R
Rui Wang
Researcher at Zhejiang University
Publications - 241
Citations - 7157
Rui Wang is an academic researcher from Zhejiang University. The author has contributed to research in topics: Rendering (computer graphics) & Computer science. The author has an hindex of 40, co-authored 197 publications receiving 5813 citations. Previous affiliations of Rui Wang include Microsoft & University of Missouri.
Papers
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Journal ArticleDOI
Real-time KD-tree construction on graphics hardware
TL;DR: This algorithm achieves real-time performance by exploiting the GPU's streaming architecture at all stages of kd-tree construction by developing a special strategy for large nodes at upper tree levels so as to further exploit the fine-grained parallelism of GPUs.
Proceedings ArticleDOI
Side-Channel Leaks in Web Applications: A Reality Today, a Challenge Tomorrow
TL;DR: It is found that surprisingly detailed sensitive information is being leaked out from a number of high-profile, top-of-the-line web applications in healthcare, taxation, investment and web search, suggesting the scope of the problem seems industry-wide.
Proceedings ArticleDOI
Signing Me onto Your Accounts through Facebook and Google: A Traffic-Guided Security Study of Commercially Deployed Single-Sign-On Web Services
Rui Wang,Shuo Chen,XiaoFeng Wang +2 more
TL;DR: This study shows that the overall security quality of SSO deployments seems worrisome, and hopes that the SSO community conducts a study similar to the authors', but in a larger scale, to better understand to what extent SSO is insecurely deployed and how to respond to the situation.
Proceedings ArticleDOI
Learning your identity and disease from research papers: information leaks in genome wide association study
TL;DR: It is shown that individuals can actually be identified from even a relatively small set of statistics, as those routinely published in GWAS papers, and it is found that those attacks can succeed even when the precisions of the statistics are low and part of data is missing.
Proceedings ArticleDOI
3D Shape Induction from 2D Views of Multiple Objects
TL;DR: The approach called "projective generative adversarial networks" (PrGANs) trains a deep generative model of 3D shapes whose projections match the distributions of the input 2D views, which allows it to predict 3D, viewpoint, and generate novel views from an input image in a completely unsupervised manner.