Traditional machine learning approaches often need a central server, where raw datasets or
model updates are trained or aggregated in a centralized way. However, these approaches …

Federated learning (FL) algorithm has been widely studied in recent years due to its ability
for sharing data while protecting privacy. However, FL has risks, such as model inversion …

Network intrusions through jamming and spoofing attacks have become increasingly
prevalent. The ability to detect such threats at early stages is necessary for preventing a …

The shuffle model of differential privacy provides promising privacy-utility balances in
decentralized, privacy-preserving data analysis. However, the current analyses of privacy …

Federated learning is a novel distribute machine learning paradigm to support cooperative
model training among multiple participant clients, where each client keeps its private data …

Over the past few years, there has been a notable surge in the integration of Blockchain
technology into supply chain management systems. This integration holds the promise of …

The Internet of Vehicles (IoV) connects a massive amount of smart vehicles for inter/intra-
vehicle information sharing. Data privacy issues, such as privacy leakage and privacy cost …

Many mobile crowdsensing applications require efficient recruitment of workers whose
qualities are often unknown a priori. While prior research has explored multi-armed bandit …

Data aggregation protocols play a crucial role in enabling real-time monitoring of the smart
grid's operational status by the power control center. To ensure robust security, a data …

Numerical vector aggregation plays a crucial role in privacy-sensitive applications, such as
distributed gradient estimation in federated learning and statistical analysis of key-value …