Data exposure in a sixth-generation (6G) communication network

Abstract

An apparatus (200) for exposing data in a 6G communication network is disclosed. The apparatus (200) is configured to receive a data exposure request to request data associated with at least one of one or more network entities and network services, from at least one of a Network Function (NF) and a User Equipment (UE). The apparatus (200) is further configured to obtain, from a data collection framework (107) connected to the one or more network entities, the requested data based on the received data exposure request. The apparatus (200) is further configured to transmit the requested data to at least one of the NF and the UE.

Description

DATA EXPOSURE IN A SIXTH-GENERATION (6G) COMMUNICATION NETWORKCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to India Provisional Application No. 202411060445, filed on August 9, 2024, and India Non-Provisional Application No. 202411060445, filed December 20, 2024, the entire contents of which are incorporated herein by reference.FIELD

[0002] The present disclosure relates to Data Exposure in a Sixth-Generation (6G) communication network.BACKGROUND

[0003] The information disclosed in this background section is only for the enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

[0004] Network data insights offer significant monetization opportunities for Communication Service Providers (CSPs). These opportunities arise from systematic collection, processing, and anonymization of network data, including traffic patterns, user behaviors, device attributes, and location information. The CSPs can deliver value-added services to a diverse clientele, including enterprises, government entities, researchers, and advertisers. The stakeholders can leveragenetwork data insights for various applications, including market analysis, network planning, public safety, and social welfare, etc.

[0005] However, the provision of network data as a value-added service presents challenges and risks, including concerns related to data quality, privacy, security, governance, and interoperability. Consequently, there is a pressing need to investigate the requirements and solutions for facilitating network data as a value-added service within the communication system (e.g., a Third-Generation (3G) communication system, a Fourth-Generation (4G) communication system, a Fifth- Generation (5G) communication system, a beyond 5G communication system, etc.), while considering pertinent technical, regulatory, and ethical dimensions.

[0006] Further, the emergence of Artificial Intelligence (Al) driven, intent-driven, and transfer learning technologies within the Third Generation Partnership Project (3 GPP) has unlocked new avenues for network data utilization. These technologies can enhance the extraction of insights from network data, optimize network performance, and facilitate personalized service delivery. Nonetheless, they introduce additional challenges regarding data privacy and security. For instance, Al algorithms may inadvertently infer sensitive information from seemingly benign data, and intent-driven technologies could expose user intentions that are deemed private. Therefore, implementing robust data anonymization techniques is essential to safeguard user privacy and security in the context of these advanced technologies.

[0007] Furthermore, a comprehensive examination of data-sharing methodologies is necessary to ensure that network data can be shared effectively and securely among various stakeholders, including network operators, service providers, and third-party developers. Each stakeholder mayhave distinct requirements and constraints concerning data access, usage, and protection, which must be meticulously addressed.SUMMARY

[0008] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the disclosure nor is it intended for determining the scope of the disclosure.

[0009] According to one embodiment of the present disclosure, an apparatus is disclosed. The apparatus is configured to receive a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from at least one of a Network Function (NF) and a User Equipment (UE). The apparatus is further configured to obtain, from a data collection framework connected to the one or more network entities, the requested data based on the received data exposure request. The apparatus is further configured to transmit the received data to at least one of the NF and the UE.

[0010] According to another embodiment of the present disclosure, a method is disclosed. The method comprises receiving, by at least one Data Exposure Entity (DEE), a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from at least one of a Network Function (NF) and a User Equipment (UE). The method further comprises obtaining, by the at least one DEE from a data collection framework connected to the one or more network entities, the requested data based on the receiveddata exposure request. The method further comprises transmitting, by the at least one DEE, the received data to at least one of the NF and the UE.

[0011] According to one embodiment of the present disclosure, a non-transitory computer- readable medium is disclosed. The non-transitory computer-readable medium stores instructions. The instructions comprising one or more instructions that are executed by at least one Data Exposure Entity (DEE). The at least one DEE comprises one or more processors. The instructions cause the one or more processors to receive a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from at least one of a Network Function (NF) and a User Equipment (UE). The instructions further cause the one or more processors to obtain, from a data collection framework connected to the one or more network entities, the requested data based on the received data exposure request. The instructions further cause the one or more processors to transmit the received data to at least one of the NF and the UE.

[0012] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope. The disclosure will be described and explained with additional specificity and detail in the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Features, aspects, and advantages of embodiments of the disclosure will be described below with reference to the accompanying drawings, in which like reference numerals denote like elements, and wherein:

[0014] FIG. 1 illustrates a block diagram of a 6G communication network, according to an embodiment of the present disclosure;

[0015] FIG. 2 illustrates a schematic block diagram of a Data Exposure Entity (DEE) for exposing data to at least one of a Network Function (NF) and a User Equipment (UE), according to an embodiment of the present disclosure;

[0016] FIG. 3 illustrates a schematic operational flow diagram of a plurality of modules of the DEE, according to an embodiment of the present disclosure;

[0017] FIG. 4 illustrates a signal flow diagram depicting exposing data to the at least one of the NF and the UE, according to an embodiment of the present disclosure;

[0018] FIG. 5 illustrates a signal flow diagram depicting a registration process for registering the DEE with a Network Repository Function (NRF), according to an embodiment of the present disclosure;

[0019] FIG. 6 illustrates a flow diagram depicting a method for exposing data to the at least one of the NF and the UE, according to an embodiment of the present disclosure; and

[0020] FIG. 7 is an example diagram of example components of a wireless communication device, according to an embodiment of the present disclosure.DETAILED DESCRIPTION

[0021] The following detailed description of example embodiments refers to the accompanying drawings. The present disclosure provides illustrations and descriptions, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the present disclosure or may be acquired from the practice of the implementations. Further, one or more features or components of one embodiment may be incorporated into or combined with another embodiment (or one or more features of another embodiment). Additionally, the flowchart and description of operations provided below relate to at least one of the embodiments in the present disclosure. It should be noted that it is possible to make other embodiments that do not exactly match the flowchart and its description. It is understood that in other embodiments one or more operations may be omitted, one or more operations may be added, and one or more operations may be performed simultaneously (at least in part).

[0022] It will be apparent that systems and / or methods, described herein, may be implemented in different forms of hardware, software, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods should not limit their implementations. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code. It is understood that software and hardware may be designed to implement the systems and / or methods based on the description herein.

[0023] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, the particular combinations are not intended to limit the disclosure ofimplementations. In fact, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Even if a dependent claim directly depends on only one claim, the present disclosure may indicate that the dependent claim is dependent on other claims in the claim set.

[0024] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” (in other words, nouns not mentioned in the plural) are intended to include one or more items, and may be used interchangeably with “one or more.” Also, as used herein, the terms “has,” “have,” “having,” “include,” “including,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Furthermore, expressions such as “at least one of [A] and [B],” “[A] and / or [B],” or “at least one of [A] or [B]” are to be understood as including only A, only B, or both A and B.

[0025] The foregoing disclosure provides illustration and description but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from the practice of the implementations.

[0026] The challenges in existing communication systems are outlined here. The 5G ecosystem faces limitations in its data exposure capabilities, lacking a robust framework for anonymizing and sharing bulk data with third parties. Current standards primarily address specific scenarios, such as User Equipment (UE) mobility, UE communications, performance data, and Quality of Experience (QoE) metrics. However, the current standards fail to offer comprehensive solutions for exposing anonymized data for monetization purposes. This limitation restricts operators fromfully utilizing their extensive data assets and hinders potential revenue generation from data-driven services.

[0027] Although 5G incorporates data exposure capabilities within the control plane, user plane, and Operations, Administration, and Maintenance (0AM) through a Network Exposure Function (NEF), these capabilities are limited to specific scenarios. There is no established requirement or framework for exposing anonymized data from a specific service (such as sensing), location, 0AM, or Radio Access Network (RAN) to third parties — whether in Business-to-Business (B2B), Business-to-Business-to-Consumer (B2B2C), or Business-to-Consumer (B2C) contexts — for monetization purposes.

[0028] The 6G communication system aims to address emerging service categories such as holographic communication, tactile internet, Radio Frequency (RF) sensing, robotics, digital twins, and the metaverse. The 6G communication system focuses on achieving ubiquitous connectivity, integrating sensing with communication, and advancing Al and computing capabilities. These advancements include extensible real-time AVMachine Learning (ML), support for Generative Al and Large Language Models (LLMs), and Al-embedded Network Functions (NFs). A 6G data collection and exposure framework may enable Application Programming Interfaces (APIs) for data anonymization and monetization, along with native Al capabilities optimized for data collection and exposure. Additionally, vertical-specific capabilities such as massive communication systems and cognitive, autonomous networks with intent-based management, autonomous operations, and real-time management solutions (e.g., Service Management and Orchestration (SMO), r-apps, and x-apps) are deemed critical.

[0029] A unified, standardized framework for cloud-native NF management and orchestration is also essential to support diverse use cases, including those that extend beyond currently identified applications.

[0030] The 6G communication system is expected to act as a transformative force, offering extended reach, novel user equipment form factors, and a robust data-centric approach with an AI- native architecture. This represents a fundamental shift from 5G, necessary to achieve commercial, social, and business success. Data exposure plays a pivotal role in unlocking new value propositions for operators, who typically maintain vast amounts of data stored in silos. Large operators often manage 10-20 separate data repositories across different regions. Aggregating and anonymizing this data could enable operators to monetize it by sharing it with third parties, fostering the creation of innovative services. However, existing 3GPP standards lack provisions for such data anonymization and exposure. Therefore, a standardized approach to data sharing is needed to promote interoperability, collaboration, and innovation while ensuring robust data privacy and security.

[0031] The present disclosure solves one or more of the above-mentioned problems by providing a Data Exposure Entity (DEE) to expose the data to one or more Application Functions (AFs). The DEE may include but is not limited to a Data Function (DF), a Data Management Function (DMF), a Data Exposure Function (DEF). In a non-limited embodiment, the DEE may be co-located with any existing NF. The NFs may include but are not limited to third-party NFs outside the 6G communication system and the Network Functions (NFs) within the 6G communication system, such as Access and Mobility Management Function (AMF), Session Management Function (SMF),Policy Control Function (PCF), etc.

[0032] Data exposure can be broadly classified into two main categories: action-oriented data and informative data. Action-oriented data enables immediate responses based on derived insights, while informative data provides critical information necessary for decision-making and strategic planning. Examples of informative data may include but are not limited to call behavior analytics and network probe data. When implementing data exposure, one or more operational considerations must be taken into account.

[0033] Adopting a multi-network approach is essential to leveraging data from a multi-access, multi-network environment. The multi-network approach provides a holistic understanding of interactions across geography, humans, and machines on a massive scale, facilitated by integrating emerging technologies like sensing, the Internet of Things (loT), and Vehicle-to-Everything (V2X) communication.

[0034] Safeguarding privacy and security requires implementing stringent measures, including privacy protocols, anonymization techniques, governance frameworks, and robust security systems. As organizations increasingly serve as key data repositories within their regions, ensuring the protection of sensitive information becomes a critical priority.

[0035] Maximizing value through context entails aligning shared data with the specific needs of external stakeholders. By anchoring data in the context of these stakeholders, rather than limiting it to internal network perspectives, its relevance and practical utility can be significantly enhanced.

[0036] Architecturally, data exposure frameworks must evolve continually to ensure accurate consumer recommendations and effective data management. Advanced methods such as graphbased interrogation, along with support for Generative Artificial Intelligence (Gen-AI) and Large Language Models (LLMs), are crucial for enhancing data analysis capabilities.

[0037] Integrating Al into data exposure strategies is vital for developing advanced techniques to extract actionable insights from complex datasets. This integration enhances the overall efficiency and value of data-driven operations, enabling smarter and more effective decision-making processes.

[0038] Referring now to the drawings, and more particularly to FIGS. 1 to 7, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.

[0039] FIG. 1 illustrates a block diagram of a 6G communication network 100 (also referred to as the network 100), according to an embodiment of the present disclosure. The network 100 may include a 6G core (6GC) 101 which is connected to a User Equipment (UE) 102, a Radio Access Network (RAN) 103, a user plane 104, and an external data network 105. The 6GC 101 may also include a plurality of Network Functions (NFs) (NF1, NF2, ...NFn), a Network Repository Function (NRF) 106, and a data collection framework 107. In an embodiment, even though the data collection framework 107 is depicted as part of the 6GC 101, the data collection framework 107 may also be located externally and connected to the 6GC 101. The 6GC 101 may also be connected to external customers and other operators 108, referred to as one or more Application Functions (AFs) via a Data Exposure Entity (DEE) 109.

[0040] In one or more embodiments, the 6GC 101 integrates various network functions, facilitating unified data collection to ensure seamless communication and efficient resource management. The 6GC 101 is configured to provide an interface with the UE 102 and enable enduser access and interaction with corresponding one or more network services. The RAN 103 is configured to provide wireless connectivity, manage radio resources, and ensure optimal signaltransmission between the UE 102 and the 6GC 101 (e.g., the DEE 109). The User Plane 104 is configured to handle user data traffic, ensuring efficient delivery of services such as voice, video, and data. Additionally, the 6GC 101 connects to external data network 105, allowing for interoperability and data exchange with external systems. The 6GC 101 is configured to provide interfaces with the one or more AFs 108, enabling collaboration and service integration across diverse platforms and enhancing the overall functionality of the network ecosystem.

[0041] In an embodiment, the techniques of the present disclosure have been implemented in the DEE 109 and are further explained in reference to FIGS. 2-7.

[0042] FIG. 2 illustrates a schematic block diagram of the DEE 109 for exposing data to the at least one of the NF and the UE 102, according to an embodiment of the present disclosure. The DEE 109 may include an apparatus 200 configured to perform the operation of the DEE 109. In an embodiment, the apparatus 200 may be connected to the NRF 106, the data collection framework 107, and the one or more AFs 108. In one embodiment, the apparatus 200 may correspond to the DEE 109.

[0043] The apparatus 200 may be configured to receive a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from the at least one of the NF and the UE 102. In a non-limited embodiment, the one or more network entities may include but are not limited to various NFs, Al engines, User Plane Function (UPF), Operations, Administration, and Maintenance (0AM), Al agents, and similar network entities. In an embodiment, the corresponding one or more network services may include but are not limited to sensing, traffic patterns and data associated with the corresponding one or more network services may be stored in multiple network entities. In a non-limited embodiment,the data exposure request may include but is not limited to a time based request, a user equipment (UE) based request, an identification based request, an inference data based request, a training data based request, a sensing data based request, and an anonymization based request. The apparatus 200 may further be configured to obtain the requested data based on the received data exposure request from a data collection framework (107) connected to the one or more network entities. In particular, the apparatus 200 may transmit the received data exposure request to the data collection framework 107 connected with the one or more network entities. In a non -limited embodiment, the apparatus 200 may transmit the received data exposure request to the data collection framework 107 in an event subscription request. In a non-limited embodiment, the data collection framework 107 may collect the requested data from the one or more network entities using a predefined service bus, such as a Common Service Bus (CSB). Accordingly, the apparatus 200 may then be configured to obtain the requested data from the data collection framework 107. In a non-limited embodiment, the apparatus 200 may be configured to obtain the requested data in an event subscription response. The apparatus 200 may then be configured to transmit the requested data to the at least one of the NF and the UE.

[0044] The apparatus 200 may include one or more processors (hereinafter referred to as the processor 220), a memory 210, and a plurality of modules 230. In one embodiment, the processor 220 may include at least one data processor for executing processes in a virtual storage area network. The processor 220 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. In one embodiment, the processor 220 may include a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor 220 maybe one or more general processors, digital signal processors (DSPs), application-specific integrated circuits, field-programmable gate arrays (FPGAs), servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor 220 may execute a software program, such as code generated manually (i.e., programmed) to perform the desired operation. The processor 220 may implement various techniques such as, but not limited to, image processing, data extraction, artificial intelligence (Al), machine learning (ML), deep learning (DL), and so forth to achieve the desired objective.

[0045] In one embodiment, the processor 220 may be configured to perform the functions of the apparatus 200.

[0046] The memory 210 may be communicatively coupled to the processor 220. The memory 210 may be configured to store data and instructions executable by the processor 220. In one embodiment, the memory 210 may communicate via a bus within the apparatus 200. The memory 210 may include but is not limited to, a non-transitory computer-readable storage media, such as various types of volatile and non-volatile storage media including, but not limited to, random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one example, the memory 210 may include a cache or random-access memory for the processor 220. In alternative examples, the memory 210 is separate from the processor 220, such as a cache memory of a processor, the system memory, or other memory. The memory 210 may be an external storage device or database for storing data. The memory 210 may be operable to store instructions executable by the processor 220. The functions, acts, or tasksillustrated in the figures or described may be performed by the programmed processor 220 for executing the instructions stored in the memory 210. The functions, acts, or tasks are independent of the particular type of instructions set, storage media, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing, and the like. The memory 210 may further include a database to store the data. Further, the memory 210 may include an operating system for performing one or more tasks of the apparatus 200, as performed by a generic operating system in the communications domain.

[0047] The plurality of modules 230, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The plurality of modules 230 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and / or any other device or component that manipulates signals based on operational instructions. The plurality of modules 230 may be configured to one or more operations of the apparatus 200 and / or the processor 220.

[0048] Further, the plurality of modules 230 can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit can comprise a computer, the processor 220, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general -purpose processor which executes instructions to cause the general -purpose processor to perform the required tasks, or the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the plurality of modules 230 may be machine-readable instructions (software) which,when executed by a processor / processing unit, perform any of the described functionalities.Furthermore, the data serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules.

[0049] A detailed explanation of the various functions and the operations of the apparatus 200 and / or the associated processor 220 or the plurality of modules 230 has been explained in the following description with reference to FIGS. 3-7.

[0050] FIG. 3 illustrates a schematic operational flow diagram of the plurality of modules 230 of the DEE 109, according to an embodiment of the present disclosure. The plurality of modules 230 may include a receiving module 310, an obtaining module 320, and a transmitting module 330.

[0051] The receiving module 310 may be configured to receive the data exposure request to request data associated with at least one of the one or more network entities and corresponding one or more network services, from the at least one of the NF and the UE 102. In a non-limited embodiment, the one or more network entities may include but are not limited to various NFs, Al engines, Al agents, UPF, 0AM, and similar network entities. In an embodiment, the corresponding one or more network services may include but are not limited to sensing, traffic patterns and data associated with the corresponding one or more network services may be stored in multiple network entities. In a non-limited embodiment, the receiving module may also receive the data exposure request from a third party, such as a city traffic controller via the NF. Further, in a non-limited embodiment, the data exposure request may include but is not limited to the time based request, the UE based request, the identification based request, the inference data based request, the training data based request, the sensing data based request, and the anonymization based request. For example, the time based request may include a request for the data for a predefined time duration.Similarly, the UE based request may include a request for the data corresponding to UEs associated with the one or more network entities. In another example, the UE based request may also include a request for the data corresponding to UEs availing the corresponding one or more network services. The data corresponding to the UE based request may include but is not limited to location data, Quality of Service (QoS) data, energy consumption data, and similar data. The identification based request may refer to the request for identification data associated with the one or more network entities or the UEs connected with the one or more network entities. The inference data based request may refer to the request for data associated with the inference services provided by the one or more network entities. The anonymization request may refer to a request to anonymize the data before sharing it with at least one of the NF and UE 102. The training data based request may refer to the request for data required to train one or more models to be used in inferencing services in the one or more network entities. The sensing data based request may refer to the request for data used to sense one or more objects by the network 100. For example, the network 100 may sense cars, persons, empty traffic slots etc., and provide that data to the 6GC 101. The various types of data requested in the data exposure request can be used in diverse sectors such as healthcare, loT, Radio Frequency (RF)-identification, and communication applications, etc.

[0052] The obtaining module 320 may be configured to obtain the requested data from the data collection framework 107 connected with the one or more network entities. In an embodiment, the obtaining module 320 may first transmit the received data exposure request to the data collection framework 107. In response, the obtaining module 320 may receive the requested data from the data collection framework 107. In a non-limited embodiment, the obtaining module 320 may be configured to authorize the received data exposure request before transmitting the received dataexposure request to the data collection framework 107. In an embodiment, the obtaining module 320 may authorize the received data exposure request using existing 5G token based authorization mechanism. In a non-limited embodiment, the obtaining module 320 may pre-process the received data exposure request before transmitting the received data exposure request to the data collection framework 107. The pre-processing of the received data exposure request may include but not limited to converting the received data exposure request in a format compatible to the data collection framework (107) using the LLM. In a non-limited embodiment, in response to receiving the data exposure request, the data collection framework 107 may collect the requested data from the one or more network entities using the predefined service bus, such as the CSB. In a real-time event-driven architecture, multiple services and applications often need to respond to the same network event. For instance, a QoS degradation or traffic congestion event might trigger actions across multiple network functions (AMF, SMF, PCF) and external systems (Al, Digital Twins). Accordingly, the CSB may act as a centralized event processor, ensuring that such events are properly routed and processed by the appropriate network functions and services. The CSB may also handle event subscription and distribution, making sure that events like a sudden drop in QoS or mobility handover are propagated to all relevant systems (e.g., NEF, external Al systems, edge applications) that need to respond. Further, the CSB may aggregate and transform data from multiple sources before routing the data to the appropriate destination. Such a function of the CSB is especially useful when data from several NFs needs to be processed or integrated into an Al engine or analytics platform. Accordingly, in a non-limited embodiment, the obtaining module 320 may be configured to transmit the received data exposure request to the data collection framework 107 in the event subscription request.

[0053] The obtaining module 320 may further be configured to obtain the requested data from the data collection framework 107 in response to the transmitted data exposure request. In a nonlimited embodiment, the obtaining module 320 may be configured to obtain the requested data in the event subscription response.

[0054] In response to obtaining the requested data, the transmitting module 330 may be configured to transmit the received data associated with at least one of the one or more network entities and corresponding one or more network services to at least one of NF and the UE 102. In a non-limited embodiment, the transmitting module 330 may be configured to pre-process the received data before transmitting the data to at least one of the NF and the UE 102. For example, the transmitting module 330 may be configured to pre-process the received data based on a type of data. For instance, if the requested data is the sensing data then the transmitting module 330 may reformat the sensing data according to the data exposure request. The transmitting module 330 may also remove any information that can be used to identify the UE or a human being. The transmitting module 330 may also use data pre-filling techniques to fill “empty” data with some dummy values. In another embodiment, the transmitting module 330 may be configured to anonymize the requested data before transmitting the data to at least one of the NF and the UE 102. For example, the transmitting module 330 may be configured to anonymize the received data based on the data exposure request. For example, if the data exposure request includes the anonymization request, then the transmitting module 330 may anonymize the received data. Thereafter, the transmitting module 330 may transmit the anonymized data to the one or more AFs 108. In a nonlimited embodiment, the anonymization may include but is not limited to a data masking, a pseudonymization, a generalization, a data swapping, a data perturbation, and a synthetization onthe requested data. The anonymization may ensure data privacy and security. For example, the sensing data may include a house address having an apartment number, street name, city, and state.The transmitting module 330 may remove personal and identifiable information like apartment numbers and city names from the sensing data by anonymizing the sensing data. However, despite removing a few elements, the data remains accurate for use.

[0055] FIG. 4 illustrates a signal flow diagram 400 depicting exposing data to at least one of the NF and the UE , according to an embodiment of the present disclosure. As shown, at operation 401, the DEE 109 performs a registration process with the NRF 106. The registration process is further explained in reference to FIG. 5. FIG. 5 illustrates a signal flow diagram 500 depicting a registration process for registering the DEE 109 with the NRF 106, according to an embodiment of the present disclosure. At operation 501, the at least one DEE 109 may transmit a Nnrf_NFManagement_NFRegister_request to the NRF 106. The Nnrf_NFManagement_NFRegister_request may refer to a request to register with the NRF 106 and may include an NF profile associated with the at least one DEE 109. In an embodiment, the NF profile may include details such as, but not limited to, a type of data to be exposed by the DEE 109. The NF profile may also include one or more values to identify the type of data anonymization techniques supported by the DEE 109. The NF profile may also include the location of the DEE 109 in the network 100. Hence, the NF profile may include the data related to the DEE 109. In response, at operation 503, the NRF 106 may store the NF profile associated with the at least one DEE 109. At operation 505, the NRF 106 may transmit a Nnrf NFManagement NFRegister response to the at least one DEE 109. The Nnrf_NFManagement_NFRegister_response may indicate successful registration of the at leastone DEE 109. Accordingly, the at least one DEE 109 may perform the registration process withNRF 106 prior to receiving the data exposure request.

[0056] Referring back to FIG. 4, at operation 403, the data collection framework 107 may perform the registration process with the NRF 106. At operation 405, at least one of the NF and the UE may discover the at least one DEE 109 via the NRF 106. In particular, at least one of the NF and the UE may send a request to the NRF 106 to receive identification information of the at least one DEE 109. The request may include details associated with the data, such as the data type to be requested by at least one of the NF and the UE. The NRF 106 may then identify the at least one DEE 109 based on the data type. For example, if the data to be requested is a sensing data, then the NRF 106 may identify the at least one DEE 109 capable of sharing the sensing data. Then, the NRF 106 may share the identification of the at least one DEE 109 with at least one of the NF and the UE. Then, at operation 407, at least one of the NF and the UE may transmit the data exposure request to the at least one DEE 109. At operation 409, the at least one DEE 109 may transmit the data exposure request to the data collection framework 107. At operation 411, the data collection framework 107 may collect the requested data from the one or more network entities 402. At operation 413, the at least one DEE 109 may obtain the requested data from the data collection framework 107. At operation 415, the at least one DEE 109 may transmit the received data to the one or more AFs 108.

[0057] Accordingly, the DEE 109 plays a key role in the 6G networks by providing data as a service (DaaS) to the one or more AFs 108. The DEE 109 is responsible for processing network data, ensuring that privacy is protected, and data is properly aggregated. Once the data is processed, the DEE 109 delivers the data as a service to at least one of the NF and the UE based on theirspecific requirements and Service Level Agreements (SLA). This allows external applications to make use of network data while maintaining strong privacy controls and meeting agreed-upon performance standards.

[0058] In an embodiment, the DEE 109 may segregate the data exposure functionalities into two main components, i.e., Control Plane related Functionality and a User Plane related Functionality. The DEE 109 may control the operations of the data service, managing the initiation, termination, and synchronization of data tasks (authorization of data entities, procedures, etc.) in the Control Plane related Functionality. The DEE 109 may handle pre-processing, anonymization, etc. of some aspects of user plane data, ensuring the efficient and reliable transport of the data to at least one of the NF and the UE. Further, in an embodiment, the DEE 109 may connect to other 6G NFs to collect data and expose it to northbound entities, such as AF 108. The DEE 109 may also connect to 3rdparty AFs 108 for secure and controlled access to the applications of the 3rdparty AFs 108.

[0059] FIG. 6 illustrates a flow diagram depicting a method 600 for exposing data to at least one of the NF and the UE, according to an embodiment of the present disclosure. The method 600 may be performed by the apparatus 200.

[0060] At step 601, the method 600 may include receiving, by the at least one DEE 109, the data exposure request to request data associated with at least one of the one or more network entities and corresponding one or more network services, from at least one of the NF and the UE. In a nonlimited embodiment, the one or more network entities may include but are not limited to various NFs, Al engines, UPF, 0AM, and similar network entities. In a non-limited embodiment, the data exposure request may include but is not limited to the time based request, the UE based request,the identification based request, the inference data based request, the training data based request, the sensing data based request, and the anonymization based request.

[0061] At step 603, the method 600 may include obtaining, by the at least one DEE 109 from a data collection framework 107 connected to the one or more network entities, the requested data based on the received data exposure request.

[0062] At step 605, the method 600 may include transmitting, by the at least one DEE 109, the received data to at least one of the NF and the UE.

[0063] While the above-discussed steps in FIG. 6 are shown and described in a particular sequence, the steps may occur in variations to the sequence in accordance with various embodiments. Further, a detailed description related to the various steps of FIG. 6 is already covered in the description related to FIGS. 1-5 and is omitted herein for the sake of brevity.

[0064] FIG. 7 is a diagram of example components of a wireless communication device 700, in accordance with an embodiment of the present disclosure. In one or more embodiments, the wireless communication device 700 may correspond to a wireless server and / or the apparatus 200. As shown in FIG. 7, the device 700 includes a processor 710, a memory 720, a storage component 730, an input component 740, an output component 750, a communication interface 760, and a bus 770.

[0065] The processor 710, as used herein, means any type of computational circuit that may comprise hardware elements and software elements. The processor 710 may be embodied as a multi-core processor, a single-core processor, or a combination of one or more multi-core processors and / or one or more single-core processors, a distributed processing system, or the like. The processor 710 may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU),an Accelerated Processing Unit (APU), an Application-Specific Integrated Circuit (ASIC), or another type of processing component.

[0066] The memory 720 includes a non-transitory computer-readable medium. The memory 720 includes a Random-Access Memory (RAM), a Read Only Memory (ROM), and / or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and / or an optical memory) that stores information and / or instructions for use by the processor 710. The memory 720 comprises machine-readable instructions which are executable by the processor 710. These machine-readable instructions when executed by the processor 710 cause the processor 710 to perform one or more method steps of an embodiment described above.

[0067] The storage component 730 stores information and / or software related to the operation and use of the device 700. For example, the storage component 730 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and / or a solid-state disk), a Compact Disc (CD), a Digital Versatile Disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium, along with a corresponding drive.

[0068] The input component 740 is configured to receive information, such as user input. For example, the input component 740 may include, but not be limited to, a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and / or a microphone. Additionally, or alternatively, the input component 740 may include a sensor for sensing information (e.g., a Global Positioning System (GPS), an accelerometer, a gyroscope, and / or an actuator).

[0069] The output component 750 is configured to provide output information from the device 700. For example, the output component 750 may be, but is not limited to, a display, a speaker, an instruction device to an external device, and / or one or more Light-Emitting Diodes (LEDs).

[0070] The communication interface 760 is an interface that provides a communication connection to other devices, such as external devices and internal devices. The connection by the communication interface 760 can be a wired connection, a wireless connection, or a combination of wired and wireless connections, and can be a direct connection or an indirect connection via a communication network that exists between the device 700 and other devices. In other words, the standard of the communication interface 760 is not limited.

[0071] The bus 770 acts as an interconnect between the processor 710, the memory 720, the storage component 730, the input component 740, the output component 750, and the communication interface 760 of the device 700. The bus 770 may include a wired interconnection or a wireless interconnection.

[0072] The number and arrangement of components shown in FIG. 7 are provided as an example. In practice, the device 700 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 7. Additionally, or alternatively, a set of components (e.g., one or more components) of the device 700 may perform one or more functions described as being performed by another set of components of the device 700. Further, one or more method steps described in any of the embodiments may be performed utilizing a plurality of devices 700 in communication with one another.

[0073] In one embodiment, a method is described. The method comprises receiving, by at least one Data Exposure Entity (DEE), a data exposure request to request data associated with one or more network entities and corresponding one or more network services, from at least one of Network Function (NF) and a User Equipment (UE). The method further comprises obtaining, by the at least one DEE from a data collection framework connected to the one or more networkentities, the requested data based on the received data exposure request. The method further comprises transmitting, by the at least one DEE, the received data to at least one of the NF and the UE.

[0074] The method as described in

[0073] , wherein transmitting the requested data comprises: pre-processing, by the at least one DEE, the requested data based on a type of data; and transmitting, by the at least one DEE, the pre-processed data to at least one of the NF and the UE.

[0075] The method as described in any of

[0073] -

[0074] , wherein transmitting the requested data comprises: anonymizing, by the at least one DEE, the requested data based on the data exposure request; and transmitting, by the at least one DEE, the anonymized data to at least one of the NF and the UE.

[0076] The method as described in any of

[0073] -

[0075] , wherein anonymizing the requested data comprises: performing, by the at least one DEE, at least one of a data masking, a pseudonymization, a generalization, a data swapping, a data perturbation, and a synthetization on the requested data.

[0077] The method as described in any of

[0073] -

[0076] , wherein obtaining the requested data from the data collection framework comprises: obtaining, by the at least one DEE, the requested data from the data collection framework in an event subscription response.

[0078] The method as described in any of

[0073] -

[0077] , wherein prior to obtaining the requested data from the data collection framework, the method comprises: transmitting, by the at least one DEE , the data exposure request to the data collection framework in an event subscription request.

[0079] The method as described in any of

[0073] -

[0078] , wherein prior to receiving the data exposure request comprises: performing, by the at least one DEE, a registration process with a Network Repository Function (NRF)

[0080] The method as described in any of

[0073] -

[0079] , wherein prior to obtaining the requested data from the data collection framework, the method comprises: authorizing, by the at least one DEE, the data exposure request received from at least one of the NF and the UE.

[0081] The method as described in any of

[0073] -

[0080] , wherein the data exposure request includes at least one of a time based request, a user equipment (UE) based request, an identification based request, an inference data based request, a training data based request, a sensing data based request, and an anonymization based request.

[0082] In another embodiment, an apparatus is described. The apparatus is configured to receive a data exposure request to request data associated with one or more network entities and corresponding one or more network services, from at least one of a Network Function and a User Equipment (UE). The apparatus is further configured to obtain, from a data collection framework connected to the one or more network entities, the requested data based on the received data exposure request. The apparatus is further configured to transmit the received data to at least one of the NF and the UE.

[0083] The apparatus as described in

[0082] , wherein for transmitting the requested data, the apparatus is configured to: pre-process the requested data based on a type of data; andtransmit the pre-processed data to at least one of the NF and the UE.

[0084] The apparatus as described in any of

[0082] -

[0083] , wherein for transmitting the requested data, the apparatus is configured to: anonymize the requested data based on the data exposure request; and transmit the anonymized data to at least one of the NF and the UE.

[0085] The apparatus as described in any of

[0082] -

[0084] , wherein the apparatus is configured to obtain the requested data from the data collection framework in an event subscription response.

[0086] The apparatus as described in any of

[0082] -

[0085] , wherein the apparatus is configured to anonymize the requested data by performing at least one of a data masking, a pseudonymization, a generalization, a data swapping, a data perturbation, and a synthetization on the requested data.

[0087] The apparatus as described in any of

[0082] -

[0086] , wherein prior to obtaining the requested data from the data collection framework, the apparatus is configured to transmit the data exposure request to the data collection framework in an event subscription request.

[0088] The apparatus as described in any of

[0082] -

[0087] , wherein prior to receiving the data exposure request, the apparatus is configured to perform a registration process with a Network Repository Function (NRF).

[0089] The apparatus as described in any of

[0082] -

[0088] , wherein prior to obtaining the requested data from the data collection framework, the apparatus is configured to authorize the data exposure request received from at least one of the NF and the UE.

[0090] The apparatus as described in any of

[0082] -

[0089] , wherein the data exposure request includes at least one of a time based request, a user equipment (UE) based request, an identificationbased request, an inference data based request, a training data based request, a sensing data based request, and an anonymization based request.

[0091] The apparatus as described in any of

[0082] -

[0090] , wherein the apparatus corresponds to a Data Exposure Entity (DEE).

[0092] In one embodiment, a non-transitory computer-readable medium storing instructions is described. The non-transitory computer-readable medium stores instructions. The instructions comprises one or more instructions that are executed by at least one Data Exposure Entity (DEE). The at least one DEE comprises one or more processors. The instructions cause the one or more processors to receive a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from at least one of a Network Function and a User Equipment (UE). The instructions further cause the one or more processors to obtain, from a data collection framework connected to the one or more network entities, the requested data based on the received data exposure request. The instructions further cause the one or more processors to transmit the received data to at least one of the NF and the UE.

[0093] It is understood that terms including “unit” or “module” at the end may refer to the unit for processing at least one function or operation and may be implemented in hardware, software, or a combination of hardware and software.

[0094] Accordingly, the present disclosure provides techniques for exposing data to the at least one of the NF and the UE.

[0095] Embodiments of the present disclosure offer several significant commercial and technical advantages, for example:

[0096] Enhancing security and privacy of data: Having a unified data framework, i.e., DEE 109 for data collection, processing, sharing, anonymizing, and exposing to the 3rdparties results in enhancing the privacy and security of the data. Further, sensitive information is protected while enabling valuable data exposure.

[0097] Data interoperability and collaboration: Providing a uniform way of data sharing and anonymizing across the global community of operators and service providers, promotes data interoperability and collaboration.

[0098] Revenue increase: Enabling the operators to monetize vast data assets by offering anonymized data services to 3rdparties using the DEE.

[0099] Increased innovation: Fostering innovation by providing valuable data to enterprises, governments, researchers, and advertisers.

[0100] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0101] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein.

[0102] Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

[0103] Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

[0104] 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 at least one embodiment, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

Claims

WE CLAIM:

1. An apparatus configured to: receive a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from at least one of a Network Function (NF) and a User Equipment (UE); obtain, from a data collection framework connected to the one or more network entities, the requested data based on the received data exposure request; and transmit the received data to at least one of the NF and the UE.

2. The apparatus as claimed in claim 1, wherein for transmitting the requested data, the apparatus is configured to: pre-process the requested data based on a type of data; and transmit the pre-processed data to at least one of the NF and the UE.

3. The apparatus as claimed in claim 1, wherein for transmitting the requested data, the apparatus is configured to: anonymize the received data based on the data exposure request; and transmit the anonymized data to at least one of the NF and the UE.

4. The apparatus as claimed in claim 3, wherein the apparatus is configured to anonymize the requested data by performing at least one of a data masking, a pseudonymization, a generalization, a data swapping, a data perturbation, and a synthetization on the requested data.

5. The apparatus as claimed in claim 1, wherein the apparatus is configured to obtain the requested data from the data collection framework in an event subscription response.

6. The apparatus as claimed in claim 1, wherein prior to obtaining the requested data from the data collection framework, the apparatus is configured to transmit the data exposure request to the data collection framework in an event subscription request.

7. The apparatus as claimed in claim 1, wherein prior to receiving the data exposure request, the apparatus is configured to perform a registration process with a Network Repository Function (NRF).

8. The apparatus as claimed in claim 1, wherein prior to obtaining the requested data from the data collection framework, the apparatus is configured to authorize the data exposure request received from at least one of the NF and the UE.

9. The apparatus as claimed in claim 1, wherein the data exposure request includes at least one of a time based request, a user equipment (UE) based request, an identification based request, an inference data based request, a training data based request, a sensing data based request, and an anonymization based request.

10. The apparatus as claimed in claim 1 , wherein the apparatus corresponds to a Data ExposureEntity (DEE).

11. A method comprising: receiving, by at least one Data Exposure Entity (DEE), a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from at least one of a Network Function (NF) and a User Equipment (UE); obtaining, by the at least one DEE from a data collection framework connected to the one or more network entities, the requested data based on the received data exposure request; and transmitting, by the at least one DEE, the received data to at least one of the NF and the UE.

12. The method as claimed in claim 11, wherein transmitting the requested data comprises: pre-processing, by the at least one DEE, the requested data based on a type of data; and transmitting, by the at least one DEE, the pre-processed data to at least one of the NF and the UE.

13. The method as claimed in claim 11, wherein transmitting the requested data comprises: anonymizing, by the at least one DEE, the requested data based on the data exposure request; and transmitting, by the at least one DEE, the anonymized data to at least one of the NF and the UE.

14. The method as claimed in claim 13, wherein anonymizing the requested data comprises:performing, by the at least one DEE, at least one of a data masking, a pseudonymization, a generalization, a data swapping, a data perturbation, and a synthetization on the requested data.

15. The method as claimed in claim 11, wherein obtaining the requested data from the data collection framework comprises: obtaining, by the at least one DEE, the requested data from the data collection framework in an event subscription response.

16. The method as claimed in claim 1, wherein prior to obtaining the requested data from the data collection framework, the method comprises: transmitting, by the at least one DEE, the data exposure request to the data collection framework in an event subscription request.

17. The method as claimed in claim 11, wherein prior to receiving the data exposure request comprises: performing, by the at least one DEE, a registration process with a Network Repository Function (NRF).

18. The method as claimed in claim 11, wherein prior to obtaining the requested data from the data collection framework, the method comprises: authorizing, by the at least one DEE, the data exposure request received from at least one of the NF and the UE.

19. The method as claimed in claim 11, wherein the data exposure request includes at least one of a time based request, a user equipment (UE) based request, an identification based request, an inference data based request, a training data based request, a sensing data based request, and an anonymization based request.

20. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by at least one Data Exposure Entity (DEE), at least one DEE comprising one or more processors, cause the one or more processors to: receive a data exposure request to request data associated with at least one of one or more network entities and corresponding one or more network services, from at least one of a Network Function (NF) and a User Equipment (UE); obtain, from a data collection framework connected to the one or more network entities, the requested data based on the received data exposure request; and transmit the received data to the at least one of the NF and the UE.

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