Related papers: Privacy-Preserving and Secure Spectrum Sharing for…
Differential Privacy (DP) considers a scenario in which an adversary has almost complete information about the entries of a database. This worst-case assumption is likely to overestimate the privacy threat faced by an individual in…
With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing of their location information. To deal with this issue,…
MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many users, it is an essential…
Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to…
Software-Defined Networking (SDN) separates the network control plane and data plane, which provides a network-wide view with centralized control (in the control plane) and programmable network configuration for data plane injected by SDN…
With the growing demand for wireless spectrum, dynamic spectrum sharing (DSS) frameworks such as the Citizens Broadband Radio Service (CBRS) have emerged as practical solutions to improve utilization while protecting incumbent users (IUs)…
Radio Access Network faces challenges from privacy and flexible wide area and local area network access. RAN is limited from providing local service directly due to centralized design of cellular network and concerns of user privacy and…
In this work, an eavesdropping-aware routing and spectrum allocation approach is proposed utilizing network coding (NC) in elastic optical networks (EONs). To provide physical layer security in EONs and secure the confidential connections…
Radio maps provide a foundational data layer for wireless digital twins, and federated learning offers a natural framework for their distributed construction without centralizing raw radio environment data. However, the exchanged client…
Retrieval-Augmented Generation (RAG) empowers LLMs with external knowledge, making cross-institutional domain-specific knowledge base integration a highly promising deployment paradigm. Despite this potential, strict privacy regulations…
Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data. Different from centralized training, the local datasets across clients in FL are non-independent…
Spectrum is a scarce commodity, and considering the spectrum scarcity faced by the wireless-based service providers led to high congestion levels. Technical inefficiencies from pooled spectrum (this is nothing but the "common carrier…
Underutilized wireless channel is a waste of spectral resource. Eavesdropping compromises data secrecy. How to overcome the two problems with one solution? In this paper, we propose a spectrum sharing model that defends against…
There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using RF sensors and fingerprinting-based methods that employ machine learning models trained on…
Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train…
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might use the model parameters to derive sensitive information about…
Differential privacy (DP) is widely employed to provide privacy protection for individuals by limiting information leakage from the aggregated data. Two well-known models of DP are the central model and the local model. The former requires…
Software-Defined Network (SDN) is a new arising terminology of network architecture with outstanding features of orchestration by decoupling the control plane and the data plane in each network element. Even though it brings several…
Smart grids are replacing conventional power grids due to rising electricity use, failing infrastructure, and reliability problems. Two-way communication, demand-side administration, and real-time pricing make smart grids (SGs) dependent on…
Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data. Due to the transmission of very high-dimensional gradient updates from many clients, FL is known…