Description:BACKGROUND
Technical Field
[0001] The embodiments herein generally relate to building a new privacy framework for users and leveraging the existing protocols and a system to form a new internet backbone, more particularly, to a system and a method for securing the private data of an internet user while interacting with a third-party device for targeted digital advertisement.
Description of the Related Art
[0002] The World Wide Web (WWW) or the internet in short, was initially conceived as a fully decentralized ecosystem fostering peer-to-peer interactions. Over time, the internet has witnessed the emergence of numerous entities capitalizing on its potential. These entities have monetized the internet by offering diverse services such as search engines, communication applications, social media platforms, entertainment via connected devices, content management systems, mobile application development, e-commerce, digital transformation solutions, and a myriad of other use cases. Within the expansive internet landscape, the digital advertising ecosystem stands out as a significant avenue. This ecosystem comprises a vast network of entities capitalizing on user activities occurring on the internet. It encompasses three primary types of entities: internet users, publishers, and advertisers. Additionally, there exists a multitude of intermediate entities that provide various products and services, acting as intermediaries between publishers and advertisers. The current state of the digital advertising ecosystem is characterized by a cluttered landscape, prompting the necessity for a substantial overhaul. The challenges include the lack of user privacy, instances of data security breaches, fraudulent advertising practices, growing complexity, and general inefficiencies that have accumulated over time.
[0003] Various countries have implemented privacy laws to govern companies responsible for storing extensive user data, encompassing user demographics, clickstream, online transactions, location history, as well as shared text, images, or videos. These laws dictate that companies must handle such data in accordance with specified regulations. Fines are imposed on companies failing to appropriately manage user-sensitive data and failing to adhere to the stipulations outlined in privacy laws. Furthermore, substantial fines are imposed in cases of data breaches and mishandling, especially in instances where users are not promptly informed about data breaches.
[0004] New generation of frameworks is currently under development to address the prevailing issues related to data security and user privacy. Companies engaged in monetizing the internet are in a competitive race to implement patchwork solutions to their existing systems, struggling to meet the privacy standards mandated by government regulations. However, these endeavours are fraught with legal, operational, and strategic challenges, highlighting the inadequacy of mere incremental changes without a comprehensive overhaul of the digital advertising ecosystem. The root cause of inefficiencies in the internet infrastructure is notably straightforward. Initially, systems were constructed to centralize and monopolize data, often providing freemium services to users. Over time, these systems evolved to leverage and scale digital advertising for revenue generation. Unfortunately, users frequently neglect to read the fine print of Terms & Conditions (T&C) or Privacy contracts signed with publishers. Consequently, users unwittingly relinquished significant control over their data to these publishers, granting them broad rights for various use cases. This business model prioritized legal compliance and minimized operational costs over privacy and data security considerations. As privacy laws came into effect, companies now find themselves scrambling to patch their existing monolithic central systems. These systems are inherently susceptible to data security breaches and are incapable of adequately safeguarding user privacy. Notably, an increasing number of companies, especially large monopolistic entities handling substantial volumes of user data, are facing government fines due to privacy and security violations.
[0005] A Self-Sovereign Identity (SSI) system serves the purpose of authenticating and authorizing users, offering both Self-Sovereign Identity and temporary IDs. Password less Single Sign-On (SSO) options with several key entities, including Google, Facebook, Microsoft, and IBM, have already been introduced. However, a critical concern arises from the fact that these central entities are primarily focused on tracking the online activities of their registered users as they explore new websites, potentially compromising user privacy. These users become susceptible to cross-targeting through Single Sign-On (SSO) mechanisms.
[0006] The current prominent entities/companies in the market provide free technology for peer communication, simplifying global connectivity at no cost. While this technology has facilitated effortless connections worldwide, it has begun to exhibit significant drawbacks. Issues include the emergence of fraudulent actors exploiting digital channels to siphon users' money and an overwhelming influx of advertisements that divert individuals from their personal objectives. To sustain their operations, the entities eventually need to monetize user accounts, necessitating increased investments in cybersecurity. As users become accustomed or habituated to the initially free technology, they are gradually exposed to advertisements. This serves as the foundational strategy for the monetization of the entities. Despite the benefits users derive from connecting with peers and businesses, a notable drawback is the presentation of advertisements without the explicit permission of the end user. Over time, these unsolicited advertisements become a nuisance, with users having minimal control over their exposure to them. The advent of 'Non-Permissioned' central actors reaching out through channels, direct messaging, or advertisements becomes a significant source of irritation to users, disrupting their focus and attention in daily activities. Online fraud and a lack of privacy have become major pain points for users of technologies offered by central players. Users, particularly those in the older age groups, especially 65 and above, have fallen victim to various fraudsters exploiting communication channels to steal their money. The serious risk to user privacy and data arises as companies unabashedly store personal information for advancing their AI ambitions and learning from individuals' data. Another major drawback with centralized systems is that they can withdraw/suspend their free/paid service without prior notice or information for doing so causing inconvenience to people and organizations.
[0007] Further, storing a substantial volume of user private data in a centralized server creates a vulnerable target for potential hackers. Numerous large-scale data breaches have been witnessed globally, leading to governmental fines imposed on affected companies. Hence, there is a need for a system or method to overcome the aforementioned drawbacks in securing the privacy data.

SUMMARY
[0008] In view of the foregoing, an embodiment herein provides a processor-implemented method for securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement. The processor-implemented method includes (i) generating, at a user device associated with the user, (a) a digital identity for the user device associated with the user using a secure data vault model, and (b) a user database to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm, (ii) authenticating the user device, by the third-party device, using the digital identity of the user device by verifying one or more verifiable credentials of the user associated with the user device using a smart contract-based blockchain to access the third-party device, (iii) implementing, at the user device, a user consent process that enables the user to control the encrypted private data that has user consent to be shared with the third-party device through the smart contract-based blockchain, (iv) extracting, using the third-party device, data that has user consent from the encrypted private data without compromising the privacy of the user by interacting with an application programming interface (API) of the user device, (v) establishing, using the smart contract-based blockchain, a peer-to-peer communication between the user device and the third-party device after the user device is authenticated to enable the user to interact directly with the third-party device, (vi) implementing, using the smart contract-based blockchain, a privacy-preserving AI technique that categorizes the encrypted private data that is raw and assigns at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data comprising user interest and behavioural trait without infringing on the privacy of the users, and (vii) enabling a targeted digital advertisement on the user device through the smart contract-based blockchain by determining an advertisement availability based on the segmented privacy-preserved data that has user consent and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data.
[0009] The proposed method empowers internet users by entrusting them with complete control over the management of their privacy. It introduces indiscernible authentication and authorization technology, allowing users to seamlessly access the internet, and interact with websites, mobile applications, and connected devices, all while preserving their privacy. Users share only the minimum necessary information required for websites to engage with them and participate in digital advertising.
[0010] In some embodiments, the method includes processing, using the third-party device, one or more credentials of the user entered at the user device, and the one or more verifiable credentials of the user issued by an issuer received from a verifiable credential database for authenticating the user device.
[0011] In some embodiments, the method includes implementing at the user device, using the smart contract-based blockchain, a federated learning technique to perform federated learning on the encrypted private data post-user consent by assigning the user in a segment based on the statistical analysis of trait of the private data of the user.
[0012] In some embodiments, the method includes aggregating, at a central federated device, federated learning behaviour from the encrypted private data of one or more users and communicating the segmented privacy-preserved data to the user device.
[0013] In some embodiments, the method includes (i) storing the extracted data in the third-party device and defining a retention period for storage of extracted data in the third-party device, and (ii) verifying, using the smart contract-based blockchain, that the extracted data is only used by the third-party device. In some embodiments, the method includes transferring the private data to the user device through the API of the user device while the user interacts with the third-party device during a session. In some embodiments, the method includes requesting, using the smart contract-based blockchain, an advertiser device whether the advertiser is interested in advertising the user when the advertisement availability for the user is determined.
[0014] In some embodiments, the method includes computing, using the smart contract-based blockchain, measurement, and campaign insights on whether (i) the user device provides access to if the user is present in a store post-purchase or (ii) the user device transacts to buy a product or view or click the product online. In some embodiments, the method includes randomizing the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique with minimal error. In some embodiments, the digital identity of the user enables the storage of private data of the user on the user database associated with the user device. In some embodiments, the method includes providing, using the user device, access to the third-party device to access the secure data vault model for categorizing the private data of the user and for targeting the user with an advertisement.
[0015] In one aspect, a system for securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement is provided. The system includes a user device, a third-party device and a smart contract-based blockchain. The user device is associated with the user. The user device generates (i) a digital identity for the user device associated with the user using a secure data vault model, and (ii) a user database to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm. The third-party device authenticates the user device, using the digital identity of the user device by verifying one or more verifiable credentials of the user associated with the user device using a smart contract-based blockchain to access the third-party device. The smart contract-based blockchain platform includes a processor. The processor implements, at the user device, a user consent process that enables the user to control the encrypted private data that has user consent to be shared with the third-party device through the smart contract-based blockchain. The processor extracts, using the third-party device, data that has user consent from the encrypted private data without compromising the privacy of the user by interacting with an application programming interface (API) of the user device. The processor establishes, using the smart contract-based blockchain, a peer-to-peer communication between the user device and the third-party device after the user device is authenticated to enable the user to interact directly with the third-party device. The processor implements, using the smart contract-based blockchain, a privacy-preserving AI technique that categorizes the encrypted private data that is raw and assigns at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data including user interest and behavioural trait without infringing on the privacy of the users. The processor enables a targeted digital advertisement on the user device through the smart contract-based blockchain by determining an advertisement availability based on the segmented privacy-preserved data that has user consent and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data.
[0016] In some embodiments, the third-party device processes one or more credentials of the user entered at the user device and the one or more verifiable credentials of the user issued by an issuer received from a verifiable credential database for authenticating the user device.
[0017] In some embodiments, the processor implements at the user device, using the smart contract-based blockchain, a federated learning technique to perform federated learning on the encrypted private data post-user consent by assigning the user in a segment based on the statistical analysis of trait of the private data of the user.
[0018] In some embodiments, the system includes a central federated device that aggregates federated learning behaviour from the encrypted private data of one or more users and communicates the segmented privacy-preserved data to the user device. In some embodiments, the processor transfers the private data to the user device through the API of the user device while the user interacts with the third-party device during a session.
[0019] In some embodiments, the processor computes, using the smart contract-based blockchain, measurement, and campaign insights on whether (i) the user device provides access to if the user is present in a store post-purchase or (ii) the user device transacts to buy a product or view or click the product online. In some embodiments, the processor randomizes the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique with minimal error.
[0020] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0022] FIG. 1 illustrates a system for securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement according to an embodiment herein;
[0023] FIG. 2 illustrates a system of FIG. 1 that includes a central federated device that performs federated learning on the encrypted private data post-user consent using a federated learning technique according to an embodiment herein;
[0024] FIG. 3 illustrates a process of authenticating and authorizing a user using a digital identity of a user device associated with the user using the system of FIG. 1 according to an embodiment herein;
[0025] FIG. 4 illustrates a peer-to-peer communication between a user device and a third-party device after the user device is authenticated to enable a user to interact with the third-party device according to an embodiment herein;
[0026] FIG. 5 illustrates a process of securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement using the system of FIG. 1 according to an embodiment herein;
[0027] FIG. 6 illustrates a system architecture of the system of FIG. 1 that secures the private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement according to an embodiment herein;
[0028] FIG. 7 illustrates a user interface of the user device of FIG. 1 that implements a user consent process that enables a user to control his encrypted private data according to an embodiment herein;
[0029] FIGS. 8A and 8B are flow diagrams that illustrate a method of securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement according to an embodiment herein; and
[0030] FIG. 9 is a schematic diagram of a computer architecture in accordance with the embodiments herein.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0031] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0032] As mentioned, there remains a need for a method and a system for securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement. Various embodiments disclosed herein provide a method and a system for securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement. Referring now to the drawings, and more particularly to FIGS. 1 through 9, where similar reference characters denote corresponding features consistently throughout the figures, preferred embodiments are shown.
[0033] FIG. 1 illustrates a system for securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement according to an embodiment herein. The system includes a user device 102, a smart contract-based blockchain platform comprising a smart contract-based blockchain 104, and a third-party device 108. The user device 102 is associated with the user. The user device 102 generates a digital identity for the user device 102 associated with the user using a secure data vault model. The user device 102 generates a user database 106 to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm. In some embodiments, the digital identity of the user enables the storage of private data of the user on the user database 106 associated with the user device 102. The third-party device 108 authenticates the user device 102 using the digital identity of the user device 102 by verifying one or more verifiable credentials of the user associated with the user device 102 using a smart contract-based blockchain 104 to access the third-party device 108. In some embodiments, the third-party device 108 processes one or more credentials of the user entered at the user device 102 and the one or more verifiable credentials of the user issued by an issuer/credential issuer 112 received from a verifiable credential database for authenticating the user device 102.
[0034] The smart contract-based blockchain platform includes a processor. The processor implements a user consent process at the user device 102 which enables the user to control the encrypted private data that has user consent to be shared with the third-party device 108 through the smart contract-based blockchain 104. The processor extracts data that has user consent from the encrypted private data using the third-party device 108 without compromising the privacy of the user by interacting with an application programming interface (API) 110 of the user device 102. The processor establishes a peer-to-peer communication between the user device 102 and the third-party device 108 using the smart contract-based blockchain 104 after the user device 102 is authenticated to enable the user to interact directly with the third-party device 108. The processor implements a privacy-preserving AI technique 114 using the smart contract-based blockchain 104 to categorize the encrypted private data that is raw and assign at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data including user interest and behavioural trait without infringing on the privacy of the users. The processor enables a targeted digital advertisement on the user device 102 through the smart contract-based blockchain 104 by determining an advertisement availability based on the segmented privacy-preserved data that has user consent and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data. In some embodiments, the processor transfers the private data to the user device 102 through the API 110 of the user device 102 while the user interacts with the third-party device 108 during a session.
[0035] In some embodiments, the processor implements at the user device 102 a federated learning technique using the smart contract-based blockchain 104 to perform federated learning on the encrypted private data post-user consent by assigning the user in a segment based on the statistical analysis of trait of the private data of the user. In some embodiments, the system includes a central federated device that aggregates federated learning behaviour from the encrypted private data of one or more users and communicates the segmented privacy-preserved data to the user device 102.
[0036] In some embodiments, the processor computes, using the smart contract-based blockchain 104, measurement, and campaign insights on whether (i) the user device 102 provides access to if the user is present in a store post-purchase or (ii) the user device 102 transacts to buy a product or view or click the product online. In some embodiments, the processor randomizes the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique 114 with minimal error. The system may store the segmented privacy-preserved data including user interest and behavioural trait on a privacy-preserving database or privacy-preserving data management platform (PP-DMP) 116.
[0037] In some embodiments, the processor stores the extracted data in the third-party device 108 and defines a retention period for storage of extracted data in the third-party device 108. The processor verifies, using the smart contract-based blockchain 104, that the extracted data is only used by the third-party device 108. In some embodiments, the processor requests, using the smart contract-based blockchain 104, an advertiser device 118 whether the advertiser is interested in advertising the user when the advertisement availability for the user is determined. In some embodiments, the f provides, using the user device 102, access to the third-party device 108 to access the secure data vault model for categorizing the private data of the user and for targeting the user with an advertisement.
[0038] In some embodiments, the system provides a login interface at the user device 102 to enable the user to login into the third-party device 108 (e.g. publisher device). The smart contract-based blockchain platform a includes smart contract-based blockchain that may execute a smart contract between the user device 102 and the third-party device 108. The smart contract may receive an updated verifiable credentials from the user device 102. The smart contract-based blockchain 104 may interact with the third-party device 108 through API/SDK integration for transferring the verifiable credentials of the user to the third-party device 108. The smart contract-based blockchain 104 maps a temporary identifier to a device ID of the user device 102 to be targeted only with appropriate consent from the user. The advertiser device 118 may exchange an advertisement that the advertiser intends to advertise to the user and is relevant to the user interest to the third party device 108.
[0039] The proposed system empowers internet users by entrusting them with complete control over the management of their privacy. It introduces indiscernible authentication and authorization technology, allowing users to seamlessly access the internet, and interact with websites, mobile applications, and connected devices, all while preserving their privacy. Users share only the minimum necessary information required for websites to engage with them and participate in digital advertising.
[0040] FIG. 2 illustrates a system of FIG. 1 that includes a central federated device 204 that performs federated learning on the encrypted private data post-user consent using a federated learning technique according to an embodiment herein. The system includes one or more user devices 102A-N, the smart contract-based blockchain, the third-party device 108 and the smart contract-based blockchain 104. The user device 102A is associated with the user 202A. The functionalities of the system are described in FIG. 1. The central federated device 204 aggregates federated learning behaviour from the encrypted private data of one or more users 202A-N and communicates the segmented privacy-preserved data to the one or more user devices 102A-N.
[0041] The federated learning techniques on encrypted user data post-consent gain insights into three distinct categories of information about users, including demographics, psychographics, and behavioural aspects, while adhering to privacy compliance and guidelines. The systems incorporate a large-scale federated learning technique that learns from user-consented data. This high-level categorization ensures that users’ private data are appropriately categorized, leveraging feedback directly from the users 202A-N and trained without bias. The input to the federated learning technique includes user-approved raw data stored in the secure data vault model/raw Private Data Vault (PDV), and the output from the federated learning technique is stored in the Privacy-Preserving Data Management Platform 116 (PPDMP). The system ensures that the users 202A-N have visibility into both the raw data and the processed categories, accessible post user-consent.
[0042] The system employs the privacy-preserving AI technology that establishes connectivity between a user device 102A and an advertiser device 118 of an AdTech or market tech industry, offering high-level customer segmentation without compromising user data (i.e. user privacy data/private data). Currently, the raw user data is extensively captured during user interactions with the publisher/third-party device 108 and circulated within the system. The advertiser device relies heavily on effective user segmentation to target the users 202A-N with specific advertisements and measure campaign effectiveness on whether (i) the user device 102A-N provides access to if the user is present in a store post-purchase or (ii) the user device 102A-N transacts to buy a product or view or click the product online. Without user data/private data, targeted digital advertising is unattainable. In this system, the privacy-preserving AI technology functions as an intermediary layer between the user devices 102A-N and the publisher/third-party device 108, directing user segments into the advertising ecosystem while safeguarding user privacy.
[0043] The privacy-preserving AI technology facilitates the assignment of users 202A-N to different demographic, psychographic, and behavioural segments without compromising privacy/private data of the user (e.g. 202A). The user’s private data and its schema may remain at a high level and encrypted. The privacy-preserving AI primarily learns from historical data stored in the user's private/secure data vault. Various types of user private data persist, including purchase history, browsing history, geographic segments, application usage, daily or occasional activities, and travel history. Each private data of the user undergoes processing by a federated learning module of the system in a distributed mode, learning from the private data of the users. The system includes a central aggregator device that aggregates the learning behaviour of a cohort of users 202A-N. The user 202A can provide feedback on the accuracy of the created segments, and this information is fed back into the federated learning technique/system for further learning.
[0044] In some embodiments, the users 202A-N exhibit various attributes and interests based on online and offline activities. The system may include a differential privacy device that encrypts user’s private data, minimizing linkage attacks to prevent the re-identification of the user 202A. This is achieved by adding a small amount of statistical noise (e) to distort information, balancing noise (e) versus accuracy based on the use case. Additionally, Homomorphic Encryption, in conjunction with federated learning technique, enables learning from the encrypted private data of the users 202A-N. Homomorphic Encryption algorithms operate on ciphertext after encrypting user private data, equivalent to performing additions and multiplications on plaintext, with applications in Machine Learning.
[0045] The Privacy-Preserving Data Management Platform 116 (PP-DMP) uniquely hosts information that is privacy-proof, rendering it unusable without user permission and ensuring personalization when required. The identifier within the PP-DMP is temporary and undergoes refreshing at predetermined intervals. Decoding the mapping from the temporary identifier to a device ID is only permitted with explicit consent from the user. This approach ensures adherence to the three core principles of Privacy, Permission, and Personalization. While there exists a central database housing information linked to user identifiers/digital identity, the decoding of this digital identity is decentralized and requires approval when the user 102A-N is actively browsing through a user device 102A. The PP-DMP hosts user information without the ability to re-identify individuals based on the stored data and the expected properties include 1) a temporary ID linking the user to the segmented privacy-preserved data, b) maintaining the segmented privacy-preserved data at a high level and storing in distinct databases, 3) exporting, using a DMP, a set of IDs to specified channels based on chosen segmentation or characteristics, and 4) governing resolution of the temporary ID by robust cybersecurity principles.
[0046] FIG. 3 illustrates a process of authenticating and authorizing a user 202A using a digital identity of a user device 102 associated with the user 202A using the system of FIG. 1 according to an embodiment herein. The user device 102 is associated with the user 202A. The user device 102 generates a digital identity for the user device 102 associated with the user 202A using a secure data vault model 302. In some embodiments, the digital identity of the user 202A enables the storage of private data of the user 202A on the user database 106 associated with the user device 102. The third-party device 108 authenticates the user device 102, using the digital identity of the user device 102 by verifying one or more verifiable credentials of the user 202A associated with the user device 102 using a smart contract-based blockchain to access the third-party device 108. In some embodiments, the third-party device 108 processes one or more credentials of the user 202A entered at the user device 102 and the one or more verifiable credentials of the user 202A issued by an issuer/credential issuer 112 received from a verifiable credential database for authenticating the user device 102. The credential issuer 112 may be government agencies, private IDP’s or institutions.
[0047] The system provides maximum privacy and controls to be handed over to the user 202A to manage his private data as opposed to monetizing the user’s private data for advertising and other applications. Once the user 202A has full control over his/her private data that is generated while interacting with the third-party device 108, the system transfers back the user’s private data to the user device 102. This system ensures that the user’s private data is transferred back to the user device 102 by executing an agreement using a smart contract-based blockchain with the third-party/publisher device 108 where all data generated by the user activity on the third-party/publisher website is shared with the user device 102 and the user 202A is the sole owner of that private data. With the digital identity along with a user consent process implemented on the user device 102, the system may generate an agreement between the user device 102 and the third-party/publisher device to mutually agree that any data generated by the user 202A at their website is owned solely by the user device 102. The system provides an option to the user 202A to store his private data on the user device 102 or in a secure Data Vault model 302. Since the user 202A has full control over the private data, the system may enable the user to monetize the data without losing the privacy using the Privacy-Preserving AI technique which segments/categorizes the user’s private data and shares the user interest / behavioural traits by federated learning from the encrypted private data.
[0048] In some embodiments, the private/secure data vault model 302 is a database where all the private data generated by the user 202A is stored and fully controlled by the user 202A using the user device 102. The interactive private data generated by the user 202A while interacting with the third-party device/publisher device 108 is shared based on the mutual consent established between the user device 102 and the third-party device/publisher device 108 through a smart contract-based blockchain. The user-generated private data may be stored on the user device 102, user-controlled cloud accounts or a decentralized cloud storage such as an Interplenary file system. The private data that is in-transit may be encrypted through SSL and the private data at rest may be encrypted with the latest cryptographic asymmetric keys. The system may categorize the private data and store it in a format that may be leveraged when analysing the history of transactions or interactions with products, places, and people. The system may enable the user 202A to choose a type of format such as JSON, XML, or YAML format to collect the private data and may store the private data in various data collection formats such as map, tuple, list and heterogeneous structures.
[0049] With the user 202A volunteering to be targeted with information or specific advertisements or discounts, the system may choose the format so as to personalize the products based on AI decisions using the federated learning technique. In the system that leverages the smart contract-based blockchain, the user-generated private data is stored by the user 202A in the user device 102 or in the private secure data vault model 302 and secured with different technologies and encryption keys, making it harder for hackers to hack one account as opposed to large volumes. The private secure data vault model 302 enables user-controlled consent mechanisms with which the user 202A may share their private data as requested by the third-party device/publisher device 108 for interaction. The user consent mechanism may be built within the user device 102 or as a part of the “Privacy secure data vault model” as opposed to integrating with the third-party device 108. That may ensure transparency for the user about their private data. The private secure data vault model 302 may be a replacement to the traditional Data Management Platforms owned by advertisement technology companies limiting misuse or breaches.
[0050] The system may include a decentralized identity provider (IDP) that stores the verifiable credentials (VC) and provides the verifiable credentials to APIs to authenticate the user 202A. This ensures limited private data is received from the user device 102 while authenticating the user 202A. The private data received is owned by the user device 102 and it is authenticated by the system using cryptic solving such as the use of Zero-Knowledge Proof.
[0051] FIG. 4 illustrates a peer-to-peer communication between a user device and a third-party device after the user device is authenticated to enable a user to interact with the third-party device according to an embodiment herein. Systems that are in use today employs Publish/Subscribe mechanism with a central server or similar client-server architecture. The current decentralized peer-to-peer networking or file sharing protocols e.g. Gnutella, XMPP etc. ensures the communication can work seamlessly on decentralized platforms including for the purposes of exchanging messages, audio, images and video. With such an underlying communication protocol, when a user visits a website of the third-party device using the user device, the initial authentication occurs through digital identity/Self-Sovereign Identity (SSI) of the user device for securing the user privacy. The user device and the third-party device are now connected directly through the Peer-to-Peer Protocol. Following successful authentication, the website of the third-party device engages with the APIs of the user device to request additional information for targeting advertisement and provides options such as accessing existing or default schemas and requesting additions or modifications to new schemas. Importantly, the user device retains control over schema and data deletion. Bidirectional data flow between the user device and the third-party device/publisher device adheres to mutually agreed-upon terms and conditions for sharing the user’s private data.
[0052] Alternatively, the system includes a decentralized application for communication, encompassing mail, messaging, video/audio sharing individually or across channels. The system ensures permission marketing on the decentralized application, wherein user consent regulates widespread marketing efforts. The initial decentralized application interactions occur through a smart contract-based blockchain, which transits to Peer-to-Peer communication upon establishing a relationship. The system may enable the users to selectively choose peers and businesses for interaction, specifying the medium and extent of engagement. That is, no entity can contact a user without a predefined contract/agreement. The system employs smart contracts within decentralization to encapsulate relationship details, including initiation, duration, and termination. The system may enable the user to evaluate credibility through online or offline means before extending relationships to online decentralized application, adding a relationship linkage post-mutual consent. This streamlined process of the system optimizes user attention and mitigates fraudulent activities on various communication platforms.
[0053] FIG. 5 illustrates a process of securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement using the system of FIG. 1 according to an embodiment herein. The system includes a user device 102, a smart contract-based blockchain platform comprising a smart contract-based blockchain 104, and a third-party device 108. At step 502, the system includes a user database to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm. At step 504, the user logs into a website of the third-party device 108. At step 506, the credentials of the user are verified when the user logs into the website of the third-party device 108 using his credentials. At step 508, the third-party device 108 authenticates the user device 102 using the digital identity of the user device 102 by verifying one or more verifiable credentials of the user associated with the user device 102, from the holder 510 of the verifiable credentials, using a smart contract-based blockchain 104 to access the third-party device 108. In some embodiments, the third-party device 108 processes one or more credentials of the user entered at the user device 102 and the one or more verifiable credentials of the user issued by an issuer/credential issuer 112 received from a verifiable credential database for authenticating the user device 102.
[0054] At step 512, the smart contract-based blockchain platform 104 implements a user consent process at the user device 102 which enables the user to control the encrypted private data that has user consent to be shared with the third-party device 108 through the smart contract-based blockchain 104. At step 514, the system extracts data that has user consent from the encrypted private data using the third-party device 108 without compromising the privacy of the user by interacting with an application programming interface (API) 110 of the user device 102.
[0055] At step 516, the system establishes a peer-to-peer communication between the user device 102 and the third-party device 108 using the smart contract-based blockchain 104 after the user device 102 is authenticated to enable the user to interact directly with the third-party device 108. At step 518, the system executes an agreement/smart contract between the user device and the third-party/publisher device to mutually agree that any data generated by the user at their website is owned solely by the user device. At step 520, the system transfers the private data to the user device 102 through the API 110 of the user device 102 while the user interacts with the third-party device 108 during a session.
[0056] At step 522, the system implements a privacy-preserving AI technique 114 using the smart contract-based blockchain 104 to categorize the encrypted private data that is raw and assign at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data including user interest and behavioural trait without infringing on the privacy of the users. At step 524, the system enables a targeted digital advertisement on the user device through the smart contract-based blockchain by determining an advertisement availability based on the segmented privacy-preserved data that has user consent at a step 526 and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data from a step 528.
[0057] At step 530, the system computes, using the smart contract-based blockchain 104, measurement, and campaign insights on whether (i) the user device 102 provides access to if the user is present in a store post-purchase or (ii) the user device 102 transacts to buy a product or view or click the product online. In some embodiments, the system randomizes the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique 114 with minimal error. The system may store the segmented privacy-preserved data including user interest and behavioural trait on the privacy preserving database or PP-DMP 116. In some embodiments, the system stores the extracted data in the third-party device 108 and defines a retention period for storage of extracted data in third-party device 108. The system verifies, using the smart contract-based blockchain 104, that the extracted data is only used by the third-party device 108.
[0058] At step 532, the system requests, using the smart contract-based blockchain 104, an advertiser device 118 whether the advertiser is interested in advertising the user when the advertisement availability for the user is determined. In some embodiments, the system provides, using the user device 102, access to the third-party device 108 to access the secure data vault model for categorizing the private data of the user and for targeting the user with an advertisement.
[0059] FIG. 6 illustrates a system architecture of the system of FIG. 1 that secures the private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement according to an embodiment herein. The system includes a user device 102, a smart contract-based blockchain platform comprising a smart contract-based blockchain 104 (e.g. decentralized public blockchain), and a third-party device 108. The user device 102 is associated with the user. The user device 102 generates a digital identity for the user device 102 associated with the user using a secure data vault model 602 (e.g. user owned private data vault). The user device 102 generates a user database 106 to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm. In some embodiments, the digital identity of the user enables the storage of private data of the user on the user database 106 associated with the user device 102. The third-party device 108 authenticates the user device 102 using the digital identity of the user device 102 by verifying one or more verifiable credentials of the user associated with the user device 102 using a smart contract-based blockchain 104 to access the third-party device 108. In some embodiments, the third-party device 108 processes one or more credentials of the user entered at the user device 102 and the one or more verifiable credentials of the user issued by an issuer/credential issuer 112 received from a verifiable credential database for authenticating the user device 102.
[0060] The smart contract-based blockchain platform implements a user consent process at the user device 102 which enables the user to control the encrypted private data that has user consent to be shared with the third-party device 108 through the smart contract-based blockchain 104. The smart contract-based blockchain platform extracts data that has user consent from the encrypted private data using the third-party device 108 without compromising the privacy of the user by interacting with an application programming interface (API) 110 of the user device 102. The smart contract-based blockchain platform establishes a peer-to-peer communication between the user device 102 and the third-party device 108 using the smart contract-based blockchain 104 after the user device 102 is authenticated to enable the user to interact directly with the third-party device 108. The smart contract-based blockchain platform implements a privacy-preserving AI technique/platform 114 using the smart contract-based blockchain 104 to categorize the encrypted private data that is raw and assign at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data including user interest and behavioural trait without infringing on the privacy of the users. The smart contract-based blockchain platform enables a targeted digital advertisement on the user device 102 through the smart contract-based blockchain 104 by determining an advertisement availability based on the segmented privacy-preserved data that has user consent and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data. In some embodiments, the smart contract-based blockchain platform transfers the private data to the user device 102 through the API 110 of the user device 102 while the user interacts with the third-party device 108 during a session.
[0061] In some embodiments, the smart contract-based blockchain platform implements at the user device 102 a federated learning technique using the smart contract-based blockchain 104 to perform federated learning on the encrypted private data post-user consent by assigning the user in a segment based on the statistical analysis of trait of the private data of the user. Additionally, Homomorphic Encryption, in conjunction with federated learning technique, enables learning from the encrypted private data of the users. The Homomorphic Encryption algorithms operate on ciphertext after encrypting user private data, equivalent to performing additions and multiplications on plaintext, with applications in Machine Learning. In some embodiments, the system includes a central federated device that aggregates federated learning behaviour from the encrypted private data of one or more users and communicates the segmented privacy-preserved data to the user device 102. The system may include a differential privacy device that encrypts user’s private data, minimizing linkage attacks to prevent the re-identification of the user.
[0062] Alternatively, the system includes a decentralized application (e.g. communication applications) for communication, encompassing mail, messaging, video/audio sharing individually or across channels. The system ensures permission marketing on the decentralized application, wherein user consent regulates widespread marketing efforts. The initial decentralized application interactions occur through the smart contract-based blockchain 104, which transits to Peer-to-Peer communication upon establishing a relationship. The system may enable the users to selectively choose peers and businesses for interaction, specifying the medium and extent of engagement. That is, no entity can contact a user without a predefined contract/agreement. The system employs smart contracts within decentralization to encapsulate relationship details, including initiation, duration, and termination. The system may enable the user to evaluate credibility through online or offline means before extending relationships to online decentralized application, adding a relationship linkage post-mutual consent. This streamlined process of the system optimizes user attention and mitigates fraudulent activities on various communication platforms.
[0063] In some embodiments, the smart contract-based blockchain platform computes, using the smart contract-based blockchain 104, measurement, and campaign insights on whether (i) the user device 102 provides access to if the user is present in a store post-purchase or (ii) the user device 102 transacts to buy a product or view or click the product online. In some embodiments, the smart contract-based blockchain platform randomizes the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique 114 with minimal error. The system may store the segmented privacy-preserved data including user interest and behavioural trait privacy-preserving database or privacy-preserving data management platform (PP-DMP) 116.
[0064] In some embodiments, the smart contract-based blockchain platform stores the extracted data in the third-party device 108 and defines a retention period for storage of extracted data in the third-party device 108. The smart contract-based blockchain platform verifies, using the smart contract-based blockchain 104, that the extracted data is only used by the third-party device 108. In some embodiments, the smart contract-based blockchain platform requests, using the smart contract-based blockchain 104, an advertiser device 118 whether the advertiser is interested in advertising the user when the advertisement availability for the user is determined. In some embodiments, the smart contract-based blockchain platform provides, using the user device 102, access to the third-party device 108 to access the secure data vault model 602 for categorizing the private data of the user and for targeting the user with an advertisement.
[0065] In some embodiments, the system provides a login interface at the user device 102 to enable the user to login into the third-party device 108 (e.g. publisher device). The smart contract-based blockchain platform 104 may execute a smart contract between the user device and the third-party device 108. The smart contract may receive an updated verifiable credentials from the user device 102. The smart contract-based blockchain platform 104 may interact with the third-party device through API/SDK integration for transferring the verifiable credentials of the user to the third-party device 108. The smart contract-based blockchain platform 104 maps a temporary identifier to a device ID of the user device 102 to be targeted only with appropriate consent from the user. The advertiser device 118 may exchange an advertisement that the advertiser intends to advertise to the user and is relevant to the user interest to the third party device 108.
[0066] FIG. 7 illustrates a user interface 702 of the user device 102 of FIG. 1 that implements a user consent process that enables a user to control his encrypted private data according to an embodiment herein. The user interface 702 enables the user to select a publisher/third-party device 108 (e.g. publisher device) to interact with. The user interface 702 provides an option to the user to select data related at least one of demographic, psychographic or behavioural segment data from the encrypted private data of the user data to share it with the third-party device 108 for targeted digital advertisement.
[0067] FIGS. 8A and 8B are flow diagrams that illustrate a method for securing private data of a user by enabling the user to control the private data to be shared with a third-party device for targeted digital advertisement according to an embodiment herein. At a step 802, a user device associated with the user generates (i) a digital identity for the user device associated with the user using a secure data vault model, and (ii) a user database to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm. At a step 804, the user device is authenticated by the third-party device, using the digital identity of the user device by verifying one or more verifiable credentials of the user associated with the user device using a smart contract-based blockchain to access the third-party device. At a step 806, a user consent process is implemented, at the user device, that enables the user to control the encrypted private data that has user consent to be shared with the third-party device through the smart contract-based blockchain. At a step 808, the third-party device extracts data that has user consent from the encrypted private data without compromising the privacy of the user by interacting with an application programming interface (API) of the user device. At a step 810, the smart contract-based blockchain establishes a peer-to-peer communication between the user device and the third-party device after the user device is authenticated to enable the user to interact directly with the third-party device. At a step 812, the smart contract-based blockchain implements a privacy-preserving AI technique that categorizes the encrypted private data that is raw and assigns at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data comprising user interest and behavioural trait without infringing on the privacy of the users. At a step 814, a targeted digital advertisement is enabled on the user device through the smart contract-based blockchain by determining an advertisement availability based on the segmented privacy-preserved data that has user consent and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data.
[0068] In some embodiments, the method includes processing, using the third-party device, one or more credentials of the user entered at the user device and the one or more verifiable credentials of the user issued by an issuer received from a verifiable credential database for authenticating the user device. In some embodiments, the method includes implementing at the user device, using the smart contract-based blockchain, a federated learning technique to perform federated learning on the encrypted private data post-user consent by assigning the user in a segment based on the statistical analysis of trait of the private data of the user. In some embodiments, the method includes aggregating, at a central federated device, federated learning behaviour from the encrypted private data of one or more users and communicating the segmented privacy-preserved data to the user device. In some embodiments, the method includes (i) storing the extracted data in the third-party device and defining a retention period for storage of extracted data in the third-party device, and (ii) verifying, using the smart contract-based blockchain, that the extracted data is only used by the third-party device.
[0069] In some embodiments, the method includes transferring the private data to the user device through the API of the user device while the user interacts with the third-party device during a session. In some embodiments, the method includes requesting, using the smart contract-based blockchain, an advertiser device whether the advertiser is interested in advertising the user when the advertisement availability for the user is determined. In some embodiments, the method includes computing, using the smart contract-based blockchain, measurement, and campaign insights on whether (i) the user device provides access to if the user is present in a store post-purchase or (ii) the user device transacts to buy a product or view or click the product online.
[0070] In some embodiments, the method includes randomizing the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique with minimal error. In some embodiments, the digital identity of the user enables the storage of private data of the user on the user database associated with the user device. In some embodiments, the method includes providing, using the user device, access to the third-party device to access the secure data vault model for categorizing the private data of the user and for targeting the user with an advertisement.
[0071] A representative hardware environment for practicing the embodiments herein is depicted in FIG. 9, with reference to FIGS. 1 through 8B. This schematic drawing illustrates a hardware configuration of an authentication system/a server/computer system/computing device in accordance with the embodiments herein. The system includes at least one processing device CPU 10 that may be interconnected via system bus 14 to various devices such as a random access memory (RAM) 12, read-only memory (ROM) 16, and an input/output (I/O) adapter 18. The I/O adapter 18 can connect to peripheral devices, such as disk units 38 and program storage devices 40 that are readable by the system. The system can read the inventive instructions on the program storage devices 40 and follow these instructions to execute the methodology of the embodiments herein. The system further includes a subject interface adapter 22 that connects a keyboard 28, mouse 30, speaker 32, microphone 34, and/or other subject interface devices such as a touch screen device (not shown) to the bus 14 to gather subject input. Additionally, a communication adapter 20 connects the bus 14 to a data processing network 42, and a display adapter 24 connects the bus 14 to a display device 26, which provides a graphical subject interface (GUI) 36 of the output data in accordance with the embodiments herein, or which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
[0072] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of appended claims.
, Claims:I/We claim:
1. A processor-implemented method for securing private data of a user (202A) by enabling the user to control the private data to be shared with a third-party device (108) for targeted digital advertisement, comprising:
generating, at a user device (102) associated with the user (202A), (i) a digital identity for the user device (102) associated with the user using a secure data vault model (302), and (ii) a user database (106) to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm;
authenticating the user device, by the third-party device, using the digital identity of the user device by verifying one or more verifiable credentials of the user associated with the user device using a smart contract-based blockchain (104) to access the third-party device;
implementing, at the user device, a user consent process that enables the user to control the encrypted private data that has user consent to be shared with the third-party device through the smart contract-based blockchain;
extracting, using the third-party device, data that has user consent from the encrypted private data without compromising the privacy of the user by interacting with an application programming interface (API) (110) of the user device;
establishing, using the smart contract-based blockchain, a peer-to-peer communication between the user device and the third-party device after the user device is authenticated to enable the user to interact directly with the third-party device;
implementing, using the smart contract-based blockchain, a privacy-preserving AI technique (114) that categorizes the encrypted private data that is raw and assigns at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data comprising user interest and behavioural trait without infringing on the privacy of the users; and
enabling a targeted digital advertisement on the user device through the smart contract-based blockchain by determining an advertisement availability based on the segmented privacy-preserved data that has user consent and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data.

2. The processor-implemented method as claimed in claim 1, wherein the method comprises processing, using the third-party device, one or more credentials of the user entered at the user device and the one or more verifiable credentials of the user issued by an issuer received from a verifiable credential database for authenticating the user device.

3. The processor-implemented method as claimed in claim 1, wherein the method comprises implementing at the user device, using the smart contract-based blockchain, a federated learning technique to perform federated learning on the encrypted private data post-user consent by assigning the user in a segment based on the statistical analysis of trait of the private data of the user.

4. The processor-implemented method as claimed in claim 3, wherein the method comprises aggregating, at a central federated device, federated learning behaviour from the encrypted private data of one or more users and communicating the segmented privacy-preserved data to the user device.

5. The processor-implemented method as claimed in claim 1, wherein the method comprises
storing the extracted data in the third-party device and defining a retention period for storage of extracted data in third-party device; and
verifying, using the smart contract-based blockchain, that the extracted data is only used by the third-party device.

6. The processor-implemented method as claimed in claim 1, wherein the method comprises transferring the private data to the user device through the API of the user device while the user interacts with the third-party device during a session.

7. The processor-implemented method as claimed in claim 1, wherein the method comprises requesting, using the smart contract-based blockchain, an advertiser device whether the advertiser is interested in advertising the user when the advertisement availability for the user is determined.

8. The processor-implemented method as claimed in claim 1, wherein the method comprises computing, using the smart contract-based blockchain, measurement, and campaign insights on whether (i) the user device provides access to if the user is present in a store post-purchase or (ii) the user device transacts to buy a product or view or click the product online.

9. The processor-implemented method as claimed in claim 1, wherein the method comprises randomizing the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique with minimal error.

10. The processor-implemented method as claimed in claim 1, wherein the digital identity of the user enables the storage of private data of the user on the user database associated with the user device.

11. The processor-implemented method as claimed in claim 1, wherein the method comprises providing, using the user device, access to the third-party device to access the secure data vault model for categorizing the private data of the user and for targeting the user with an advertisement.

12. A system for securing private data of a user by enabling the user (202A) to control the private data to be shared with a third-party device (108) for targeted digital advertisement, comprising:
a user device (102) that is associated with the user (202A), wherein the user device generates (i) a digital identity for the user device associated with the user using a secure data vault model (302), and (ii) a user database (106) to store the private data including personally identifiable information of the user that is encrypted using a cryptographic algorithm;
a third-party device that authenticates the user device, using the digital identity of the user device by verifying one or more verifiable credentials of the user associated with the user device using a smart contract-based blockchain (104) to access the third-party device;
a smart contract-based blockchain platform that comprises a processor that
implements, at the user device, a user consent process that enables the user to control the encrypted private data that has user consent to be shared with the third-party device through the smart contract-based blockchain; and
extracts, using the third-party device, data that has user consent from the encrypted private data without compromising the privacy of the user by interacting with an application programming interface (API) (110) of the user device;
establishes, using the smart contract-based blockchain, a peer-to-peer communication between the user device and the third-party device after the user device is authenticated to enable the user to interact directly with the third-party device;
implements, using the smart contract-based blockchain, a privacy-preserving AI technique (114) that categorizes the encrypted private data that is raw and assigns at least one of demographic, psychographic or behavioural segment to the encrypted private data of the user based on statistics to generate segmented privacy-preserved data comprising user interest and behavioural trait without infringing on the privacy of the users; and
enables a targeted digital advertisement on the user device through the smart contract-based blockchain by determining an advertisement availability based on the segmented privacy-preserved data that has user consent and determining an advertisement that an advertiser intends to advertise to the user and is relevant to the user by analysing the extracted data.

13. The system as claimed in claim 12, wherein the third-party device processes one or more credentials of the user entered at the user device and the one or more verifiable credentials of the user issued by an issuer received from a verifiable credential database for authenticating the user device.

14. The system as claimed in claim 12, wherein the processor implements at the user device, using the smart contract-based blockchain, a federated learning technique to perform federated learning on the encrypted private data post-user consent by assigning the user in a segment based on the statistical analysis of trait of the private data of the user.

15. The system as claimed in claim 12, wherein the system comprises a central federated device that aggregates federated learning behaviour from the encrypted private data of one or more users and communicates the segmented privacy-preserved data to the user device.

16. The system as claimed in claim 12, wherein the processor transfers the private data to the user device through the API of the user device while the user interacts with the third-party device during a session.

17. The system as claimed in claim 12, wherein the processor computes, using the smart contract-based blockchain, measurement, and campaign insights on whether (i) the user device provides access to if the user is present in a store post-purchase or (ii) the user device transacts to buy a product or view or click the product online.

18. The system as claimed in claim 12, wherein the processor randomizes the categorization of the segmented privacy-preserved data post-analysis by the privacy-preserving AI technique with minimal error.

Dated this November 22nd 2023

Arjun Karthik Bala (IN/PA 1021)
Agent for Applicant