Differential privacy-based protection of data in communication network environment

Abstract

Techniques are disclosed for differential privacy-based protection of data in a communication network environment. In one example, from a user equipment perspective, a method comprises receiving, by user equipment, a data privacy configuration, wherein the data privacy configuration specifies a differential privacy mechanism to be applied by the user equipment prior to sending data to a communication network for use by a third-party application. The method further comprises applying, at the user equipment, the differential privacy mechanism to the data, and sending, from the user equipment, the data to the communication network in response to application of the differential privacy mechanism. The method may further comprise applying a portion of differential privacy at the network (e.g., gNB) in order to achieve a total differential privacy for a configured list of information elements.

Description

[0001] DIFFERENTIAL PRIVACY-BASED PROTECTION OF DATA IN COMMUNICATION NETWORK ENVIRONMENT

[0002] Field

[0003] The field relates generally to communication networks, and more particularly, but not exclusively, to security management in such communication networks.

[0004] Background

[0005] This section introduces aspects that may be helpful in facilitating a better understanding of the inventions. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is in the prior art or what is not in the prior art.

[0006] Fourth generation (4G) wireless mobile telecommunications technology, also known as Long Term Evolution (LTE) technology, was designed to provide high-capacity mobile multimedia with high data rates particularly for human interaction. Next generation or fifth generation (5G) technology is intended to be used not only for human interaction, but also for machine type communications in so-called Internet of Things (loT) networks.

[0007] While 5G networks are intended to enable massive loT services (e.g., very large numbers of limited capacity devices) and mission-critical loT services (e.g., requiring high reliability), improvements over legacy mobile communication services are supported in the form of enhanced mobile broadband (eMBB) services providing improved wireless Internet access for mobile devices.

[0008] In an example communication system, user equipment (5G UE in a 5G network or, more broadly, a UE) such as a mobile terminal (subscriber) communicates over an air interface with a base station or access point of an access network referred to as a 5G AN in a 5G network. The access point (e.g., gNB) is illustratively part of an access network of the communication system.

[0009] For example, in a 5G network, the access network referred to as a 5G AN is described in 5G Technical Specification (TS) 23.501, entitled “Technical Specification Group Services and System Aspects; System Architecture for the 5G System,” and TS 23.502, entitled “Technical Specification Group Services and System Aspects; Procedures for the 5G System (5GS),” the disclosures of which are incorporated by reference herein in their entireties. In general, the access point (e.g., gNB) provides access for the UE to a core network (CN or 5GC), which then provides access for the UE to other UEs and / or a data network such as a packet data network (e.g., Internet).

[0010] TS 23.501 goes on to define a 5G Service-Based Architecture (SBA) which models services as network functions (NFs) that communicate with each other using representational state transfer application programming interfaces (Restful APIs).

[0011] Furthermore, TS 33.501, entitled “Technical Specification Group Services and System Aspects; Security Architecture and Procedures for the 5G System,” the disclosure of which is incorporated by reference herein in its entirety, further describes security management details associated with a 5G network.

[0012] Security management is an important consideration in any communication network environment. However, due to continuing attempts to improve the architectures and protocols associated with a 5G network in order to increase network efficiency and / or subscriber convenience, security management issues associated with data exchanged in the communication network environment can present a significant challenge. For example, implementing data privacy algorithms in the communication network environment is a technical challenge.

[0013] Summary

[0014] Illustrative embodiments provide techniques for differential privacy-based protection of data in a communication network environment.

[0015] In one illustrative embodiment, from a user equipment perspective, a method comprises receiving, by user equipment, a data privacy configuration, wherein the data privacy configuration specifies a differential privacy mechanism to be applied by the user equipment prior to sending data to a communication network for use by a third-party application. The method further comprises applying, at the user equipment, the differential privacy mechanism to the data, and sending, from the user equipment, the data to the communication network in response to application of the differential privacy mechanism.

[0016] In another illustrative embodiment, from a network operator entity perspective, a method comprises generating, by a network operator entity, a data privacy configuration to be applied by a plurality of user equipment and a communication network through which the plurality of user equipment send data to a third-party application, wherein the data privacy configuration specifies a differential privacy mechanism to be applied to the data prior to sending the data to the third-party application. The method further comprises sending, from the network operator entity, the data privacy configuration to the plurality of user equipment and the communication network.

[0017] In a further illustrative embodiment, from an access point of a communication network perspective, a method comprises receiving, at an access point, a data privacy configuration, wherein the data privacy configuration specifies a differential privacy mechanism with a total data randomization to be applied prior to sending data, received from a plurality of user equipment, to a third-party application. The method further comprises applying, by the access point, the differential privacy mechanism to the data, wherein a portion of the total data randomization applied to the data by the access point depends on a portion of the total data randomization applied to the data by each of the plurality of user equipment. The method further comprises sending, from the access point, the data to the third-party application in response to application of the differential privacy mechanism.

[0018] In yet another illustrative embodiment, from a third-party application entity perspective, a method comprises receiving, at a third-party application entity, data from a communication system, wherein the data comprises one or more of first data sourced from a plurality of user equipment and second data sourced from an access point associated with the communication network, and wherein the data comprises a total data randomization applied in accordance with a differential privacy mechanism. The method further comprises comparing, by the third-party application entity, the data to a data quality metric, and utilizing, by the third- party application entity, at least a portion of the data based on the data quality metric comparison.

[0019] In an additional illustrative embodiment, from a communication network entity perspective, a method comprises receiving, at a communication network entity, a data privacy configuration to be applied by a plurality of user equipment and an access point through which the plurality of user equipment send data to a third-party application, wherein the data privacy configuration specifies a differential privacy mechanism to be applied to the data prior to sending the data to the third-party application. The method further comprises generating, by the communication network entity, a specific data privacy configuration, based on the received data privacy configuration, wherein the specific data privacy configuration specifies a set of privacy parameters and data elements to which the set of privacy parameters is to be applied. The method further comprises sending, from the communication network entity, the specific data privacy configuration to the plurality of user equipment and the access point.

[0020] Further illustrative embodiments are provided in the form of a non-transitory computer- readable storage medium having embodied therein executable program code that when executed by a processor causes the processor to perform the above steps. Still further illustrative embodiments comprise an apparatus with a processor and a memory configured to perform the above steps.

[0021] Advantageously, illustrative embodiments provide techniques for implementing a data privacy configuration in a communication network environment that dynamically (e.g., adjustable) and collaboratively (e.g., between entities in the communication network environment) considers a trade-off between user equipment data size, total local noise needed / desired to be applied, and the pooled global noise needed / desired to be applied.

[0022] These and other features and advantages of embodiments described herein will become more apparent from the accompanying drawings and the following detailed description.

[0023] Brief Description of the Drawings

[0024] FIG. 1 illustrates a communication network environment with which one or more illustrative embodiments may be implemented.

[0025] FIG. 2 illustrates user equipment and entities with which one or more illustrative embodiments may be implemented.

[0026] FIGS. 3A and 3B illustrate a procedure for differential privacy-based protection of data in a communication network environment according to an illustrative embodiment.

[0027] Detailed Description

[0028] Embodiments will be illustrated herein in conjunction with example communication systems and associated techniques for security management in communication systems. It should be understood, however, that the scope of the claims is not limited to particular types of communication systems and / or processes disclosed. Embodiments can be implemented in a wide variety of other types of communication systems, using alternative processes and operations. For example, although illustrated in the context of wireless cellular systems utilizing the 3rd Generation Partnership Project (3GPP) system elements such as a 3GPP next generation system (5G), the disclosed embodiments can be adapted in a straightforward manner to a variety of other types of communication systems such as 6G communication systems.

[0029] In accordance with illustrative embodiments implemented in a 5G communication system environment, one or more 3GPP technical specifications (TS) and technical reports (TR) may provide further explanation of network elements / functions and / or operations that may interact with parts of the inventive solutions, e.g., the above-referenced 3GPP TS 23.501, TS 23.502, and TS 33.501. Other 3GPP TS / TR documents may provide other details that one of ordinary skill in the art will realize. Note that 3GPP TS / TR documents are non-limiting examples of communication network standards (e.g., specifications, procedures, reports, requirements, recommendations, and the like). However, while well-suited for 5G-related 3GPP standards, embodiments are not necessarily intended to be limited to any particular standards.

[0030] It is to be understood that the term 5G network, and the like (e.g., 5G system, 5G communication system, 5G environment, 5G communication environment etc.), in some illustrative embodiments, may be understood to comprise all or part of an access network and all or part of a core network. However, the term 5G network, and the like, may also occasionally be used interchangeably herein with the term 5GC network, and the like, without any loss of generality, since one of ordinary skill in the art understands any distinctions.

[0031] Prior to describing illustrative embodiments, a general description of certain main components of a 5G network will be described below in the context of FIGS. 1 and 2.

[0032] FIG. 1 shows a communication system 100 within which illustrative embodiments are implemented. It is to be understood that the elements shown in communication system 100 are intended to represent some main functions provided within the system, e.g., control plane functions, user plane functions, etc. As such, the blocks shown in FIG. 1 reference specific elements in 5G networks that provide some of these main functions. However, other network elements may be used to implement some or all of the main functions represented. Also, it is to be understood that not all functions of a 5G network are depicted in FIG. 1. Rather, at least some functions that facilitate an explanation of illustrative embodiments are represented. Subsequent figures may depict some additional elements / functions (i.e., network entities).

[0033] Accordingly, as shown, communication system 100 comprises user equipment (UE) 102 that communicates via an air interface 103 with an access point 104. It is to be understood that UE 102 may use one or more other types of access points (e.g., access functions, networks, etc.) to communicate with the 5GC network other than a gNB. By way of example only, the access point 104 may be any 5G access network (gNB), an untrusted non-3GPP access network that uses an Non-3GPP Interworking Function (N3IWF), a trusted non-3GPP network that uses a Trusted Non-3GPP Gateway Function (TNGF) or wireline access that uses a Wireline Access Gateway Function (W-AGF) or may correspond to a legacy access point (e.g., eNB). Furthermore, access point 104 may be a wireless local area network (WLAN) access point as will be further explained in illustrative embodiments described herein.

[0034] The UE 102 may be a mobile station, and such a mobile station may comprise, by way of example, a mobile telephone, a computer, an loT device, or any other type of communication device. The term “user equipment” as used herein is therefore intended to be construed broadly, so as to encompass a variety of different types of mobile stations, subscriber stations or, more generally, communication devices, including examples such as a combination of a data card inserted in a laptop or other equipment such as a smart phone. Such communication devices are also intended to encompass devices commonly referred to as access terminals.

[0035] In one illustrative embodiment, UE 102 is comprised of a Universal Integrated Circuit Card (UICC) part and a Mobile Equipment (ME) part. The UICC is the user-dependent part of the UE and contains at least one Universal Subscriber Identity Module (USIM) and appropriate application software. The USIM securely stores a permanent subscription identifier and its related key, which are used to uniquely identify and authenticate subscribers to access networks. The ME is the user-independent part of the UE and contains terminal equipment (TE) functions and various mobile termination (MT) functions. Alternative illustrative embodiments may not use UICC-based authentication, e.g., a Non-Public (Private) Network (NPN).

[0036] Note that, in one example, the permanent subscription identifier is an International Mobile Subscriber Identity (IMSI) unique to the UE. In one embodiment, the IMSI is a fixed 15 -digit length and consists of a 3 -digit Mobile Country Code (MCC), a 3 -digit Mobile Network Code (MNC), and a 9-digit Mobile Station Identification Number (MSIN). In a 5G communication system, an IMSI is referred to as a Subscription Permanent Identifier (SUPI). In the case of an IMSI as a SUPI, the MSIN provides the subscriber identity. Thus, only the MSIN portion of the IMSI typically needs to be encrypted. The MNC and MCC portions of the IMSI provide routing information, used by the serving network to route to the correct home network. When the MSIN of a SUPI is encrypted, it is referred to as Subscription Concealed Identifier (SUCI). Another example of a SUPI uses a Network Access Identifier (NAI). NAI is typically used for loT communication.

[0037] The access point 104 is illustratively part of a radio access network or RAN of the communication system 100. Such a radio access network may comprise, for example, a 5G System having a plurality of base stations. Components of a radio access network may, more generally, be considered “radio access entities.”

[0038] Further, the access point 104 in this illustrative embodiment is operatively coupled to an Access and Mobility Management Function (AMF / SEAF) 106. In a 5G network, the AMF / SEAF supports, inter alia, mobility management (MM) and security anchor (SEAF) functions.

[0039] AMF / SEAF 106 in this illustrative embodiment is operatively coupled to (e.g., uses the services of) other network functions 108. As shown, some of these other network functions 108 include, but are not limited to, an Authentication Server Function (AUSF), a Unified Data Management (UDM) function, an Application Function (AF), and a Network Data Analytics Function (NWDAF). These listed network function examples are typically implemented in the home network of the UE subscriber, further explained below. Note that, in a 5GC network, the 4G function of the HSS (home subscriber server) is split into the AUSF, UDM, and a Unified Data Repository (UDR, not expressly shown) functions. Typically, AUSF authenticates UEs and provides any needed cryptographic keys, while UDR stores the user data and UDM manages the user data. The AF exposes the application layer for interaction with 5G NFs and network resources. The NWDAF is a 5G network function that collects data from various 5GC network functions, application functions, as well as operations, administration, and management (0AM) systems, and operational support systems. The NWDAF is configured to facilitate the way 5GC data is produced and consumed, as well as to generate analytical insights and take actions based on the analytical insights. It is also realized that third-party applications can also be enabled to operate with one or more network functions in the 5GC.

[0040] Other network functions 108 may include network functions that can act as service producers (NFp) and / or service consumers (NFc). Note that any network function can be a service producer for one service and a service consumer for another service. Further, when the service being provided includes data, the data-providing NFp is referred to as a data producer, while the data-requesting NFc is referred to as a data consumer. A data producer may also be an NF that generates data by modifying or otherwise processing data produced by another NF. Note that NFs may, more generally, be considered “network entities.”

[0041] Note that a UE, such as UE 102, is typically subscribed to what is referred to as a Home Public Land Mobile Network (HPLMN) in which some or all of the functions 106 and 108 reside. Alternatively the UE, such as UE 102, may receive services from an NPN where these functions may reside. The HPLMN is also referred to as the Home Environment (HE). If the UE is roaming (not in the HPLMN), it is typically connected with a Visited Public Land Mobile Network (VPLMN) also referred to as a visited network, while the network that is currently serving the UE is also referred to as a serving network. In the roaming case, some of the network functions 106 and 108 can reside in the VPLMN, in which case, functions in the VPLMN communicate with functions in the HPLMN as needed. However, in a non-roaming scenario, access and mobility management functions 106 and the other network functions 108 reside in the same communication network, i.e., HPLMN. Embodiments described herein, unless otherwise specified, are not necessarily limited by which functions reside in which PLMN (i.e., HPLMN or VPLMN).

[0042] The access point 104 is also operatively coupled (via one or more of functions 106 and / or 108) to a Session Management Function (SMF) 110, which is operatively coupled to a User Plane Function (UPF) 112. UPF 112 is operatively coupled to a Packet Data Network, e.g., Internet 114. Note that the thicker solid lines in this figure denote a user plane (UP) of the communication network, as compared to the thinner solid lines that denote a control plane (CP) of the communication network. It is to be appreciated that network (e.g., Internet) 114 in FIG. 1 may additionally or alternatively represent other network infrastructures including, but not limited to, cloud computing infrastructure and / or edge computing infrastructure. Further typical operations and functions of such network elements are not described here since they are not the focus of the illustrative embodiments and may be found in appropriate 3GPP 5G documentation. Note that functions shown in 106, 108, 110 and 112 are examples of network functions (NFs).

[0043] It is to be appreciated that this particular arrangement of system elements is an example only, and other types and arrangements of additional or alternative elements can be used to implement a communication system in other embodiments. For example, in other embodiments, the communication system 100 may comprise other elements / functions not expressly shown herein. Accordingly, the FIG. 1 arrangement is just one example configuration of a wireless cellular system, and numerous alternative configurations of system elements may be used. For example, although only single elements / functions are shown in the FIG. 1 embodiment, this is for simplicity and clarity of description only. A given alternative embodiment may of course include larger numbers of such system elements, as well as additional or alternative elements of a type commonly associated with conventional system implementations.

[0044] It is also to be noted that while FIG. 1 illustrates system elements as singular functional blocks, the various subnetworks that make up the 5G network are partitioned into so-called network slices. Network slices (network partitions) are logical networks that provide specific network capabilities and network characteristics that can support a corresponding service type, optionally using network function virtualization (NFV) on a common physical infrastructure. With NFV, network slices are instantiated as needed for a given service, e.g., eMBB service, massive loT service, and mission-critical loT service. A network slice or function is thus instantiated when an instance of that network slice or function is created. In some embodiments, this involves installing or otherwise running the network slice or function on one or more host devices of the underlying physical infrastructure. UE 102 is configured to access one or more of these services via access point 104.

[0045] FIG. 2 is a block diagram illustrating computing architectures for various participants in methodologies according to illustrative embodiments. More particularly, system 200 is shown comprising user equipment (UE) 202 and a plurality of entities 204-1, . . . . , 204-N. For example, in illustrative embodiments and with reference back to FIG. 1, UE 202 can represent UE 102, while entities 204-1, . . . , 204-N can represent functions 106 and 108 (i.e., network entities), as well as access point 104 (i.e., radio access entity). It is to be appreciated that the UE 202 and entities 204-1, . . . . , 204-N are configured to interact to provide security management and other techniques described herein.

[0046] The user equipment 202 comprises a processor 212 coupled to a memory 216 and interface circuitry 210. The processor 212 of the user equipment 202 includes a security management processing module 214 that may be implemented at least in part in the form of software executed by the processor. The security management processing module 214 performs security management described in conjunction with subsequent figures and otherwise herein. The memory 216 of the user equipment 202 includes a security management storage module 218 that stores data generated or otherwise used during security management operations.

[0047] Each of the entities (individually or collectively referred to herein as 204) comprises a processor 222 (222-1, . . . , 222-N) coupled to a memory 226 (226-1, . . . , 226-N) and interface circuitry 220 (220-1, . . . , 220-N). Each processor 222 of each entity 204 includes a security management processing module 224 (224-1, . . . , 224-N) that may be implemented at least in part in the form of software executed by the processor 222. The security management processing module 224 performs security management operations described in conjunction with subsequent figures and otherwise herein. Each memory 226 of each entity 204 includes a security management storage module 228 (228-1, . . . , 228-N) that stores data generated or otherwise used during security management operations.

[0048] The processors 212 and 222 may comprise, for example, microprocessors such as central processing units (CPUs), application-specific integrated circuits (ASICs), digital signal processors (DSPs) or other types of processing devices, as well as portions or combinations of such elements.

[0049] The memories 216 and 226 may be used to store one or more software programs that are executed by the respective processors 212 and 222 to implement at least a portion of the functionality described herein. For example, security management operations and other functionality as described in conjunction with subsequent figures and otherwise herein may be implemented in a straightforward manner using software code executed by processors 212 and 222.

[0050] A given one of the memories 216 and 226 may therefore be viewed as an example of what is more generally referred to herein as a computer program product or still more generally as a processor-readable storage medium that has executable program code embodied therein. Other examples of processor-readable storage media may include disks or other types of magnetic or optical media, in any combination. Illustrative embodiments can include articles of manufacture comprising such computer program products or other processor-readable storage media.

[0051] Further, the memories 216 and 226 may more particularly comprise, for example, electronic random- access memory (RAM) such as static RAM (SRAM), dynamic RAM (DRAM) or other types of volatile or non-volatile electronic memory. The latter may include, for example, non-volatile memories such as flash memory, magnetic RAM (MRAM), phase- change RAM (PC-RAM) or ferroelectric RAM (FRAM). The term “memory” as used herein is intended to be broadly construed, and may additionally or alternatively encompass, for example, a read-only memory (ROM), a disk-based memory, or other type of storage device, as well as portions or combinations of such devices.

[0052] The interface circuitries 210 and 220 illustratively comprise transceivers or other communication hardware or firmware that allows the associated system elements to communicate with one another in the manner described herein.

[0053] It is apparent from FIG. 2 that user equipment 202 and plurality of entities 204 are configured for communication with each other as security management participants via their respective interface circuitries 210 and 220. This communication involves each participant sending data to and / or receiving data from one or more of the other participants. The term “data” as used herein is intended to be construed broadly, so as to encompass any type of information that may be sent between participants including, but not limited to, identity data, key pairs, key indicators, tokens, secrets, security management messages, registration request / response messages and data, request / response messages, authentication request / response messages and data, metadata, control data, audio, video, multimedia, consent data, other messages, etc.

[0054] It is to be appreciated that the particular arrangement of components shown in FIG. 2 is an example only, and numerous alternative configurations may be used in other embodiments. For example, any given network element / function and / or access point can be configured to incorporate additional or alternative components and to support other communication protocols.

[0055] Other system elements such as access point 104, SMF 110, and UPF 112 may each be configured to include components such as a processor, memory and network interface. Also, entities such as third-party applications and network operators (to be further explained below in the context of FIGS. 3 A and 3B) can participate in methodologies described herein via computing devices configured to include components such as a processor, memory and network interface. These elements and devices need not be implemented on separate standalone processing platforms, but could instead, for example, represent different functional portions of a single common processing platform.

[0056] More generally, FIG. 2 can be considered to represent processing devices configured to provide respective security management functionalities and operatively coupled to one another in a communication system. By way of example only, all or parts of each of UE 202 and the plurality of entities 204 (e.g., processor and memory) can be considered examples of means for performing one or more operations, one or more steps, one or more functions, one or more processes, etc. as described herein.

[0057] As mentioned above, the 3GPP TS 23.501 defines the 5GC network architecture as service-based, e.g., Service-Based Architecture (SBA). It is realized herein that in deploying different NFs, there can be many situations where an NF may need to interact with an entity external to the SBA-based 5GC network (e.g., including the corresponding PEMN(s), e.g., HPEMN and VPEMN). Thus, the term “internal” as used herein illustratively refers to operations and / or communications within the SBA-based 5GC network (e.g., SBA-based interfaces) and the term “external” illustratively refers to operations and / or communications outside the SBA-based 5GC network (non-SBA interfaces).

[0058] Given the above general description of some features of a 5GC network, problems with existing security approaches in the context of differential privacy-based protection of data in a communication network environment, and solutions proposed in accordance with illustrative embodiments, will now be described herein below.

[0059] Differential privacy provides guarantees on the privacy of the data in exchange for reducing the accuracy of a utility function that needs the data. Randomizing a function of the data (functional perturbation), the pure raw randomization of the data (input perturbation) or randomizing the output of a function (output perturbation), are among the widely used differential privacy mechanisms. It is realized herein that the differential privacy mechanisms could be improved if it was known which function is to be computed. In other words, it is realized herein that having the knowledge of the function can improve both utility and privacy.

[0060] Differential privacy was originally proposed to give a formal guarantee of privacy in case commonly used anonymization techniques (such as k-anonymity and 1-divergence) are not sufficient. Differential privacy is a technique of such a statistical nature that once an algorithm or data is protected with differential privacy, no matter how the output of the algorithm or the data is further processed, the privacy of individuals in the data set stays protected. The original motivation for proposing this strong definition of privacy came from the observation that combining data from different sources can break privacy. Nowadays, for example, the United States Census Bureau has adopted differential privacy in privacy protection and several major customer data-driven companies use it. Differential privacy can also help to comply with data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

[0061] A differential privacy guarantee provides a mathematical upper bound for the risk of leaking information about an individual and allows a user to quantify a precise level of acceptable individual level risk. Differential privacy is described using probability theory and mathematically the definition is given as follows. Assume that two data sets X and Y are neighbors if you get one by replacing a single data entry in the other. Let e > 0 and 6 I [0,1] (e and 6 are the differential privacy parameters). A randomized function M(X) : XN-> Jk is (e, 5) -DP (DP standing for differential privacy here) or if for every pair of neighboring data sets X and Y and for every outcome S c R we have:

[0062] P(M(X) e S) < e£P(M(y) e S) + 6.

[0063] The parameter 8 can be interpreted as the probability of a total data breach, and the smaller the s, the less probable it is to tell whether the output originated from data sets X or Y, i.e., the less probable it is to notice the presence of any individual and the more privacypreserving is the mechanism.

[0064] There is a fundamental relationship in some mechanisms of differential privacy with the number of data set entries and the noise variance needed to provide a certain level of (s, 8). The relationship raises a concept referred to as the small data size problem, wherein the more data, the less noise is needed to protect an individual’s privacy. This problem appears in the federated learning setting, where N sites each with D number of data points are publishing functions of their data set. If each user adds noise proportional to its own data set size to protect its privacy with (e, 8), the aggregated final data set at the network side will end up with a noise variance N time larger than the case where all of the YnDn- ND was at one central point with (e, 8) -DP. There are some techniques developed to collect this data without compromising privacy and security of the data set; however, most of them either assume a trusted third party or they use homomorphic encryption, which is very computationally resource hungry.

[0065] Moreover, it is further realized herein that using differential privacy in wireless networks is beneficial. For example, to protect the signaling that is required for communication by the UEs that are participating in a NWDAF and a third-party artificial intelligence / machine learning (AI / ML) engine or neural network model trainer, the communication network could be a regulator. Less noise may be required when many UEs are participating in the AI / ML- based solution. Existing solutions, among other technical deficiencies, do not provide flexibility in terms of allowing such configurations and decisions from the network side.

[0066] Further, it is realized herein that the gNB may be sending some data independently to AI / ML applications. This data may be privacy sensitive for operators. For example, a cell name or a cell identifier (ID) may reveal the location of the base transceiver station (BTS). If a third-party AI / ME application is implemented, country specific privacy aspects may also be important. This may include operator’s data which is considered privacy sensitive for the operator. For example, operators in certain countries may not want the cell name to be known by consumers outside the country. If the public cloud hosting the AI / ME application is located outside the country, network operators may also want to ensure privacy of network data (e.g., cell names). In such cases, BTSs or other NFs may also need to add noise -using differential privacy.

[0067] It is further realized herein that the network can serve as a trusted third party that enhances the privacy-utility trade-off by dealing with the above-mentioned small data size problem. If one uses federated learning with a Gaussian mechanism with 100 UEs, the gain in utility (accuracy of training) is 100-fold. In other words, it is required to add noise with 100 times smaller variance.

[0068] Ensuring privacy of data shared by UEs for AI / ML applications in wireless networks (at 0AM, wireless edge, centralized cloud, third-party application) is a problem which is not yet solved in 3GPP and Open RAN (ORAN) standardization bodies.

[0069] Illustrative embodiments address the above and other technical challenges and / or technical deficiencies in existing data privacy approaches in a communication network environment by providing dynamic and collaborative differential privacy-based protection of data (e.g., sensitive data). More particularly, one or more illustrative embodiments, the UE and the gNB are configured to collaborate on producing data with differential privacy enabled.

[0070] A reason for involving the gNB is due to the above-mentioned the small data set problem. If the UE alone tries to provide a given (c,5)-DP, then the amount of additive noise variance in the practical and widely accepted method of a Gaussian mechanism would be inversely proportional to the number of datapoints that the specific UE has. However, it is realized herein that if data of a plurality of UEs is pooled at the gNB and then noise is added, then the amount of required noise dramatically decreases (proportional to the number of UEs). Therefore, illustrative embodiments enable the UE to add some relatively small local noise, for a minimum privacy guarantee, and the rest of the required noise is added by the gNB once all other UEs upload their data.

[0071] Advantageously, illustrative embodiments, inter alia: (i) allow for orders of magnitude gain in learning and utility of data by adding less noise; (ii) strategically place the network as a trusted third-party to handle the privacy aspect of the data for the UE and the AI / ML application; (iii) allow the AI / ML application to know which datasets have less noise (higher quality of data) thus allowing for better and faster training of the neural network (i.e., more energy efficient training of the neural network based on this knowledge); and (iv) signaling is concise and allows for coordination and collaboration among the data-holders (UE) and the data processors (AI / ML application).

[0072] Accordingly, in one or more illustrative embodiments, global (e, 8) is configured on UEs from the network. The UE decides finally how much (e, 8) it uses and communicates to the network when scrambled data is used. Note that the term (e, 8) -DP illustratively refers to the amount of (e, 8) defined noise introduced using a particular differential privacy mechanism.

[0073] The network shares the possible (e, 8) -DP that can be provided based on the number of active UEs. Each UE can have an independent privacy target (e;, 8j)-DP.

[0074] If (ej, 8j) > (e, 8), i.e., the privacy of gNB is stricter than that of UE, then the UE simply sends its data to the gNB; else if (e;, 8;) < (e, 8), then the UE provides (e'j, 8'j)-DP such that the released data set after the gNB provided the additional (e, 8)-DP becomes (e,, 8;).

[0075] The gNB collects data from all the UEs. Based on the number of UEs, Nueand the target (e, 8) -DP, the gNB performs a differential privacy mechanism to achieve the target, e.g., the gNB samples from a Gaussian noise distribution of (0, o2), where o~l / lVue, and adds this to the data.

[0076] At the network and by the knowledge of each UE’s privacy target (e;, 8;), a data quality score is introduced. This data quality metric (score) is disproportionally related to the (e,, 8j). The AI / ML function is made aware of this score by the network operator or network itself, and takes this into account when it comes to training of a neural network.

[0077] FIGS. 3A and 3B illustrate a procedure 300 for differential privacy-based protection of data in a communication network environment according to an illustrative embodiment. More particularly, procedure 300 illustrates a detailed end-to-end message sequence between entities comprising UEs 302, a gNB 304, a serving network 5GC 306, an AI / ML application (or otherwise, any consumer application) 308, a (network) operator 310. Numerical references in procedure 300 correspond to steps 1-15 (including stages, sub-procedures, operations, etc.) performed at or otherwise between entities. Prior to steps 1-15, it is assumed that primary authentication is successful, and non-access stratum (NAS) and access stratum (AS) security contexts are established, for each of UEs 302 with respect to gNB 304 and serving network 5GC 306. Note also that it is assumed that AI / ML application 308 can be trained at a third- party, an 0AM, a wireless edge platform, or a centralized cloud platform.

[0078] In step 1, operator 310 defines a privacy policy which specifies the type of data which is considered privacy sensitive. By way of example only, operator 310 can specify that any data which includes a public Internet Protocol (IP) address, a cell name, subscriber information, and personal data such as an email address, is considered privacy sensitive.

[0079] In step 2, serving network 5GC 306 derives the global and target epsilon and delta (e,, 8j) parameters for data which is shared by UEs 302, and for which UEs 302, as well as gNB 304, will add noise.

[0080] In step 3, serving network 5GC 306 also translates the privacy policy into exact information elements (IES) and data points which UEs 302 and gNB 304 will be collecting for consumption of various AI / ML applications including AI / ML application 308. Note that some reports, e.g., minimization of drive tests (MDT) and radio link failure (RLF) reports, are shared from the UE side and, in these reports, which elements are privacy sensitive is also derived by serving network 5GC 306.

[0081] In step 4, the privacy configurations derived from the above two steps are then sent from serving network 5GC 306 to gNB 304.

[0082] In step 5, gNB 304 extracts the privacy configurations where it is the source of data, and stores the configuration locally for subsequent use in step 11.

[0083] Note that steps 6 to 10 are performed for each of UEs 302 participating in the data collected for AI / ML application 308.

[0084] In step 6, the UE related privacy configuration is sent to each of the UEs 302. As mentioned above, in some reports sent by each of UEs 302 (e.g., MDT and RLF), there may be specific elements which are privacy sensitive (e.g., UE location). Each of UEs 302 should scramble only these elements in such reports.

[0085] In step 7, each of UEs 302 decides on the exact epsilon, delta (e,, 6.) parameters to be used for each IE which needs to be scrambled (noised). In step 8, before sending the data, each of UEs 302 apply differential privacy with the epsilon, delta (e;, 8.) parameters decided in the above step.

[0086] In step 9, each of UEs 302 inform gNB 304 about the privacy parameters (i.e., (ej, 8j) parameters) used for each IE. In this step, a consolidated list with associated privacy parameters can be sent.

[0087] In step 10, each of UEs 302 sends the relevant data to gNB 304 including the scrambled / noised privacy sensitive data.

[0088] In step 11 , gNB 304 itself performs differential privacy on its own data as well as data received from UEs 302. For data received from UEs 302, gNB 304 considers the target epsilon, delta (e,, 6.) parameters and then applies differential privacy.

[0089] Note that steps 12 - 15 are performed for each data set shared to AI / ML application 308.

[0090] In step 12, gNB 304 provides to AI / ML application 308 the details of which IE is privacy protected with how much noise and which differential privacy mechanism / scheme.

[0091] For steps 13-15, AI / ML application 308 makes decisions to use only the data which has sufficient noise to protect privacy but not too much to corrupt the data.

[0092] In step 13, AI / ML application 308 maps the privacy parameters (i.e., (e,, 8j) parameters) to data quality metrics. Proportional to the (e,, 6.) parameters, the quality of the data is determined by gNB 304.

[0093] In step 14, AI / ML application 308 labels the data with a corresponding data quality metric, e.g., a value in a range of data quality values.

[0094] In step 15, the labeled data is used for training and / or inference of a neural network.

[0095] It is to be appreciated that, in order to determine a split between the local UE-added noise and gNB-added noise, the network can share the possible (e, 8)-DP (i.e., total data randomization) that can be provided based on the number of active UEs. Recall, as mentioned above, each UE can have an independent privacy target (e;, 8j)-DP. If (e;, 8;) > (e, 8), i.e., the privacy of gNB is stricter than that of UE, then UE simply sends its data to the gNB. Else if (Cj, 8j) < (e, 8), then the UE provides (e'j, 8'j)-DP (i.e., a UE portion of the total data randomization) such that the released data set, after the gNB provides the additional (e, 8) -DP (i.e., a gNB portion of the total data randomization), becomes (e,, 8j).

[0096] While there are different ways to arrive at (e, 8)-DP, only one way is to add noise. If the method is fixed, then the (e'j, 8'i)-DP can be computed. As an example, below, it is assumed that both the gNB and UE use a Gaussian mechanism. Then, the above steps determine how much noise each UE has to add to their data given the amount that the gNB is providing. For a fixed value of (e, 8) -DP, the variance of the noise needed can be computed as:

[0097] A [ 1.25

[0098] If the (ej, 8;) < (e, 8), the variance of the noise needed to achieve (ej, 8i)-DP can be computed using the above formula as:

[0099] Then, a- = oL— o. gives the amount of noise needed to be added locally.

[0100] Note that in the above example, properties of independent Gaussian random variables are used. However, other mechanisms can be used, e.g., Laplace mechanism, randomized response, etc. Note that the gNB does not need to reveal the mechanism it uses.

[0101] Note that there may be a straggler UE problem with the number of active UEs in each round. That is, if a subset of UEs fail to participate and do not share their results, then the gNB needs to add more noise to achieve the same (e, 8) -DP. Again, assuming a Gaussian mechanism, A~ = 1 / N is the sensitivity parameter, which depends, inter alia, on the number of participants.

[0102] In other words, at the beginning of every round of data collection, either the gNB waits on the straggler UEs to provide their data, or it re-adjusts its amount of noise based on the new amount of A.

[0103] As used herein, it is to be understood that the term “communication network” in some embodiments can comprise two or more separate communication networks. Further, the particular processing operations and other system functionality described in conjunction with the diagrams described herein are presented by way of illustrative example only and should not be construed as limiting the scope of the disclosure in any way. Alternative embodiments can use other types of processing operations and messaging protocols. For example, the ordering of the steps may be varied in other embodiments, or certain steps may be performed at least in part concurrently with one another rather than serially. Also, one or more of the steps may be repeated periodically, or multiple instances of the methods can be performed in parallel with one another.

[0104] It should again be emphasized that the various embodiments described herein are presented by way of illustrative example only and should not be construed as limiting the scope of the claims. For example, alternative embodiments can utilize different communication system configurations, user equipment configurations, base station configurations, provisioning and usage processes, messaging protocols and message formats than those described above in the context of the illustrative embodiments. These and numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art.

Claims

What is claimed is:

1. An apparatus comprising: means for receiving a data privacy configuration, wherein the data privacy configuration specifies a differential privacy mechanism to be applied by the apparatus prior to sending data to a communication network for use by a third-party application; means for applying the differential privacy mechanism to the data; and means for sending the data to the communication network in response to application of the differential privacy mechanism.

2. The apparatus of claim 1, wherein the differential privacy mechanism applied by the apparatus represents at least a portion of a total data randomization to be applied to the data prior to the data being received by the third-party application.

3. The apparatus of claim 2, wherein means for applying the differential privacy mechanism further comprises means for selecting a set of privacy parameters, associated with the differential privacy mechanism, to be applied to the data, wherein the selected set of privacy parameters affect the portion of the total data randomization to be applied to the data.

4. The apparatus of claim 3, further comprising means for sending the selected set of privacy parameters to the communication network.

5. The apparatus of claim 3, wherein the data privacy configuration further specifies one or more types of data elements to which the selected set of privacy parameters is to be applied.

6. A method comprising: receiving, by user equipment, a data privacy configuration, wherein the data privacy configuration specifies a differential privacy mechanism to be applied by the user equipment prior to sending data to a communication network for use by a third-party application; applying, at the user equipment, the differential privacy mechanism to the data; andsending, from the user equipment, the data to the communication network in response to application of the differential privacy mechanism.

7. An apparatus comprising: means for generating a data privacy configuration to be applied by a plurality of user equipment and a communication network through which the plurality of user equipment send data to a third-party application, wherein the data privacy configuration specifies a differential privacy mechanism to be applied to the data prior to sending the data to the third-party application; and means for sending the data privacy configuration to the plurality of user equipment and the communication network.

8. The apparatus of claim 7, wherein the data privacy configuration further specifies one or more types of data elements to which the differential privacy mechanism is to be applied.

9. The apparatus of claim 7, wherein the data privacy configuration further specifies a total data randomization to be applied to the data prior to sending the data to the third-party application.

10. The apparatus of claim 7, wherein the differential privacy mechanism to be applied to the data prior to sending the data to the third-party application is selected by the apparatus based on the number of user equipment in the plurality of user equipment.

11. A method comprising: generating, by a network operator entity, a data privacy configuration to be applied by a plurality of user equipment and a communication network through which the plurality of user equipment send data to a third-party application, wherein the data privacy configuration specifies a differential privacy mechanism to be applied to the data prior to sending the data to the third-party application; and sending, from the network operator entity, the data privacy configuration to the plurality of user equipment and the communication network.

12. An apparatus comprising: means for receiving a data privacy configuration, wherein the data privacy configuration specifies a differential privacy mechanism with a total data randomization to be applied prior to sending data, received from a plurality of user equipment, to a third-party application; means for applying the differential privacy mechanism to the data, wherein a portion of the total data randomization applied to the data by the apparatus depends on a portion of the total data randomization applied to the data by each of the plurality of user equipment; and means for sending the data to the third-party application in response to application of the differential privacy mechanism.

13. The apparatus of claim 12, further comprising: means for applying the differential privacy mechanism to data sourced by the apparatus; and means for sending the data sourced by the apparatus to the third-party application in response to application of the differential privacy mechanism.

14. The apparatus of claim 13, further comprising means for sending a set of privacy parameters, applied in accordance with the differential privacy mechanism, to the third-party application.

15. The apparatus of claim 12, further comprising means for adjusting the portion of the total data randomization applied to the data by the apparatus based on the number of the plurality of user equipment that provide data to the apparatus.

16. A method comprising: receiving, at an access point, a data privacy configuration, wherein the data privacy configuration specifies a differential privacy mechanism with a total data randomization to be applied prior to sending data, received from a plurality of user equipment, to a third-party application; applying, by the access point, the differential privacy mechanism to the data, wherein a portion of the total data randomization applied to the data by the access point depends on aportion of the total data randomization applied to the data by each of the plurality of user equipment; and sending, from the access point, the data to the third-party application in response to application of the differential privacy mechanism.

17. An apparatus comprising: means for receiving data from a communication system, wherein the data comprises one or more of first data sourced from a plurality of user equipment and second data sourced from an access point associated with the communication network, wherein the data comprises a total data randomization applied in accordance with a differential privacy mechanism; means for comparing the data to a data quality metric; and means for utilizing at least a portion of the data based on the data quality metric comparison.

18. The apparatus of claim 17, further comprising means for receiving a set of privacy parameters applied in accordance with the differential privacy mechanism.

19. The apparatus of claim 17, wherein means for utilizing at least a portion of the data further comprises utilizing the portion of the data in an artificial intelligence / machine learning application.

20. A method comprising: receiving, at a third-party application entity, data from a communication system, wherein the data comprises one or more of first data sourced from a plurality of user equipment and second data sourced from an access point associated with the communication network, wherein the data comprises a total data randomization applied in accordance with a differential privacy mechanism; and comparing, by the third-party application entity, the data to a data quality metric; and utilizing, by the third-party application entity, at least a portion of the data based on the data quality metric comparison.

21. An apparatus comprising:means for receiving a data privacy configuration to be applied by a plurality of user equipment and an access point through which the plurality of user equipment send data to a third-party application, wherein the data privacy configuration specifies a differential privacy mechanism to be applied to the data prior to sending the data to the third-party application; means for generating a specific data privacy configuration, based on the received data privacy configuration, wherein the specific data privacy configuration specifies a set of privacy parameters and data elements to which the set of privacy parameters is to be applied; and means for sending the specific data privacy configuration to the plurality of user equipment and the access point.

22. The apparatus of claim 21, wherein the set of privacy parameters affect respective portions of a total data randomization to be applied to the data elements by the plurality of user equipment and the access point.

23. The apparatus of claim 21, wherein the specific data privacy configuration further specifies which ones of the data elements correspond to the plurality of user equipment and which ones of the data elements correspond to the access point.

24. A method comprising, receiving, at a communication network entity, a data privacy configuration to be applied by a plurality of user equipment and an access point through which the plurality of user equipment send data to a third-party application, wherein the data privacy configuration specifies a differential privacy mechanism to be applied to the data prior to sending the data to the third-party application; generating, by the communication network entity, a specific data privacy configuration, based on the received data privacy configuration, wherein the specific data privacy configuration specifies a set of privacy parameters and data elements to which the set of privacy parameters is to be applied; and sending, from the communication network entity, the specific data privacy configuration to the plurality of user equipment and the access point.

Images