the scope and spirit of the invention. It should be understood that the accompanying figures are intended and provided to illustrate embodiments of the invention described below and are not necessarily drawn to scale. In the drawings, like numbers refer to like elements throughout, and thicknesses and dimensions of some components may be exaggerated for providing better clarity and ease of understanding.

[0022] It should be noted that the terms "first", "second", and the like, herein do not denote any order, ranking, quantity, or importance, but rather are used to distinguish one element from another. Further, the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.

[0023] FIG. 1 illustrates an interactive computing environment 100 for distribution of or or more advertisements frauds to third parties in real time. The interactive computing environment 100 shows a relationship between various entities involved in the distribution of advertisement frauds to third parties. The advertisement fraud is a type of fraud which is done to generate more revenue from the one or more advertisements being displayed by generating fake install or clicks. The fake install is done with the help of software, bots. The fake install or fake traffic is done through techniques such as click fraud, transaction fraud and the like. The click fraud corresponds to regular or constant clicking by one or more users 102 on the one or more advertisements in order to generate more revenue for a publisher. The click fraud is when the publisher gets paid based on pay-per-click or pay-per-view whenever the one or more advertisements is clicked. The click fraud refers to the generation of fraudulent clicks through online bots which are not identifiable and are treated as genuine install. The transaction fraud refers to initiating install via fake clicks and bots (as described above in the application). The transaction fraud takes place when the publisher applies fraudulent techniques to drive fake installs of applications in order to generate more revenue.

[0024] The interactive computing environment 100 includes the one or more users 102, one or more media devices 104, a publisher 106, a data sharing platform 108, a server 110, a database 112 and one or more third parties 114. Each of the components of the interactive computing environment 100 interacts with each other to share advertisement fraud data in real-time.

[0025] The interactive computing environment includes the one or more users 102 who is any person present at any location and access the multimedia content. The one or more users 102 is any legal person or natural person who access online multimedia content and need an IP based network for accessing the multimedia content. In addition, the one or more users 102 are individuals or persons who accesses online multimedia content on the respective one or more media devices 104. In another embodiment of the present disclosure, the one or more users 102 are a computer or bots who is programmed to view the one or more advertisements and performs click and transaction. In an embodiment of the present disclosure, the one or more users 102 includes but may not be limited to a natural person, legal entity, the individual, machine and robots for viewing the one or more advertisements. The one or more users 102 are associated with the one or more media devices 104.

[0026] The interactive computing environment further includes the one or more media devices 104 which help to communicate information. The one or more media devices 104 includes but may not be limited to a Smartphone, a laptop, a desktop computer, a tablet and a personal digital assistant. In an embodiment of the present disclosure, the one or more media devices 104 include a smart television, a workstation, an electronic wearable device and the like. In an embodiment, the one or more media devices 104 include portable communication devices and fixed communication devices. In an embodiment of the present disclosure, the one or more media devices 104 are currently in the switched-on state. The one or more users 102 accesses the one or more media devices 104 in real-time. The one or more media devices 104 are any type of devices having an active internet. The one or more media devices 104 are an internet-enabled device for allowing the one or more users 102 to access the publisher 106. In an embodiment of the present disclosure, the one or more users 102 are owner of the one or more media devices 104. In another embodiment of the present disclosure, the one or more users 102 are not the owner of the one or more media devices 104. In addition, the one or more media devices 104 are used for viewing an application installed on the one or more media devices 104.

[0027] The interactive computing environment 100 further includes the publisher 106 used for viewing content on the one or more media devices 104. The publisher 106 includes but may not be limited to mobile application, web application, and website. The publisher 106 is the mobile application which displays content to the one or more users 102 on the one or more media devices 104. The content may include one or more publisher content, one or more video content and the like. The application or the publisher 106 accessed by the one or more users 102 shows content related to the interest of the one or more users 102. In an example, the one or more users 102 are interested in watching online videos, reading blogs, play online games, accessing social networking sites and the like. The publisher 106 is the application developed by the application developer for viewing or accessing specific content. The publisher 106 or applications are advertisement supporting applications which are stored on the one or more media devices 104. The publisher 106 or mobile applications are of any type which includes gaming application, a utility application, a service based application and the like. The publisher 106 provides space, frame, area or a part of their application pages for advertising purposes is referred to as advertisement slots. The publisher 106 consists of various advertisement slots which depend on the choice of the publisher 106. The publisher 106 advertises products, services or businesses to the one or more users 102 for generating revenue. The publisher 106 displays one or more advertisements on the one or more devices 104 when the one more users 102 are accessing the publisher 106.

[0028] The one or more advertisements are a graphical or pictorial representation of the information to promote a product, an event, service and the like. In general, the one or more advertisements are a medium for promoting a product, service, or an event. The one or more advertisements include text advertisement, video advertisement, graphic advertisement and the like. The one or more advertisements are displayed in third party applications developed by application developers. The one or more advertisements are presented for attracting the one or more users 102 based on the interest in order to generate revenue. The one or more advertisements are shown to the one or more users 102 based on the interest of the one or more users 102 and shown for a specific period of time. The one or more users 102 clicks on the one or more advertisements and the one or more users 102 is re-directed to a website or application or application store associated with the clicked one or more advertisements. The one or more advertisements are providing to the publisher 106 by one or more advertisers who want to advertise their product, service through the publisher 106. The publisher 106 gets paid if the one or more users 102 visit the application or website through the one or more advertisements of the one or more advertisers. The number of users who visits the one or more advertisements through the publisher 106 generates more revenue for the publisher 106.

[0029] The one or more advertisers are those who want to advertise their product or service and the like to the one or more users 102. The one or more advertisers approach the publisher 106 and provide the one or more advertisements for display for the one or more users 102 on the publisher 106. The one or more advertisers pay the publisher 106 based on the number of users being redirected or taking the product or services provided by the one or more advertisers.

[0030] The one or more advertisements are placed on the advertisement slots in the publisher application on the one or more media devices 104 associated with the one or more users 102. The one or more advertisers purchase the advertisement slots from the publisher 106. The one or more advertisements may be served based on a real-time bidding technique or a direct contract between the one or more advertisers and the publisher 106. The one or more advertisers provide the one or more advertisements to advertising networks and information associated with advertising campaigns. The advertisement networks enable display of the one or more advertisements in real-time on the publisher 106 on behalf of the one or more advertisers. The advertising networks are entities that connect the one or more advertisers to websites and mobile applications that are willing to serve advertisements.
[0031] The interactive computing environment 100 includes the data sharing platform 108. The data sharing platform 108 is used for sharing fraud data with the one or more third parties 114 for finding fraud being done by the one or more users 102 or the publisher 106. The data sharing platform 108 is a platform for integrating with the one or more third parties 114 for detecting fraud done by the one or more users 102 or the publisher 106 in real-time. The data sharing platform 108 performs sharing of fraud data to the one or more third parties 114 in real time and detection of fraud in the one or more advertisements in real time. The data sharing platform 108 performs the fraud detection based on integration with the one or more third parties 114 or the one or more advertisers. The data sharing platform 108 detect click fraud and transaction fraud done by the publisher 106 or the one or more users 102. In an embodiment of the present disclosure, the data sharing system 108 take actions accordingly based on the fraud detected by the data sharing system 108. The data sharing platform 108 is associated with the server 110.

[0032] The server 110 performs the task of accepting a request and respond to the request for other functions. The server 110 may be a cloud server which is used for cloud computing to enhance the real-time processing of the system and using virtual space for task performance. The cloud server is built, hosted and delivered through a cloud computing platform. The cloud computing is the process of using remote network server which hosts on the internet to store, manage, and process data. The use of cloud server helps to access the data sharing platform 108 to be accessed from anywhere using the internet. The server 110 performs the task of accepting request and responding to the request of other functions. The server 110 handles each operation and task performed by the data sharing system 108. The server 110 stores one or more instructions for performing the various operations of the data sharing platform 108. In an embodiment of the present disclosure, the data sharing platform 108 is located on the server 110. In another embodiment of the present disclosure, the data sharing platform 108 is located on the one or more media devices 104. The server 110 includes database 112 which is used for storing data in real-time. Further. The server 110 is associated with the one or more third parties 114.

[0033] The database 112 is an area where all the information is stored for access during the functioning of the data sharing system 108. The database 112 includes data which is pre-stored in the database 112 and data collected in real-time. In an embodiment of the present disclosure, the database 112 is a cloud database or any other database based on the requirement of the data sharing platform 108. The data is stored in the database 112 in various tables. The tables are a matrix which stored different type of data. In an example, one table may store data related to the one or more users 102 and in another table the one or more media devices 104 related data is stored.

[0034] The one or more third parties 114 are those parties who want to identify fraud in their system or method to prevent fraud in real-time. The one or more third parties 114 includes but may not be limited to cyber security provider, one or more advertisers, advertisement networks and stakeholders. The one or more third parties 114 include a bank, payment gateway, security services, and the like. The one or more third parties 114 connect with the data sharing platform 108 to integrate their database with the data sharing platform 108 to identify fraud. The one or more third parties 114 provide access to their database to identify fraud and detect abnormality with the one or more users 102 or the publisher 106. The one or more third parties 114 communicate or access the data sharing platform 108 through the server 110. The one or more third parties 114 sends a connection request to the data sharing platform 108 for accessing the fraud data stored in the data sharing platform 108.

[0035] The fraud data include blacklist, whitelist, publisher data, past data and the like. The blacklist includes a list of the publisher 106 showing fraudulent activity which is identified in the past or real-time as performing fraud behavior. The whitelist includes the list of the publisher 106 using genuine means for showing the one or more advertisements on the one or more media devices 104. The fraud data represents the publisher 106 in the blacklist or the whitelist by way of IP address and device Id’s.

[0036] The data sharing platform 108 receives the connection request from the one or more third parties 114 to access the fraud data. The request is received from the one or more third parties through the server 110 for accessing the fraud data. The request includes a set of data and data which is associated with at least one publisher 106. The data includes the publisher data, the data collected by the one or more third parties 114 when the one or more users 102 view the one or more advertisements. The publisher data includes but may not be limited to number of click, past revenue generated by the publisher 106 and number of transaction. In an embodiment of the present disclosure, the publisher data includes time stamp, location of click, interaction data, number of install and the like. The set of data include rules which are provided by the one or more third parties 114 for performing fraud detection after integrating with the one or more third parties 114. The set of data includes but may not be limited to thresholds for identifying the publisher 106 as fraud, the rules for adding the publisher 106 or the one or more users 102 in the blacklist or the whitelist. The set of data includes rules for removing the publisher 106 from the whitelist or the blacklist.

[0037] In addition, the data sharing platform 108 authorize the one or more third parties 114 for accessing the fraud data of the data sharing platform 108 based on the connection request. The authorization of the one or more third parties 108 for accessing the fraud data is done to allow the one or more third parties 114 to access and associate the fraud data. The association of the fraud data of the one or more third parties 114 with the data sharing platform 108 will help to identify the publisher 106 who are performing a fraudulent activity. The authorization of the one or more third parties 114 is done based on username and password provided to the one or more third parties 114. The username and password are provided to the one or more third parties 114 for accessing the data sharing platform 108. In an embodiment of the present disclosure, the authorization of the one or more third parties 114 may be performed by using digital signature which is provided to the one or more third parties 114.

[0038] Further, the data sharing platform 108 after authorizing the one or more third parties 114 correlates data from the one or more third parties and the fraud data. The correlation between the data of the one or more third parties 114 and the fraud data stored in the database 112 is done to identify the publisher 106. The correlation help to enhance the fraud data based on the data collected from the one or more third parties 114 and the fraud data of the data sharing platform 108.

[0039] Furthermore, the data sharing platform 108 optimize rules for identification of fraud being done by the publisher 108. The rules for identification of fraud are set of rules defined by the one or more third parties 114 to identify fraud based on these rules. The rules are conditions specified by the advertiser or the one or more third parties 114 in order to list a publisher 106 or the one or more users 102 as fraud or genuine. The rules are specified by the one or more third parties with the connection request for identifying fraud. The optimization of the rules is done based on set of data from the one or more third parties 114. The set of data is received from the one or more third parties 114 during the connection request. The set of data are used for optimizing the rules for the at least one of the one or more third parties 114.

[0040] In an example, third parties X send connection request to the data sharing platform 108 for accessing the fraud data. The connection request includes the set of data and the data associated with the at least one publisher 106. The data sharing platform 108 authorize the third parties X and further correlation is done between the data of the publisher 106 and the fraud data of the data sharing platform 108. The after correlation of the fraud data, the data sharing system 108 optimize the rules based on the set of data received with the connection request. The optimization is done to identify fraud based on the rules set by the third party X.

[0041] Moreover, the data sharing platform 108 analyze publisher data, application data and the fraud data collected after correlation. The analysis is done to identify the publisher 106 in the blacklist or the whitelist. The publisher data includes number of click, past revenue generated by the publisher 106, number of transaction and the like. In an embodiment of the present disclosure, the publisher data includes but may not be limited to time stamp, location of click, interaction data and number of install.

[0042] The application data includes application size, time to download, time to run, redirection time, click to install, click to run, user click time and the like. In an embodiment of the present disclosure, the application data includes but may not be limited to device load time, time to run, time to install, network download time and application usage time. In another embodiment of the present disclosure, the application data includes application idle time, application opening time, number of user click, network speed, country bandwidth and the like.

[0043] The analysis of the publisher data, the application data and the fraud data after correlation is done by the fraud detection platform 108 based on the rules to identify fraud. The fraud detection platform 108 identifies fraud based on the threshold provided as rules by the one or more third parties. The threshold is used to mark the publisher 106 or the one or more users 102 as using fraud means. The fraud means is done for generating revenue or the one or more users 102 being bots or a software used for performing click and transaction fraud. The analysis is done to check if the one or more users 102 or the publisher 106 is already present in the whitelist or the blacklist.

[0044] Also, the data sharing platform 108 blocks the publisher 106 or the one or more users 102 based on the analysis. The blocking of the publisher 106 is done to block the publisher 106 for publishing the one or more advertisement on the one or more media devices 104. The blocking of the one or more users 102 for performing the transaction or download of the publisher 106 (mobile application). The blocking is done based on the threshold. If the analysis identifies that the threshold defined in the rules is crossed than the publisher 106 is blocked to publish the one or more advertisements on the one or more media devices 104. Further, the data sharing platform 108 add the publisher 106 or the one or more users 102 in the blacklist.

[0045] In an embodiment of the present disclosure, the analysis is done to identify a score for the publisher 106 or the one or more users 102. Further, the data sharing platform 108 blocks the publisher 106 or the one or more users 102 by comparing the score of the publisher 106 or the one or more users 102 with the threshold. The blocking stops the publisher 106 from publishing the one or more advertisement on the one or more media devices 104 and adds the publisher 106 or the one or more users 102 in the blacklist.

[0046] Also, the data sharing platform 108 authorize the publisher 106 or the one or more users 102 based on the analysis. The authorization of the publisher 106 is done to allow the publisher 106 for publishing the one or more advertisement on the one or more media devices 104. The authorization of the one or more users 102 for performing the transaction or download of the publisher 106 (mobile application). The authorization is done based on the threshold. If the analysis identifies that the threshold defined in the rules has not been crossed than the publisher 106 is authorize to publish the one or more advertisements on the one or more media devices 104.

[0047] In an embodiment of the present disclosure, the analysis is done to identify a score for the publisher 106 or the one or more users 102. Further, the data sharing platform 108 authorize the publisher 106 or the one or more users 102 by comparing the score of the publisher 106 or the one or more users 102 with the threshold. The authorization allows the publisher 106 to publish the one or more advertisement on the one or more media devices 104. Further, if the data sharing platform 108 identifies that the publisher 106 or the one or more users 102 is not performing any fraud based on the analysis. The data sharing platform 108 removes the one or more users 102 or the publisher 106 from the blacklist. In addition, the data sharing platform 108 adds the publisher 106 or the one or more users 102 in the whitelist of the data sharing platform 108.

[0048] Also, the data sharing platform 108 notify the one or more third parties 114 or the one or more advertisers about the publisher 106 using fraud means. The notification is sent by e-mail or message to the one or more third parties or the one or more advertisers in real time. In an embodiment of the present disclosure, the notification is sent to the one or more third parties 114 or the one or more advertisers through any other means suitable for sending a communication.

[0049] Also, the data sharing platform 108 integrate with automatic payment network of the one or more third parties 114. The integration with the automatic payment network of the one or more third parties 114 allows the data sharing platform 108 to block the payment of the publisher 106. The blocking of payment of the publisher 106 is done when the analysis identifies that the publisher 106 or the one or more users 102 is present in the blacklist of the fraud data of the data sharing platform 108.

[0050] Also, the data sharing platform 108 generates a report in a pre-defined interval of time for the one or more third parties 114. The pre-defined interval of time is defined by the one or more third parties 114. The report generated includes the publisher 106 or the one or more users 102 who are using genuine or fraud means for publishing of the one or more advertisement on the one or more media devices 104. The report generated is sent to the one or more third parties who have integrated with the data sharing platform 114 to inform the status of the publisher 106 or the one or more users 102.

[0051] In an embodiment of the present disclosure, the data sharing platform 108 integrate with the fraud data from the third party databases. The integration is done in order to generate fraud data comprising of the publisher 106 and the one or more users 102 who are performing fraud and adding them to the whitelist or blacklist. The integration is done with the fraud data of the third party databases by correlating it with the fraud data of the third party databases.

[0052] In another embodiment of the present disclosure, the data sharing platform 108 stores the fraud data, the publisher data, the application data and the rules. The data sharing platform 108 stored the data in the database 112 in real-time.

[0053] In yet another embodiment of the present disclosure, the data sharing platform 108 updates the fraud data, the publisher data, the application data and the rules. The data sharing platform 108 updates the data in the database 112 in real-time.

[0054] In an embodiment of the present disclosure, the data sharing platform 108 may distribute fraud data to bona-fide parties who will not use the data to circumvent the fraud platform (common practice of many fraud players is to use anti-fraud systems themselves and change their setup accordingly). In an embodiment of the present disclosure, the data sharing platform 108 checks if dissemination of fraud data to a new third party results in change of behavior/setup immediately and hence conclude that the new third party is not a bona-fide user of the fraud data.

[0055] FIG. 2 illustrates a block diagram of a computing device 200, in accordance with various embodiments of the present disclosure. The device 200 is a non-transitory computer readable storage medium. The device 200 includes a bus 202 that directly or indirectly couples the following devices: memory 204, one or more processors 206, one or more presentation components 208, one or more input/output (I/O) ports 210, one or more input/output components 212, and an illustrative power supply 214. The bus 202 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 2 are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. The inventors recognize that such is the nature of the art, and reiterate that the diagram of FIG. 2 is merely illustrative of an exemplary device 200 that can be used in connection with one or more embodiments of the present invention. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of FIG. 2 and reference to “computing device.”

[0056] The device 200 typically includes a variety of computer-readable media. The computer-readable media can be any available media that can be accessed by the device 200 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer storage media and communication media. The computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the device 200. The communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

[0057] Memory 204 includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory 204 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical-disc drives, etc. The device 200 includes the one or more processors 206 that read data from various entities such as memory 204 or I/O components 212. The one or more presentation components 208 present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc. The one or more I/O ports 210 allow the device 200 to be logically coupled to other devices including the one or more I/O components 212, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.

[0058] It may be noted that the foregoing description has been explained with help of one third party for the explanation purpose only. In an embodiment of the present disclosure, the interactive computing environment may contain any number of users being associated with any number of portable communication devices and any number of third parties present at an instance of time.

[0059] The foregoing descriptions of specific embodiments of the present technology have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present technology to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to explain the principles of the present technology best and its practical application, to thereby enable others skilled in the art to best utilize the present technology and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present technology.

[0060] While several possible embodiments of the invention have been described above and illustrated in some cases, it should be interpreted and understood as to have been presented only by way of illustration and example, but not by limitation. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments.
,CLAIMS:What is claimed is:

1. A computer system comprising:
one or more processors (206); and
a memory (204) coupled to the one or more processors (206), the memory (204) for storing instructions which, when executed by the one or more processors (206), cause the one or more processors (206) to perform a method for distribution of advertisement fraud data to one or more third parties (114) in real time, the method comprising:
receiving, at a data sharing platform (108), a request from the one or more third parties (114) to access fraud data, wherein the request comprises set of data and data associated with at least one publisher (106);
correlating, at the data sharing platform (108), data from the one or more third parties (114) and the fraud data, wherein the correlation is done after authorizing the one or more third parties (114) for accessing the fraud data, wherein the correlation is done in real time;
analyzing, at the data sharing platform (108), publisher data, application data and the fraud data collected after correlation, wherein analysis is done after optimizing rules for identification of fraud, wherein the analysis is done to identify the publisher (106) in the blacklist or whitelist; and
blocking, at the data sharing platform (108), the publisher (106) based on the analysis, wherein the blocking is done to stop the publisher (106) from publishing one or more advertisement on one or more media devices (104) by fraud and add the publisher (106) in the blacklist.

2. The computer system as recited in claim 1, wherein the fraud data comprises the blacklist and the whitelist, wherein the blacklist comprises the publisher (106) showing fraudulent activity, wherein the whitelist comprises the publisher (106) using genuine means for showing the one or more advertisement on the one or more media devices (104), wherein the fraud data represents the publisher (106) in the blacklist or whitelist by way of IP address and device Id’s.

3. The computer system as recited in claim 1, wherein the publisher data comprises number of click, past revenue generated by the publisher, number of transaction, time stamp, location of click, interaction data and number of install.

4. The computer system as recited in claim 1, wherein the application data comprises application size, time to download, time to run, redirection time, click to install, click to run, user click time, device load time, time to run, time to install, network download time, application usage time, application idle time and application opening time.

5. The computer system as recited in claim 1, wherein the rules are conditions specified by the one or more third parties (114) in order to list the publisher (106) as fraud or genuine, wherein the rules are specified by the one or more third parties with the connection request as the set of data.

6. The computer system as recited in claim 1, further comprising
optimizing, at the data sharing platform (108), the rules for the identification of fraud done by the publisher (106), wherein the optimizations is done based on the set of data received from the one or more third parties (114);

7. The computer system as recited in claim 1, further comprising
authorizing, at the data sharing platform (108), the publisher (106) based on the analysis, wherein the authorization is done to allow the publisher (106) to publish the one or more advertisement on the one or more media devices (104) and add the publisher (106) in the whitelist.

8. The computer system as recited in claim 1, further comprising
notifying, at the data sharing platform (108), the one or more third parties (114) about the publisher (106) using fraud means showing the one or more advertisements, wherein the notification is send by e-mail or message to the one or more third parties (114) in real time.

9. The computer system as recited in claim 1, further comprising
blocking, at the data sharing platform (108), payment to the publisher (106) from the one or more third parties (114) by integrating with the automatic payment network, wherein the blocking is done to stop the payment for the publisher (106) present in the blacklist of the fraud data.

10. The computer system as recited in claim 1, further comprising
generating, at the data sharing platform (108), report in a pre-defined interval of time, wherein the report comprises the publisher (106) who are using genuine or fraud means for the publishing of the one or more advertisement on the one or more media devices (104).