Internet DRAFT - draft-sarischo-6gip-aiml-security-privacy
draft-sarischo-6gip-aiml-security-privacy
Network Working Group B. Sarikaya
Internet-Draft Unaffiliated
Intended status: Standards Track R. Schott
Expires: 12 August 2024 Deutsche Telekom
9 February 2024
Security and Privacy Implications of 3GPP AI/ML Networking Studies for
6G
draft-sarischo-6gip-aiml-security-privacy-00
Abstract
This document provides an overview of 3GPP work on Artificial
Intelligence/ Machine Learning (AI/ML) networking. Application areas
and corresponding proposed modifications to the architecture are
identified. Security and privacy issues of these new applications
need to be identified out of which IETF work could emerge.
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Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Training and Federated Learning . . . . . . . . . . . . . . . 4
3. Architecture . . . . . . . . . . . . . . . . . . . . . . . . 5
4. Security and Privacy . . . . . . . . . . . . . . . . . . . . 6
5. Work Points . . . . . . . . . . . . . . . . . . . . . . . . . 7
5.1. Future Work . . . . . . . . . . . . . . . . . . . . . . . 8
6. Security Considerations . . . . . . . . . . . . . . . . . . . 9
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 9
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 9
9.1. Normative References . . . . . . . . . . . . . . . . . . 9
9.2. Informative References . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
Artificial Intelligence (AI) has historically been defined as the
science and engineering to build intelligent machines capable of
carrying out tasks as humans do. Inspired from the way human brain
works, machine learning (ML) is defined as the field of study that
gives computers the ability to learn without being explicitly
programmed. Since it is believed that the main computational
elements in a human brain are 86 billion neurons, the more popular ML
approaches are using “neural network” as the model. Neural networks
(NN) take their inspiration from the notion that a neuron’s
computation involves a weighted sum of the input values. A
computational neural network contains the neurons in the input layer
which receive some values and propagate them to the neurons in the
middle layer of the network, which is also called a “hidden layer”.
The weighted sums from one or more hidden layers are ultimately
propagated to the output layer, which presents the final outputs of
the network.
Neural networks having more than three layers, i.e., more than one
hidden layer are called deep neural networks (DNN). In contrast to
the conventional shallow-structured NN architectures, DNNs, also
referred to as deep learning, made amazing breakthroughs since 2010s
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in many essential application areas because they can achieve human-
level accuracy or even exceed human accuracy. Deep learning
techniques use supervised and/or unsupervised strategies to
automatically learn hierarchical representations in deep
architectures for classification. With a large number of hidden
layers, the superior performance of DNNs comes from its ability to
extract high-level features from raw sensory data after using
statistical learning over a large amount of data to obtain an
effective representation of an input space. In recent years, thanks
to the big data obtained from the real world, the rapidly increased
computation capacity and continuously-evolved algorithms, DNNs have
become the most popular ML models for many AI applications.
The performance of DNNs is gained at the cost of high computational
complexity. Hence more efficient compute engines are often used,
e.g. graphics processing units (GPU) and network processing units
(NPU). Compared to the inference which only involves the feedforward
process, the training often requires more computation and storage
resources because it involves also the back propagation process.
Many DNN models have been developed over the past two decades. Each
of these models has a different “network architecture” in terms of
number of layers, layer types, layer shapes (i.e., filter size,
number of channels and filters), and connections between layers.
Three popular structures of DNNs: multilayer perceptron (MLPs),
convolution neural networks (CNNs), and recurrent neural networks
(RNNs). Multilayer perceptron (MLP) model is the most basic DNN,
which is composed of a series of fully connected layers. In a fully
connected layer, all outputs are connected to all inputs. Hence MLP
requires a significant amount of storage and computation.
A convolution neural network (CNN) is composed of multiple
convolutional layers. Applying various convolutional filters, CNN
models can capture the high-level representation of the input data,
making it popular for image classification and speech recognition
tasks. Recurrent neural network (RNN) models are another type of
DNNs, which use sequential data feeding. The input of RNN consists
of the current input and the previous samples. RNN models have been
widely used in the natural language processing task on mobile
devices, e.g., language modeling, machine translation, question
answering, word embedding, and document classification.
AI/ML has very many applications, however, two areas have emerged
that involve networking. One is the network optimization, time-
series forecasting, predictive maintenance, Quality of Experience
(QoE) modeling and the other is speech recognition, image
recognition, video processing. In the former, the end device is the
base station and the latter the UE [TR22.874].
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This document aims to present Artificial Intelligence Machine
Learning (AIML) networking issues that may require further protocol
work, mostly on the security and privacy aspects of networking.
2. Training and Federated Learning
Training is a process in which an AI/ML model learns to perform its
given tasks, more specifically, by optimizing the value of the
weights in the DNN. A DNN is trained by inputting a training set,
which are often correctly-labelled training samples. Taking image
classification for instance, the training set includes correctly-
classified images. The training process is repeated iteratively to
continuously reduce the overall loss. Until the loss is below a
predefined threshold, the DNN with high precision is obtained. After
a DNN is trained, it can perform its task by computing the output of
the network using the weights determined during the training process,
which is referred to as inference. In the model inference process,
the inputs from the real world are passed through the DNN. Then the
prediction for the task is output. For instance, the inputs can be
pixels of an image, sampled amplitudes of an audio wave or the
numerical representation of the state of some system or game.
Correspondingly, the outputs of the network can be a probability that
an image contains a particular object.
With continuously improving capability of cameras and sensors on
mobile devices, valuable training data, which are essential for AI/ML
model training, are increasingly generated on the devices. For many
AI/ML tasks, the fragmented data collected by mobile devices are
essential for training a global model. In the traditional
approaches, the training data gathered by mobile devices are
centralized to the cloud datacenter for a centralized training.
In Distributed Learning mode, each computing node trains its own DNN
model locally with local data, which preserves private information
locally. To obtain the global DNN model by sharing local training
improvement, nodes in the network will communicate with each other to
exchange the local model updates. In this mode, the global DNN model
can be trained without the intervention of the cloud datacenter.
In 3GPP Federated Learning (FL) mode, the cloud server trains a
global model by aggregating local models partially-trained by each
end devices. The most agreeable Federated Learning algorithm so far
is based on the iterative model averaging whereby within each
training iteration, a UE performs the training based on the model
downloaded from the AI server using the local training data. Then
the UE reports the interim training results (e.g., gradients for the
DNN) to the cloud server via 5G uplink (UL) channels. The server
aggregates the gradients from the UEs, and updates the global model.
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Next, the updated global model is distributed to the UEs via 5G Data
Link (DL) channels. Then the UEs can perform the training for the
next iteration.
Summarizing, we can say that distributed learning is about having
centralized data but distributing the model training to different
nodes, while federated learning (FL) is about having decentralized
data and training and in effect having a central model [Srini21]
3. Architecture
A new framework for protocols called Service based architecture (SBA)
comprises Network Functions (NFs) that expose services through
RESTful APIs has been defined. There are providers and consumers
(publishers and subscribers) which are new functions in the system
[IsNo20].
3GPP core, i.e., 5GC network, aka mobile core network, which
establishes reliable, secure connectivity to the network for end
users and provides access to its services has a new server function:
The Network Data Analytics Function (NWDAF) provides analytics to 5GC
Network Functions (NFs) and Operations and Management (OAM). An
NWDAF may contain the Analytics logical function (AnLF): A logical
function in NWDAF, which performs inference, derives analytics
information and Model Training logical function (MTLF) which trains
Machine Learning (ML) models and exposes new training services. The
Application AI/ML operation logic is controlled by an Application
Function (AF). Any AF request to the 5G System in the context of 5G
System (5GS) (which consists of the 5GC (5G Core Network), 5G-AN (5G
Access Network) and UE) assistance to Application AI/ML operation
should be authorized by the 5GC [TR23.700-80].
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NWDAF relies on various sources of data input including data from 5G
core NFs, AFs, 5G core repositories, e.g., Network Repository
Function (NRF), Unified Data Management (UDM), etc., and OAM data,
including performance measurements (PMs), Key Performance Indicators
(KPIs), configuration management data and alarms. An NWDAF may
provide in turn analytics output results to 5G core NF, AFs, and OAM.
Optionally, Data Collection Coordination Function (DCCF) and
Messaging Framework Adaptor Function (MFAF) may be involved to
distribute and collect repeated data towards or from various data
sources. Note that AF contains a Network Exposure Function (NEF) if
it is an untrusted AF. NEF may assist the AI/ML application server
in scheduling available UE(s) to participate in the AI/ML operation,
e.g., Federated Learning. Also, 5GC may assist the selection of UEs
to serve as FL clients, by providing a list of target member UE(s),
then subscribing to the NEF to be notified about the subset list of
UE(s) (i.e., list of candidate UE(s)) that fulfill certain filtering
criteria [TR23.700-82].
[TR23.700-82] expands upon the data analytics as a useful tool to
optimize the service offering by predicting events related to the
network or UE conditions. These services however can also assist the
3rd party AI/ML application service provider for the AI/ML model
distribution, transfer, training for various applications (e.g.,
video/speech recognition, robot control, automotive). This takes us
to the concept of the application enablement layer can play role on
the exposure of AI/ML services from different 3GPP domains to the
Application Service Providers (ASP) in a unified manner.
4. Security and Privacy
AI/ML networking raises many security and privacy issues.
[TR23.700-80] and [TR23.700-82] identify a number of key issues and
[TR33.898] presents a study on one of the key issues which will be
detailed here.
[TR23.700-80] studies the exposure of different types of assistance
information such as traffic rate, packet delay, packet loss rate,
network condition changes, candidate FL members, geographical
distribution information, etc., to AF for AI / ML operations. Some
of assistance information could be user privacy sensitive, such as
candidate FL members, geographical distribution information. There
is a need to study how to protect such privacy-related assistance
information. In addition, 5GC needs to determine which assistance
information is required by AF to complete AI/ML operation and to
avoid exposing information that is unnecessary for AI/ML operations.
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Because of the use of Restful API which depend on the use of HTTP
protocol, OAuth [RFC6749] protocol seems to be the natural choice
here for authorization.
One solution can be developed reusing existing mechanism for
authorization of 5GC assistance information exposure to AF. The
solution is based on reusing the OAuth-based authorization mechanism
OAuth [RFC6749] protocol which extends traditional client-server
authentication by providing a third-party client with a token. Since
such token resembles a different set of credentials compared to those
of the resource owner, the device needs not be allowed to use the
resource owner's credentials to access protected resources.
UE privacy profile/local policies stored in a database can also be
employed to authorize UE-related 5GC assistance information exposure.
UE privacy profile/local policies may also contain protection
policies that indicate how 5GC assistance information should be
protected (e.g., encryption, integrity protection, etc.). NWDAF via
Network Exposure Function (NEF) sends the UE-related 5GC assistance
information to AF when the local policies/UE privacy profile
authorize the AF to access the information. According to the local
policies/UE privacy profiles, NWDAF may need to protect the 5GC
assistance information with security mechanisms.
Network Functions securely expose capabilities and events to 3rd
party Application Functions (AF) via Network Exposure Function (NEF).
The interface between the NEF and the Application Function needs
integrity protection, replay protection, confidentiality protection
for communication between the NEF and Application Function, and
mutual authentication between the NEF and Application Function and
protect internal 5G Core network information. The NEF also enable
secure provision of information in the 3GPP network by authenticated
and authorized AFs.
Security should be provided to support the protection of user privacy
sensitive assistance information being exposed to AF. TLS 1.3
[RFC8446] is used to provide integrity protection, replay protection
and confidentiality protection for the interface between the NEF and
the AF [TS33.501].
5. Work Points
Security and privacy of AI/ML Networking based services and
applications need further work. [TR33.898] provides solutions to
only one of many possible key issues. Each key issue has been in
depth investigated in [TR23.700-80] and [TR23.700-82] from which new
solutions can be developed.
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We list below only some of the key issues identified:
* enhance the mobile core network to expose information to the UE to
facilitate its Application AI/ML operation (e.g., Model Training,
Splitting and inference feedback etc.)
* expose UE-related information to an AF ensuring that privacy and
security requirements are met.
* additional parameters to be provisioned to the mobile core network
by an external party for the assistance to Application AI/ML
operation.
* Whether and how the existing the mobile core network data
transfer/traffic routing mechanisms are re-used or enhanced to
support the transmission of the Application AI/ML traffic(s)
between AI/ML endpoints (i.e., UE and AF)?
* information to be provided by the mobile core network to the AF
can help the AF to select and manage the group of UEs which will
be part of FL operation.
* enhancing the architecture and related functions to support
application layer AI/ML services
* supporting federated learning at application enablement layers
* enhancing the architecture and related functions to support
management and/or configuration for split AI/ML operation, and in-
time transfer of AI/ML models. The management and configuration
aspects including discovery of required nodes for split AI/ML
operation and support of different models of AI/ML operation
splitting in which the AI/ML operation/model is split into
multiple parts according to the current task and environment.
5.1. Future Work
A use case document is needed. So far 3GPP identified many use cases
and some of which are described above in this document. New set of
use cases on Rule Based Automation, Autonomous Networks, Automated
Testing, Energy Efficiency and so on could be added to the existing
use cases. All or some of these usage areas of AI/ML can further be
elaborated in a use case document These use cases should make it
clear why the security and privacy protocols are needed.
A problem statement on AI/ML networking document is needed. Such a
document should identify the problems that possibly need a new
protocol to be developed or need to identify extensions to an
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existing protocol. One possibility in that direction could be
refining the work points identified above and formulating them in
terms of existing or to be defined in the future security and privacy
protocols.
6. Security Considerations
Security considerations of AI/ML Networking is TBD.
7. IANA Considerations
There are no IANA considerations for this document.
8. Acknowledgements
We acknowledge useful comments from Hesham ElBakoury.
9. References
9.1. Normative References
[RFC6749] Hardt, D., Ed., "The OAuth 2.0 Authorization Framework",
RFC 6749, DOI 10.17487/RFC6749, October 2012,
<https://www.rfc-editor.org/rfc/rfc6749>.
[RFC8446] Rescorla, E., "The Transport Layer Security (TLS) Protocol
Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
<https://www.rfc-editor.org/rfc/rfc8446>.
9.2. Informative References
[IsNo20] Isaksson, M. and C. Norrman, "Secure Federated Learning in
5G Mobile Networks", December 2020,
<https://www.ericsson.com/en/reports-and-papers/research-
papers/secure-federated-learning-5g>.
[Srini21] Srinivasan, A., "Difference between distributed learning
versus federated learning algorithms", November 2021,
<https://www.kdnuggets.com/2021/11/difference-distributed-
learning-federated-learning-algorithms.html>.
[TR22.874] 3rd Generation Partnership Project, "Study on traffic
characteristics and performance requirements for AI/ML
model transfer in 5GS", December 2021.
[TR23.700-80]
3rd Generation Partnership Project, "Study on 5G System
Support for AI/ML-based Services", December 2022.
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[TR23.700-82]
3rd Generation Partnership Project, "Study on application
layer support for AI/ML services", November 2023.
[TR33.898] 3rd Generation Partnership Project, "Study on security and
privacy of Artificial Intelligence/Machine Learning (AI/
ML)-based services and applications in 5G", July 2023.
[TS33.501] "Security Architecture and Procedures for 5G System",
December 2023, <https://www.3gpp.org/ftp/Specs/
archive/33_series/33.501/33501-i30.zip>.
Authors' Addresses
Behcet Sarikaya
Unaffiliated
Email: sarikaya@ieee.org
Roland Schott
Deutsche Telekom
Ida-Rhodes-Strasse 2
64295 Darmstadt
Germany
Email: Roland.Schott@telekom.de
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