Related papers: Preserving Data Privacy for ML-driven Applications…

Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from…

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high…

The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises…

In this paper, we present a spectrum monitoring framework for the detection of radar signals in spectrum sharing scenarios. The core of our framework is a deep convolutional neural network (CNN) model that enables Measurement Capable…

The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and openness of these architectures also introduce novel security…

While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications…

With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its…

Open Radio Access Networks (O-RAN) are increasingly adopting data-driven control through Deep Reinforcement Learning (DRL) to optimize complex tasks such as network slicing and mobility management. However, the deployment of DRL in…

Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing.The evolving network architecture requires the orchestration of multiple network resources such as radio…

Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. Only processed or `smashed' data can be transmitted from the clients to the server during the SL…

Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis. Presumably, the privacy of data in a deep learning…

Spectrum sharing is increasingly vital in 6G wireless communication, facilitating dynamic access to unused spectrum holes. Recently, there has been a significant shift towards employing machine learning (ML) techniques for sensing spectrum…

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband…

Privacy-preserving analytics is designed to protect valuable assets. A common service provision involves the input data from the client and the model on the analyst's side. The importance of the privacy preservation is fuelled by legal…

Advanced fifth generation (5G) and beyond (B5G) communication networks have revolutionized wireless technologies, supporting ultra-high data rates, low latency, and massive connectivity. However, they also introduce vulnerabilities,…

The evolution of 6G envisions a wide range of applications and services characterized by highly differentiated and stringent Quality of Service (QoS) requirements. Open Radio Access Network (O-RAN) technology has emerged as a transformative…

Data privacy has emerged as an important issue as data-driven deep learning has been an essential component of modern machine learning systems. For instance, there could be a potential privacy risk of machine learning systems via the model…

Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications. Overall,…

Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types…

Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information. In particular, on-device machine learning allows us to avoid sharing raw data with a third-party server during inference.…