Claims:1. A method of categorization of data users and an optimization thereof to authenticate data transactions on a data market platform, the method comprising a processor implemented steps of:
defining, by one or more hardware processors, a plurality of data users operating on the data market platform (301);
capturing, by one or more data market applications, a first set of information corresponding to each of the plurality of data users defined, wherein the first set of information comprises historical transactional data of each of the plurality of data users (302);
extracting, from the first set of information, a second set of information using a data collecting module, wherein the second set of information comprises historical transactional data corresponding to one or more selected data users amongst the plurality of data users, and wherein the one or more selected data users comprise users initiating one or more data transactions on the data market platform (303);
performing, using the second set of information, a plurality of steps, wherein the plurality of steps comprise (304):
(i) identifying, based upon a correlation of the second set of information and a predefined set of information, one or more data violations performed by the one or more selected data users (304(i));
(ii) generating a third set of information comprising data violations details upon identifying the one or more data violations performed by the one or more selected data users (304(ii)); and
(iii) optimizing the categorization of the one or more selected data users into one or more predefined user categories upon identifying the one or more data violations, wherein the optimization comprises (304(iii)):
(a) analyzing, from the one or more data violations identified, whether the one or more selected data users intentionally or unintentionally performed the one or more data violations (304(iii)(a));
(b) determining, from the one or more data violations identified and the third set of information generated, a total number of additional data users performing one or more historical data transactions with the one or more selected data users (304(iii)(b)); and
(c) categorizing, based upon the one or more data violations performed intentionally or unintentionally, the total number of additional data users determined, and a pre-estimated value corresponding to the one or more data violations, the one or more selected data users into the one or more predefined user categories via a user grouping module (304(iii)(c)); and
authorizing, based upon the categorization, the one or more selected data users to perform the one or more data transactions on the data market platform (305).

2. The method of claim 1, wherein the step of optimizing the categorization is preceded by identifying, based upon the first set of information, the one or more predefined user categories for authorizing the one or more selected data users to perform the one or more data transactions on the data market platform.

3. The method of claim 1, wherein the step of optimizing facilitates auto-generating, based upon the categorization, a set of recommendations on data usage behavior and feedback of the one or more selected data users.

4. A system (100) for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform (201), the system (100) comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
define a plurality of data users operating on the data market platform (201);
capture, by one or more data market applications (202), a first set of information corresponding to each of the plurality of data users defined, wherein the first set of information comprises historical transactional data of each of the plurality of data users;
extract, from the first set of information, a second set of information using a data collecting module (203), wherein the second set of information comprises historical transactional data corresponding to one or more selected data users amongst the plurality of data users, and wherein the one or more selected data users comprise users initiating one or more data transactions on the data market platform (201);
perform, using the second set of information, a plurality of steps, wherein the plurality of steps comprise:
(i) identify, based upon a correlation of the second set of information and a predefined set of information, one or more data violations performed by the one or more selected data users;
(ii) generate a third set of information comprising data violations details upon identifying the one or more data violations performed by the one or more selected data users; and
(iii) optimize the categorization of the one or more selected data users into one or more predefined user categories upon identifying the one or more data violations, wherein the optimization comprises:
(i) analyze, from the one or more data violations identified, whether the one or more selected data users intentionally or unintentionally performed the one or more data violations;
(ii) determine, from the one or more data violations identified and the third set of information generated, a total number of additional data users performing one or more historical data transactions with the one or more selected data users; and
(iii) categorize, based upon the one or more data violations performed intentionally or unintentionally, the total number of additional data users determined, and a pre-estimated value corresponding to the one or more data violations, the one or more selected data users into the one or more predefined user categories via a user grouping module (205); and
authorize, based upon the categorization, the one or more selected data users to perform the one or more data transactions on the data market platform (201).

5. The system (100) of claim 4, wherein the one or more hardware processors (104) are configured to optimize the categorization by identifying, based upon the first set of information, the one or more predefined user categories for authorizing the one or more selected data users to perform the one or more data transactions on the data market platform (201).

6. The system (100) of claim 5, wherein the one or more hardware processors (104) are configured to auto-generate, based upon the categorization, a set of recommendations on data usage behavior and feedback of the one or more selected data users.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

CATEGORIZATION OF DATA USERS AND OPTIMIZATION THEREOF TO AUTHENTICATE DATA TRANSACTIONS ON DATA MARKET PLATFORM

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.


TECHNICAL FIELD
The disclosure herein generally relates to categorization of data users and an optimization thereof to authenticate data transactions on a data market platform, and, more particularly, to systems and methods for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform.

BACKGROUND
Trading data as a commodity has become increasingly popular in recent years, and data marketplaces have emerged as a new business model where data from a variety of sources can be collected, processed, enriched, bought, and sold. They are effectively changing the way data is distributed and managed on the Internet. Data have long been shared and traded: for example, academics share research data and businesses share household credit data. In recent years, much of the data being traded are exhaust data, created as a by-product of other activities such as online shopping or socializing, rather than specifically created for an analytical purpose
A data marketplace is an online venue where users can buy and sell data. With the growth of big data, which consists of large and complex data sets, structured or unstructured, the data marketplaces have proliferated in growth. A data market platform which offers a variety of data may be accessed by a wide variety of large number of users at a time which may often lead to security compromise in one way or the other. Conceptually, data marketplaces are multi-sided platforms, where a digital intermediary connects data providers, data purchasers, and other complementary technology providers. Data market platforms would, in principle, generate value for both data buyers and sellers through enhanced market efficiency, resource allocation efficiency, and an improved match between supply and demand.
Third parties can offer value-added solutions on top of the data the marketplace offers. For example, real-time analytics can make consumer insights more actionable and timelier than ever before. The marketplace also has an exchange platform as a technical base for the exchange of data and services, including platform-as-a-service offers. Finally, the data marketplace offers organizations an innovative way to turn some of that data into cash and reap the benefits that will accrue from building a self-reinforcing ecosystem, enabling crowdsourcing, supporting interoperability, satisfying customer data needs, and improving data quality.
Data security is paramount in a data marketplace for secured verification of identities or users information in order to facilitate secured buying, selling or exchange of data. The data owners must be able to securely share their data without compromising the confidential information of individuals. The data suppliers must take the onus for legally auditing and enforcing the data licenses as the data marketplace supports creation and definition of data licenses.

SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform is provided, the method comprising: defining, by one or more hardware processors, a plurality of data users operating on the data market platform; capturing, by one or more data market applications, a first set of information corresponding to each of the plurality of data users defined, wherein the first set of information comprises historical transactional data of each of the plurality of data users; extracting, from the first set of information, a second set of information using a data collecting module, wherein the second set of information comprises historical transactional data corresponding to one or more selected data users amongst the plurality of data users, and wherein the one or more selected data users comprise users initiating one or more data transactions on the data market platform; performing, using the second set of information, a plurality of steps, wherein the plurality of steps comprise: (i) identifying, based upon a correlation of the second set of information and a predefined set of information, one or more data violations performed by the one or more selected data users; (ii) generating a third set of information comprising data violations details upon identifying the one or more data violations performed by the one or more selected data users; and (iii) optimizing the categorization of the one or more selected data users into one or more predefined user categories upon identifying the one or more data violations, wherein the optimization comprises: (a) analyzing, from the one or more data violations identified, whether the one or more selected data users intentionally or unintentionally performed the one or more data violations; (b) determining, from the one or more data violations identified and the third set of information generated, a total number of additional data users performing one or more historical data transactions with the one or more selected data users; and (c) categorizing, based upon the one or more data violations performed intentionally or unintentionally, the total number of additional data users determined, and a pre-estimated value corresponding to the one or more data violations, the one or more selected data users into the one or more predefined user categories via a user grouping module; authorizing, based upon the categorization, the one or more selected data users to perform the one or more data transactions on the data market platform; identifying, based upon the first set of information, the one or more predefined user categories for authorizing the one or more selected data users to perform the one or more data transactions on the data market platform; and auto-generating, based upon the categorization, a set of recommendations on data usage behavior and feedback of the one or more selected data users.
In another aspect, there is provided a system for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform, the system comprising a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: define a plurality of data users operating on the data market platform; capture, by one or more data market applications, a first set of information corresponding to each of the plurality of data users defined, wherein the first set of information comprises historical transactional data of each of the plurality of data users; extract, from the first set of information, a second set of information using a data collecting module, wherein the second set of information comprises historical transactional data corresponding to one or more selected data users amongst the plurality of data users, and wherein the one or more selected data users comprise users initiating one or more data transactions on the data market platform; perform, using the second set of information, a plurality of steps, wherein the plurality of steps comprise: (i) identify, based upon a correlation of the second set of information and a predefined set of information, one or more data violations performed by the one or more selected data users; (ii) generate a third set of information comprising data violations details upon identifying the one or more data violations performed by the one or more selected data users; and (iii) optimize the categorization of the one or more selected data users into one or more predefined user categories upon identifying the one or more data violations, wherein the optimization comprises: (a) analyze, from the one or more data violations identified, whether the one or more selected data users intentionally or unintentionally performed the one or more data violations; (b) determine, from the one or more data violations identified and the third set of information generated, a total number of additional data users performing one or more historical data transactions with the one or more selected data users; and (c) categorize, based upon the one or more data violations performed intentionally or unintentionally, the total number of additional data users determined, and a pre-estimated value corresponding to the one or more data violations, the one or more selected data users into the one or more predefined user categories via a user grouping module; authorize, based upon the categorization, the one or more selected data users to perform the one or more data transactions on the data market platform; optimize the categorization by identifying, based upon the first set of information, the one or more predefined user categories for authorizing the one or more selected data users to perform the one or more data transactions on the data market platform; and auto-generate, based upon the categorization, a set of recommendations on data usage behavior and feedback of the one or more selected data users.
In yet another aspect, there is provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes the one or more hardware processors to perform a method for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform, the method comprising: defining, by the or more hardware processors, a plurality of data users operating on the data market platform; capturing, by one or more data market applications, a first set of information corresponding to each of the plurality of data users defined, wherein the first set of information comprises historical transactional data of each of the plurality of data users; extracting, from the first set of information, a second set of information using a data collecting module, wherein the second set of information comprises historical transactional data corresponding to one or more selected data users amongst the plurality of data users, and wherein the one or more selected data users comprise users initiating one or more data transactions on the data market platform; performing, using the second set of information, a plurality of steps, wherein the plurality of steps comprise: (i) identifying, based upon a correlation of the second set of information and a predefined set of information, one or more data violations performed by the one or more selected data users; (ii) generating a third set of information comprising data violations details upon identifying the one or more data violations performed by the one or more selected data users; and (iii) optimizing the categorization of the one or more selected data users into one or more predefined user categories upon identifying the one or more data violations, wherein the optimization comprises: (a) analyzing, from the one or more data violations identified, whether the one or more selected data users intentionally or unintentionally performed the one or more data violations; (b) determining, from the one or more data violations identified and the third set of information generated, a total number of additional data users performing one or more historical data transactions with the one or more selected data users; and (c) categorizing, based upon the one or more data violations performed intentionally or unintentionally, the total number of additional data users determined, and a pre-estimated value corresponding to the one or more data violations, the one or more selected data users into the one or more predefined user categories via a user grouping module; authorizing, based upon the categorization, the one or more selected data users to perform the one or more data transactions on the data market platform; identifying, based upon the first set of information, the one or more predefined user categories for authorizing the one or more selected data users to perform the one or more data transactions on the data market platform; and auto-generating, based upon the categorization, a set of recommendations on data usage behavior and feedback of the one or more selected data users.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 illustrates a block diagram of a system for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform, in accordance with some embodiments of the present disclosure.
FIG. 2 is an architectural diagram depicting components and flow of the system for the categorization of data users and the optimization thereof to authenticate data transactions on the data market platform, in accordance with some embodiments of the present disclosure.
FIG. 3A through 3B is a flow diagram illustrating the steps involved in the process of the categorization of data users and the optimization thereof to authenticate data transactions on the data market platform, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
Embodiments of the present disclosure provide systems and methods for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform. A data marketplace offers a very large volume of data for selling and buying. There are multiple users accessing the data marketplace. Users include all types of data providers, and the data marketplace system actively sourcing new kinds of data has to be secured. The traditional systems and methods have provided for a secured environment by protecting servers against external and internal attacks, allowing data owners to set rules around accessing and putting data usage agreements in place.
Network connectivity enables different data vendors to exchange profiles for common data customers on a data marketplace, either statically or dynamically, in order to build broad and detailed profiles across vendor domains. There exist many potentially powerful synergies between the data sets that may collected by different data sellers to be leveraged to provide appropriate services and products to data customers on a data marketplace platform. When analyzed with the proper statistical tools these data sets can reveal fundamental patterns in the behavior of data users, and enable a data seller to provide appropriate information to a data user. Furthermore, access to user-profiles collected by other data sellers may enable sellers (vendors) to provide focused information delivery to first-time data users, and also cross-market services with other appropriate data vendors.
However, the data sellers want to know information of data buyers before selling their data to verify whether the buyer is trustworthy, reliable, and policy compliant. The data seller expects such information related to the data buyer from various applications. To provide information related to the data buyer, the applications use provenance data (for example, payment history available) to capture every data transaction of the data buyers and by using that transaction data, some of the traditional systems and methods classify the data buyers based on their data transactions. However, the traditional systems and methods provide for a generalized classification or simply provide a rating for a data buyer based upon some previous information like data buyer’s payment history.
Hence, there is a need for a methodology that provides for a categorization of data users and an optimization thereof to authenticate all data transactions to be on data market platform(s) to ensure an authentic data access, and thereby enhancing the security of such platform(s).
Referring now to the drawings, and more particularly to FIG. 1 through 3B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 illustrates an exemplary block diagram of a system 100 for categorization of data users and an optimization thereof to authenticate data transactions on a data market platform, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory 102. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
According to an embodiment of the present disclosure, by referring to FIG. 2, the architecture of the data market platform 201 implementing the proposed methodology may be considered in detail. By referring to FIG. 2 again, it may be noted that the architecture comprises one or more data market applications 202, a data collecting module 203, a provenance database 204, a user grouping module 205, and a data transaction verifier 206.
Herein, the one or more data market applications 202 capture historical transactional information of data users on the data market platform 201. The data collecting module 203 facilitates extracting historical transactional information or data of data users initiating data transaction(s) on the data market platform 201. The provenance database 204 includes historical information (for example, data violation(s) history, unauthorized data manipulation(s) performed etc.) pertaining to data users on the data market platform 201. The user grouping module 205 categorizes data users into predefined user categories. The data transaction verifier 206 verifies all data transactions performed on the data market platform 201.
FIG. 3A through 3B, with reference to FIGS. 1 and 2, illustrates an exemplary flow diagram of a method for the categorization of data users and the optimization thereof to authenticate data transactions on the data market platform, in accordance with some embodiments of the present disclosure. In an embodiment the system 100 comprises one or more data storage devices of the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to the components of the system 100 as depicted in FIG. 1 and the flow diagram. In the embodiments of the present disclosure, the hardware processors 104 when configured the instructions performs one or more methodologies described herein.
According to an embodiment of the present disclosure, at step 301, the one or more hardware processors 104 define a plurality of data users operating on the data market platform 201. In general, a data marketplace is an online venue where users can buy and sell data. Data Marketplace is a platform that facilitates various entities in monetizing data and may provide infrastructure for data storage services. It can also be a platform where end users can discover and shape, analyze and publish data. A data market platform which offers a variety of data may be accessed by a number of data users (or users) at a time.
The plurality of data users may comprise users or customers that access data or data products as well as governing the related applications and the control or use of the data once exchanged on a data market platform. The term “data user” (or the plurality of data users) may be associated with a data buyer, a data seller or a data broker any other person accessing a data market platform in some way or the other. Further, the data user may comprise of an entity that has or is predicted to in the future make a procurement of one or more data products, one or more data services, one or more data contents etc. from another entity. The plurality of data users may comprise not just an individual or a family, but also businesses, organizations, or the like. Further, as used herein, the term "entity" refers to a customer, subscriber, user, or the like.
According to an embodiment of the present disclosure, at step 302, the one or more hardware processors 104 capture, via one or more data market applications 202, a first set of information corresponding each of to the plurality of data users defined, wherein the first set of information comprises historical transactional data of each of the plurality of data users defined on the data market platform 201. In an embodiment, the historical transactional data may comprise, but not limited to, transactions involving one or more data users amongst the plurality of data users defined, frequency of interaction(s) of the one or more data users with the any of the plurality of data users or with data users other than the plurality of data users, archived data, such as archived feedback data, data items listed for sale, and data items browsed for purchase. The one or more hardware processors 104 may capture the first set of information from the provenance database 204 or from a plurality of other sources, for example an existing corpus of documents.
Considering an example scenario, suppose data buyers X, Y and Z (amongst the plurality of data users) intend to buy data corresponding to automated cars on the data market platform 201. The first set of information corresponding to the data buyers X, Y and Z may comprise (but not limited to) driving license details, online payment details of previous car(s), and policy premium payment details etc. Similarly, for data sellers A, B and C (amongst the plurality of data users) registered on the data market platform 201 to sell data corresponding to the automated cars, the first set of information that may be captured may comprise (but not limited to) number of agreements executed with automatic car dealers, feedback on social media platforms corresponding to the quality or price of previous transactions made etc.
According to an embodiment of the present disclosure, at step 303, the one or more hardware processors 104 extract, from the first set of information, a second set of information via the data collecting module 203, wherein the second set of information comprises historical transactional data corresponding to one or more selected data users amongst the plurality of data users, and wherein the one or more selected data users comprise users initiating one or more data transactions on the data market platform 201. Suppose, a data user K intends to initiate a data transaction to buy data on automated cars on any data market platform. The data user K may initiate the data transaction by selecting the option ‘initiate transaction’ on said data market platform. The one or more hardware processors 104, based upon the option ‘initiate transaction’, categorize the data user K as the one or more selected data users.
Considering same example scenario as discussed in the step 302 above, suppose the data user X intends to buy a digital data comprising information on smart climate control features, sensory parking details or automatic gear details etc. of one or more automated cars on the data market platform. Upon selecting the option ‘initiate transaction’ on the data market platform, the data user X may be selected as the one or more selected data users by the one or more hardware processors 104.
In an embodiment, the one or more hardware processors 104 extract the second set of information corresponding to the data user X. Thus, the second set of information that may be extracted based upon the first set of information may comprise, inter-alia, previous car(s) details, policy renewal details, driving rules violation(s) details, driving license number etc.
According to an embodiment of the present disclosure, at step 304, the one or more hardware processors 104 perform, using the second set of information, a plurality of steps. At step 304(i), the one or more hardware processors 104 identify, based upon a correlation of the second set of information and a predefined set of information, one or more data violations performed by the one or more selected data users. In an embodiment, the predefined set of information may comprise a license agreement or any pre-defined terms and conditions which are binding upon each of the plurality of data users for transacting on the data market platform 201.
Considering an example scenario, suppose a data user (buyer) X buys a supermarket data of city A with a license agreement, wherein the license agreement permits viewing the supermarket data five times for an area A1, two permissions to download the supermarket data for an area A2, and resale to any buyer in the area A3 on the data market platform 201, wherein the areas A1, A2 and A3 belong to the city A. Another data user (seller) Y, who intends to sell the supermarket data of the city A may, via the data collecting module 203, extract the second set of information or the historical transactional data corresponding to the data user X. The one or more hardware processors 104 may then perform the correlation of the second set of information and the predefined set of information, that is, the license agreement to identify any data violation(s) performed by the data user X.
Suppose the second set of information corresponding to the data user (buyer) comprises (but not limited to):
“X obtained supermarket data of a city B from Y in 2012”;
“X had three permissions to download the supermarket data an area B1 of the city B in 2012”; and
“X sold the supermarket data of the city B to A having records of defaults in bank payments in 2012”
Based upon the comparison by the one or more hardware processors 104, the one or more data violations may be identified as:
“Data download limits exceeded in 2012”; and
“Payment received from authorized sources”.
According to an embodiment of the present disclosure, at step 304(ii), the one or more hardware processors 104 generate a third set of information comprising data violations details upon identifying the one or more data violations performed by the one or more selected data users. In an embodiment, the third set of information may comprise name of data user(s) involved in the one or more data violations, nature of data, categories of data users like defined / undefined etc.
Further, the third set of information (or the data violation details) may comprise, for example, message fragments that triggered data violation(s), violated rule(s) of the policy, data identifying the message component (e.g., a file attachment name, message body, etc.), IP addresses of computing system(s) used in the one or more data violations etc.. Considering the same example scenario as in step 304(i) above, the third set of information may comprise:
“X previously violated supermarket data usage in 2012”; and
“X sold supermarket data to an unauthorized buyer A”.
According to an embodiment of the present disclosure, at step 304(iii), the one or more hardware processors 104 optimize the categorization of the one or more selected data users into one or more predefined user categories upon identifying the one or more data violations. Some of the traditional systems and methods provide for categorization of buyers or sellers (or data buyers or sellers) based upon historical supplying or historical buying information respectively. Further, some of the traditional systems and methods categorize the sellers based on ratings given by the buyers or based upon reputation data. However, the proposed methodology provides for the categorization of the data users and the optimization of the categorization, as the categorization is performed based upon the correlation of each of the historical transactions conducted by the data users (including the data users not transacting on the data market platform 201), the one or more data violation details and the optimization.
In an embodiment, the step of optimizing the categorization is preceded by identifying, based upon the first set of information, the one or more predefined user categories for authorizing the one or more selected data users to perform the one or more data transactions on the data market platform 201. The one or more predefined user categories may be identified, inter-alia, as per the requirements of the data market platform 201, requirements of the stakeholders, degree of scrutiny required, level of data involved etc.
In one embodiment, the one or more hardware processors 104 may identify the one or more predefined user categories by implementing a set of categorizing rules (pre-defined) (for example, define four categories in case of robotic surgeries information, define only two categories in case of a gaming platform). In another embodiment, the one or more hardware processors 104 may identify the one or more predefined user categories by using data on data violations performed on another similar data market platform.
Considering an example scenario, suppose a data market platform E facilitates a large volume of medical data transactions comprising sensitive robotic information on surgeries performed. The data market platform E is thus required to be fully secured and the stakeholders comprising doctors and patients may need the sensitive robotic information to be exchanged on E via a fully secured medium, with no unauthorized user permitted to access the sensitive robotic information.
In such a scenario, the data users (based upon the requirements) may be categorized in four categories, for example a category A comprising of fully compliant data users with secured accesses, a category B comprising fully compliant data users with partial accesses, a category C comprising partially complaint data users and a category D comprising non-compliant data users. The one or more hardware processors may thus identify the one or more predefined user categories as A, B, C and D, wherein A, B, C and D are identified based upon previous historical data of patients, doctors and administrative staff along with the requirements of the data market platform E, requirements of the stakeholders, degree of scrutiny required, level of data involved etc.
According to an embodiment of the present disclosure, the process of optimization may now be considered in detail. At step 304(iii)(a) the one or more hardware processors 104 analyze, from the one or more data violations identified, whether the one or more selected data users intentionally or unintentionally performed the one or more data violations. In general, the risk of data violations is higher in case of data market platforms, as the data market platforms host thousands of transactions every day and data may be hosted via one or more cloud based technologies. The data users may not have a complete control over sensitive data hosted on a cloud or other technologies.
Further, the digital laws governing data market platforms across different geographies may vary from a country to country. Hence, the proposed disclosure facilitates the optimization of the categorization by analyzing whether the one or more selected data users intentionally or unintentionally performed the one or more data violations.
Considering the same example scenario as in step 304(ii) above, suppose the third set of information is generated as “X previously violated supermarket data usage in 2012”; and “X sold supermarket data to an unauthorized buyer A”, the one or more hardware processors 104 analyze the one or more data violations as intentional as the data user X has previously violated supermarket data usage and sold the supermarket data to an unauthorized buyer intentionally, since the buyer to whom the supermarket data was sold by the data user X may have some historical fraudulent transaction data.
In an embodiment, the one or more hardware processors 104 may analyze the one or more data violations as intentional or unintentional by implementing a set of pre-defined rules, for example, “if the buyer is unauthorized or in case of previous violation, the one or more data violations to be categorized as intentional”.
In another embodiment, the one or more hardware processors 104 may analyze the one or more data violations based upon levels of access of the one or more selected users on the data market platform 201. For example, if the data user X has access to some sensitive data like net worth of other data users, any data violation performed by the data user X may be analyzed as intentional. It may be noted that the embodiments of the proposed disclosure do not restrict analyzing of the one or more data violations based upon the set of predefined rules or the levels of access only. The embodiments of the proposed disclosure facilitate analyzing the one or more data violations performed by the one or more selected data users by implementing various other methods or a combination of one or more methods thereof.
According to an embodiment of the present disclosure, at step 304(iii)(b) the one or more hardware processors 104 determine, from the one or more data violations identified and the third set of information generated, a total number of additional data users performing one or more historical data transactions with the one or more selected data users. The total number of additional data users comprise users other than the plurality of users (defined on the data market platform 201) who have previously transacted with the one or more selected data users on some other platforms.
The categorization of data users (buyers) is thus optimized as even if the one or more selected data users have transacted with any other additional data user (like buying or selling of data) on some other data management or data market platform successfully or unsuccessfully, the proposed methodology determines all historical successful transaction or data violations, along with the reasons from the one or more historical data transactions.
Considering same example scenario, suppose the one or more data violations identified is “X sold supermarket data to an unauthorized buyer A and X downloaded supermarket data legitimately and sold to B”. The total number of additional users may be identified as A and B, wherein A and B have not previously transacted on the data market platform 201, but have transacted with X on some other data management platform or some other data market or online platform.
According to an embodiment of the present disclosure, at step 304(iii)(c) the one or more hardware processors 104 categorize, based upon the one or more data violations performed intentionally or unintentionally, the total number of additional data users determined, and a pre-estimated value corresponding to the one or more data violations, the one or more selected data users into the one or more predefined user categories via the user grouping module 205.
In an embodiment, before performing the categorization, the pre-estimated value may be computed by the one or more hardware processors 104 as the ratio of the data violations as per the total data transactions of the data user (or the data buyer). The process of categorization and the pre-estimated value computation may now be considered in detail.
Suppose, the one or more predefined user categories have been identified as A, B and C for a data market platform 201. Suppose B1, B2 and B3 comprise the plurality of data users (data buyers) defined on any data market platform (other than the data market platform 201). The data user B1 buys data pertaining to purchasing power of the consumers from different sellers across a city X. B1 has right to access data ten times. Another data user B2 buys similar kind of data (as B1) with view and edit rights across a city Y. Finally, a data user B3 buys similar kind of data as well with view and copyrights across city Z.
The data user B1 buys 25 areas of data and used that for his business without violating any rights by attempting to download. The data user B2 buys 50 areas of data and manipulated data as per his needs and also attempted to download the manipulated data of 10 areas data. The data user B3 buys the data of 10 areas and attempted to sell the data by copying.
In an embodiment, while performing the categorization, the one or more hardware processors 104 perform the correlation of the second set of information and the predefined set of information (for example, license terms or any attempts to perform unassigned rights) for the data users B1, B2 and B3 and the data user B1 is identified as not violating any access rules. Similarly, the data user B2 is identified to have manipulated the data and the data user B3 is identified to have misused the copyright on the data and attempted to sell the data by copying to sell data on the data market platform 201.
The data user B1 accessed the data ten times (as per the rights), and during eleventh attempt the data user B1 unintentionally attempted to access, before attempting the access, a network (not shown in the figure) of the data market platform 201 displays access limit is completed, and based on that information, the data user B1 stops access attempts. Some other data buyers may try accessing the data again by providing the access key details even though access limit is crossed and even warning is given by the network of data market platform 201. In logs, the network of the data market platform 201 stores attempt information of the data user B1 as unintentional and not violating any access and categorized as the data user B1 in a pre-defined user category A by the user grouping module 205.
Suppose, the data user B2 is identified to download 20% of his data by editing and is further identified to intentionally attempt to download the data based upon access history (for example, logs) and thereby violating his terms and conditions. The data user B2 may thus be categorized in a pre-defined user category B by the user grouping module 205. The pre-estimated value (that is, the ratio of the data violations as per the total data transactions of the data user) of the data user B2 may be computed as:
10/50×100=20%
wherein 50 is initial areas of data bought by the data user B2, 10 is areas of data manipulated by the data user B2.
Similarly, suppose, the data user B3 is identified to have copied the entire data bought for selling across other cities (that is, other than the city Z where B3 was authorized to sell the data) and sold that data in cities K and L. The one or more hardware processors 104 may categorize the data user B3 in a pre-defined user category C by the user grouping module 205. The pre-estimated value of the data user B2 may be computed as:
10/10×100=100%
wherein 10 is initial areas of data bought by the data user B3, 10 is areas of data manipulated by the data user B3 (as the data user B3 is identified to have copied the entire data).
In an embodiment, the step of optimizing facilitates auto-generating, based upon the categorization, a set of recommendations by the one or more hardware processors 104, on data usage behavior and feedback of the one or more selected data users. The set of recommendations may thus comprise an analysis of how the one or more selected data users have used their bought data, how the one or more selected data users have previously transacted on other data market of data management platform (that is, other than the data market platform 201), and an overall feedback. Considering the same example scenario, the set of recommendations may be auto-generated for the data users B2 and B3 as below:
“It is identified that the data user B2 intentionally misused 20% of the data from overall 100% of the data bought. The data user B2 has intentionally misused 10% of the data on another data market platform E 48 months back. The data user B2 to be verified with greater scrutiny before being authorized to transact on the data market platform 201”; and
“It is identified that the data user B3 has intentionally violated all copyright terms and conditions. The data user B3 has previously unintentionally violated all license terms on a data market platform Y. The data user B2 has an overall low rating on all overall online platforms”.
According to an embodiment of the present disclosure, at step 305, the one or more hardware processors 104 authorize, based upon the categorization, the one or more selected data users to perform the one or more data transactions on the data market platform 201. The authorization may be performed on the basis of a pre-defined criteria, for example, using a set of pre-defined authorization rules, to authorize only authentic and verified data users to perform the one or more data transactions on the data market platform 201. In an example implementation, the set of pre-defined authorization rules may comprise:
“Authorize data users categorized as A to perform data transactions”;
“Verify data users categorized as B using log information before authorizing category B users”;
“Copyright Violators not allowed to perform data transactions”’ and
“Category C data users not allowed to perform data transactions”.
Considering the same example scenario as in step 304(iii)(c) above, out of three data users B1, B2 and B3, the data user B1 may be allowed to transact on the data market platform 201 as the data users B1 has been categorized in the category A. The data users B3 may not be allowed to transact at all. The data users B2 may be allowed to transact based from some further verification of the download details, like downloading though perform intentionally but based upon wrong historical information may be regarded as unintentional downloading. Finally, the data transaction verifier 206 verifies the one or more data transactions performed on the data market platform 201 by the one or more selected data users authorized to perform the one or more data transactions.
According to an embodiment of the present disclosure, advantages of the proposed disclosure may now be considered in detail. The proposed disclosure facilitates a secured online data market platform as based upon the categorization, only authentic, genuine and verified data users are allowed to perform data transaction(s) on any data market or data management platform, thereby reducing the scope of overall misuse of data. The proposed methodology helps a data seller to verify whether a data buyer is trustworthy, reliable and policy compliant. The proposed methodology facilitates the categorization and the optimization as it captures every data transaction related to data buyers and categorizes the data buyers based on their data transactions and provides the details about a data buyer along with category details. A data seller decides to sell or not to a data buyer based on the information provided.
The proposed solution thus acts as a hub where data relevant to reaching different data users over different channels is integrated, analyzed, and shared. By auto-generating the set of recommendations on the data usage behavior of each of the plurality of data users, the proposed disclosure facilitates real-time decision making, wherein data buyer(s) may quickly analyze information about various data sellers to make decision whether to sell or not to sell data to data buyer(s). Using historical data and other data related to data buyers over a time may be useful in creating a model of a data buyer demand, such as data demand over time or for a particular product. Overall, by implementing the proposed methodology, data market platform(s) may provide services related to collection of a data buyer reputation information, providing warnings, alerts, and the like, providing analysis of reputation information of data buyer(s), and / or making alternative recommendations.
In an embodiment, the memory 102 can be configured to store any data that is associated with the categorization of data users and the optimization thereof to authenticate data transactions on the data market platform. In an embodiment, the information pertaining to the plurality of data users, the first set of information, the second set of information extracted, the correlation of the second set of information and the predefined set of information, the third set of information, the one or more data violations identified, and optimizing the categorization of the one or more selected data users into the one or more predefined user categories etc. is stored in the memory 102. Further, all information (inputs, outputs and so on) pertaining to the categorization of data users and the optimization thereof to authenticate data transactions on the data market platform may also be stored in the database, as history data, for reference purpose.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.