Big data privacy preserving in multi-access edge computing for heterogeneous Internet of Things
With the popularity of smart devices, multi-access edge computing (MEC) has become the
mainstream of dealing with big data in heterogeneous Internet of Things (H-IoT). MEC …
mainstream of dealing with big data in heterogeneous Internet of Things (H-IoT). MEC …
Combining lyapunov optimization with actor-critic networks for privacy-aware IIoT computation offloading
G Wu, X Chen, Y Shen, Z Xu, H Zhang… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
Opportunistic computation offloading is an effective way to improve the computing
performance of Industrial Internet of Things (IIoT) devices. However, as more and more …
performance of Industrial Internet of Things (IIoT) devices. However, as more and more …
Deep PDS-learning for privacy-aware offloading in MEC-enabled IoT
The rapid uptake of Internet-of-Things (IoT) devices imposes an unprecedented pressure for
data communication and processing on the backbone network and the central cloud …
data communication and processing on the backbone network and the central cloud …
Keep your data locally: Federated-learning-based data privacy preservation in edge computing
Recently, edge computing has attracted significant interest due to its ability to extend cloud
computing utilities and services to the network edge with low response times and …
computing utilities and services to the network edge with low response times and …
From centralized management to edge collaboration: A privacy-preserving task assignment framework for mobile crowdsensing
The flexible combination of pervasive portable smart devices and omnipresent high-speed
access infrastructures has revolutionized the data sensing and knowledge acquisition in …
access infrastructures has revolutionized the data sensing and knowledge acquisition in …
Big data analytics by crowdlearning: Architecture and mechanism design
Crowdsensing has emerged as a powerful tool to collect IoT big data. Moving big data to the
cloud for analysis is time consuming and has the risk of data privacy leakage. An alternative …
cloud for analysis is time consuming and has the risk of data privacy leakage. An alternative …
Performance-enhanced federated learning with differential privacy for internet of things
X Shen, Y Liu, Z Zhang - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Federated learning (FL), which enables multiple distributed devices (clients) to
collaboratively train a global model without transmitting their private data, has attracted …
collaboratively train a global model without transmitting their private data, has attracted …
LiPSG: Lightweight privacy-preserving Q-learning-based energy management for the IoT-enabled smart grid
Z Wang, Y Liu, Z Ma, X Liu, J Ma - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
As the largest Internet-of-Things (IoT) deployment in the world, the smart grid implements
extremely reduction in the energy dissipation for the operation of the smart city. However, the …
extremely reduction in the energy dissipation for the operation of the smart city. However, the …
A privacy-preserving and reputation-based truth discovery framework in mobile crowdsensing
In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task
completion. Most of the existing studies focus on the privacy preservation of mobile users …
completion. Most of the existing studies focus on the privacy preservation of mobile users …
Verifiable, reliable, and privacy-preserving data aggregation in fog-assisted mobile crowdsensing
Fog-assisted mobile crowdsensing (FA-MCS) alleviates challenges with respect to
computation, communication, and storage from the traditional model of mobile crowdsensing …
computation, communication, and storage from the traditional model of mobile crowdsensing …