S
Shusen Yang
Researcher at Xi'an Jiaotong University
Publications - 93
Citations - 2568
Shusen Yang is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Computer science & Differential privacy. The author has an hindex of 20, co-authored 77 publications receiving 2066 citations. Previous affiliations of Shusen Yang include University of Liverpool & Imperial College London.
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A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities
TL;DR: It becomes critically important to study how the current approaches to standardization in this area can be improved, and better understand the opportunities for the research community to contribute to the IoT field.
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$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy
TL;DR: Li et al. as discussed by the authors proposed an efficient multi-dimensional joint distribution estimation algorithm with local differential privacy, and developed a local differentially private high-dimensional data publication algorithm (LoPub ) by taking advantage of their distribution estimation techniques.
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Distributed Real-Time Anomaly Detection in Networked Industrial Sensing Systems
TL;DR: This paper introduces a fully distributed general anomaly detection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs and formally proves the scalability of the approach.
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IoT Stream Processing and Analytics in the Fog
TL;DR: The general models and architecture of fog data streaming are presented, by analyzing the common properties of several typical applications, and the design space of fog streaming is analyzed with the consideration of four essential dimensions (system, data, human, and optimization).
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Using social network theory for modeling human mobility
TL;DR: A novel human mobility model based on heterogeneous centrality and overlapping community structure in social networks is presented which satisfies the common statistical features observed from distinct real social networks and achieves a good trade-off between complexity and reality.