Federated learning (FL) can protect clients' privacy from leakage in distributed machine
learning. Applying federated learning to edge computing can protect the privacy of edge …

摘要批处理, 流式计算和机器学习等分布式的大数据计算环境在云上的广泛部署与应用,
为云用户带来了极大的便利, 但随之带来的隐私数据泄露事件愈演愈烈. 如何在这种云上部署的 …

By integrating the traditional power grid with information and communication technology,
smart grid achieves dependable, efficient, and flexible grid data processing. The smart …

In this work, we propose a new algorithm ProjectiveGeometryResponse (PGR) for locally
differentially private (LDP) frequency estimation. For universe size of k and with n users, our …

From tracking pandemics to applications, such as Google Maps, Uber, environmental
monitoring, journalism, healthcare, crisis/disaster response, air quality control, noise and …

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many
benefits to users (eg, avoiding crowded areas during the COVID-19 pandemic). Although the …

Data sensing and gathering is an essential task for various information-driven services in
smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain …

Federated learning (FL) allows predictive model training on the sensed data in a wireless
Internet of Things (IoT) network evading data collection cost in terms of energy, time, and …

There exists a problem of user privacy leakage in the smart grids (SGs) that malicious
attackers may intercept or tamper with electricity data and associate the stolen data with real …

Privacy‐preserving data aggregation is an important technology for mobile crowdsensing.
Blockchain‐assisted data aggregation enables the traceability of sensing data to improve …